gamification assisted language learning for japanese...

Research ArticleGamification Assisted Language Learning forJapanese Language Using Expert Point Cloud Recognizer

Yogi Udjaja 12

1Computer Science Department School of Computer Science Bina Nusantara University Jl K H Syahdan No 9Kemanggisan Palmerah Jakarta 11480 Indonesia2Ekspanpixel Jl K H Syahdan No 37R Kemanggisan Palmerah Jakarta 11480 Indonesia

Correspondence should be addressed to Yogi Udjaja yogiudjajabinusacid

Received 22 May 2018 Accepted 28 November 2018 Published 18 December 2018

Academic Editor Michael J Katchabaw

Copyright copy 2018 Yogi Udjaja This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Starting from people limitations to understand Japanese education and social life of those people in Japan can be hinderedTherefore a game is needed that allows one to understand Japanese using Gamification Assisted Language Learning (GALL)method involving the introduction of Japanese language implementation using expert point cloud ($EP) recognizer This methodis used to stimulate the sensory and motor nervous system and motivate students (players) to study harder This can be evidencedby increase in players ability from 20 to 100

1 Introduction

Gamification emerged in the late 1960 [1] Because research-ers see the effectiveness of gamification use it has becomea major highlight since the early 2010 [2] Gamification is amethod to implement by which a boring activity is convertedinto a fun activity to attract interest and attention andmotivate and improve performance in certain activities Itis widely used in the fields of education economics socialstudies culture politics health business ecology and manymore related fields [3ndash8]

In general gamification changes personrsquos perception ofnongame activity into games [9] In this case the game canalso serve to stimulate different sensory and motor systemsfor each person [10] so as to enhance personrsquos understandingand memory [11] Then it can activate the brain cells as oneof its functions can improve the ability of person language[12 13] Human intuitive theories explain the developmentof brain cells in the game because one can explain theworld of the game analyze some examples and generatecounterfactual thinking and produce effective plans [14]

On the other hand some countries use language asone of the main conditions for continuing education and

obtaining citizenship and residence [15] In a study conductedby [16] on language education policy in China IndonesiaJapan the Philippines and Vietnam one of the countriesthat rejected internal linguistic diversity was Japan It isgoverned by themyth of Japanese identitymonoethnicity thatis largely derived from the assimilation of Japanese normsthat reinforce a monolingual as well as monoculture ideal ofthe Japanese state itself Then at the Meiji restoration in 1868began an educational revolution where foreign language wasthe focus of education in Japan

Currently according to statistics from Japan StudentService Organization (JASSO) through articles written inStudent Guide to Japan 20162017 the value of production perperson (Gross Domestic Product) is the 3rd highest in theworld and Japan ranks the 7th in the world and number 1in Asia because 24 Japanese nationals received Nobel prizesin 2016 These things make more and more people interestedin continuing education in Japan which can be seen fromthe data of 2011-2016 the number of international students isincreasing (see Figure 1) then data as of May 1 2016 explainsthat the number of international students continuing theireducation in Japan is 239287 people and that dominates fromAsia that is 222627 people (see Figure 1) [17] If viewed

HindawiInternational Journal of Computer Games TechnologyVolume 2018 Article ID 9085179 12 pageshttpsdoiorg10115520189085179

2 International Journal of Computer Games Technology

Table 1 Number of International Students Based on Nations

No CountryRegion Number of Students2016 2015 2014 2013 2012 2011

1 China 98483 94111 94399 81884 86324 875332 Vietnam 53807 38882 26439 6290 4373 40333 Nepal 19471 16250 10448 3188 2451 20164 Republic of Korea 15457 15279 15777 15304 16651 176405 Taiwan 8330 7314 6231 4719 4617 45716 Indonesia 4630 3600 3188 2410 2276 21627 Sri Lanka 3976 2312 1412 794 670 7378 Myanmar 3851 2755 1935 1193 1151 11189 Thailand 3842 3526 3250 2383 2167 239610 Malaysia 2734 2594 2475 2293 2319 241711 USA 2648 2423 2152 2083 2133 145612 Mongolia 2184 1843 1548 1138 1114 117013 Bangladesh 1979 1459 948 875 1052 132214 Philippines 1332 1028 753 507 497 49815 France 1299 1122 957 793 740 53016 Other 15264 13881 12243 42291 33313 34098

Total 239287 208379 184155 168145 161848 163697

163697 161848168145

184155

208379

239287

As of May 1 each year

150000

170000

190000

210000

230000

250000

Num

ber o

f Stu

dent

s

2012 2014 20162010Year

Figure 1 Movement of number of international students in Japanfrom 2011-2016

statistically from a survey conducted by JASSO by 2016 thenumber of Indonesian international students is ranked 6thafter Taiwan (see Table 1) [18 19]

Along with the increase of international students Japanis also the 6th most popular country with many devoteesto continue education On the other hand the increasingnumber of international students and the popularity of thecountry resulted in various problems and challenges thatmust be faced According to [20] one of the factors which is acommon problem often found against international studentsin Japan is the language skills Of the 100 internationalstudents studying in Japan 81 say that Japanese is hard tounderstand [20] Based on these results preferably beforecontinuing education to Japan prospective students mustlearn Japanese first so as to facilitate life in Japan andstudents who are interested in continuing education in

Japan are easier to be accepted in schools or universities indestination (schools or universities that have the main termsof Japanese as a language of daily learning)

In the process of learning Japanese has several elementsthat must be considered the letters (moji) vocabulary (goi)and grammar (bunpo) One of the first elements to be learnedin Japanese is to memorize letters such as hiragana katakanaand kanji because Japanese lettersmodel is different from thealphabetic language in general [21] In fact it is quite difficultto memorize Japanese because of its form and complexwriting Therefore many researchers are comparing severalmethods of learning Japanese language so that someonebetter understand the language

One of the learning models is conventional or usingtextbooks In general conventional Japanese learning makesit difficult for a person to understand the material given [22]Ref [22] said that Japanese language learning methods thatuse multimedia elements can attract motivation and encour-age someone to continue learning Japanese to understandthe use of the grammar Meanwhile the problem when usingmultimedia elements is that the learner focuses more on theeffects raised of the multimedia elements provided instead offocusing on Japanese content such as graphics animation orother multimedia elements Therefore gamification is madeusing Japanese as the most important part of the game

In its implementation this educational game uses pointcloud ($P) recognizer algorithm developed by [23] becausethe $P recognizer has fast computing and high accuracy Onthe other hand the weakness of the algorithm is that it canonly detect the result of the pattern that has been made so itcannot detect the process of making it Then the algorithm isdeveloped again into expert point cloud ($EP) recognizer forthe player who can learn to write Japanese well and truly

International Journal of Computer Games Technology 3

Table 2 Description of $-Family Recognizer Algorithm

Symbol Description119899 Number of sampled points

119879 Number of training samples per gesturetype

119877 Number of iterations required119878 Number of strokes in a multistroke

1198782119904 Number of different permutations ofstroke ordering and direction

2 Related Work

21 Game Development Live Cycle (GDLC) The main stagesin game development are design prototype production andtesting Based on research conducted by [9 24] GDLC hasseveral processes scilicet initiation preproduction produc-tion testing beta and release done iteratively to enableflexibility during the development process resulting in goodgame quality The quality can be measured from 5 criterianamely fun functional balanced internally and accessible

22 Computer-Assisted Language Learning (CALL) CALLwas born in the 1960s Fundamentals of CALL frameworkare always doing mutual relationship between developmentimplementation and evaluation so that CALL can alwaysevolve [25] Then in the evaluation several considerations areinvolved that is

(i) Can users understand what the application is doing(ii) What kind of content of the lesson that is created is

compatible with current technological interactionsFor example in terms of reading writing or listening

(iii) Howwell are the design elements with understandingof user

According to [26] in general CALL is used audiolinguallywhere the students listen to a recording and then learner isasked to retell the recorded tape by saying or typing an answerthat has been programmed by computer Then in 2016 CALLwas developed using game The game has a Role-PlayingGame Simulators gameplay (RPG Sims) where the inferencegained by the game can be used to facilitate learning Japaneselanguage as it produces fairly good learning outcomes andRPG Sims has a high potential to motivate learners too [27]

Once reviewed game still adopts the conventional wayof learning and then converts to digital Things that becomeattraction or how to motivate learners to learn can bedeveloped again by making the learning content as the maingame contentThen according to [28] computer studies needto analyze linguistic input from learners to detect errorsand provide corrective feedback and have contextual instruc-tional guidance

23 $-Family Recognizer Unistroke ($1) Recognizer is a2D gesture recognizer algorithm designed to read patternsquickly As the name implies $1 algorithm can only be usedto read a single pattern (stroke) or it can be said to have 2

permutations [23 29] Characteristics of the $1 algorithm arerotation invariant and size invariant Rotation invariant is apattern formed by slope of angle created by user if it is inaccordance with the order of formation of the same patternit will produce same reading Subsequently size invariant is apattern formed with a certain size created by the user wherereading is done by adjusting the size of the data createdresulting in same reading Here is the complexity algorithmof $1

$1 = 119874 (119899119879119877) (1)

Multistroke ($N) Recognizer is an algorithm that readsmore than one pattern (stroke) but uses a lot of memoryresulting in a slowprocess because of the permutation of eachstroke [23] Here is the complexity algorithm of $N

$119873 = 119874 (1198991198782119904119879) (2)

Because this algorithm produces a slow process the pointcloud recognizer algorithm is developed

Point Cloud ($P) Recognizer is to optimize the $Nalgorithm that reads a pattern based on a stroke made then$P is based on the relationship between the points so it doesnot require permutation or the number of patterns does notaffect the complexity level of the algorithm This algorithmproduces accuracy above 99 and the process is faster than$N [23]

Characteristics possessed by the $P algorithm are the sizeinvariant and direction invariant The size invariant of $Pequals $1 while the direction invariant is a pattern formed in adifferent order thatwill produce the same reading if pattern isformed according to existing datasets Here is the complexityalgorithm of $P

$119875 = 119874 (11989925119879) (3)

The explanation of the $-Family algorithm can be seen inTable 2

24 Game Experience Game experience is judged basedon emotion thought reaction and behavior from playersbecause it is influenced by the functionality content serviceplayer affinity and value of the player where there are someelements that become benchmarks namely user interface(UI) user experience (UX) gameplay experience (GX) andgame balancing That matter will be evaluated using gameexperience questionnaire

The benchmark adopts research undertaken by [30ndash38]specifically

(i) User Interface (UI)

(1) Usability UI can be said to be usable whenall features work properly and have informativefeedback

(2) Consistent UI can be said to be consistentwhen an event used is also used elsewhere withthe same model thereby reducing short-termmemory


More Fun

Less Fun

LowerLearning

Performance

HigherLearning

Performance

Fun but not worth learning

Not fun and not worth learning

Not fun but worth learning

Fun andworth learning

Figure 2 Gameplay Experience Evaluation for Learning Performance [30]

(ii) User Experience (UX)

(1) Useful UX can be said to be useful if it canmeetbasic needs of player or game has benefits forplayer

(2) Usable UX can be said to be usable if it can beused efficiently and easily learned

(3) Desirable UX can be said to be desirable if itcan contribute to user satisfaction by having adesign with attractive aesthetic value

(iii) Gameplay Experience (GX)

Based on research [35] there are 2 main factors that canbe formed through cognitive and affective person namelybeliefs and feelings which are derived from a combinationthat is expected by someone to an object where it becomessomething fun for player Then besides fun [30] adds areview for gamification where the content contained thereinis worthy of studying (see Figure 2)

(iv) Game Balancing

(1) Game is fair every action taken has an appro-priate impact on the game Any success andfailure experienced by player can be understoodrationally

(2) Different skill levels this is needed to determinethe satisfaction of players where there are chal-lenges that have different levels of difficulty inevery quest faced

3 Propose Method

31 Gamification Assisted Language Learning (GALL) Ingeneral the process of CALL is to change the learning oflanguage conventionally to digital When collaborated withgamifications these high potentials can be maximized sincegamification has the following elements

(i) Like games the main requirement of gamificationis to make a person feel happy and satisfied andthere are intrinsic elements of the contribution ofknowledge in it

(ii) Have goals to achieve

(iii) Limiting game with the rules that apply to achieve thegoal

(iv) Provide information about progress of achievementsthat have been made to achieve the objectives

(v) Have psychological elements to motivate players Ref[39] says there are 6 principal perspectives on moti-vation that closely relate to gamification namely traitperspective behavioral learning perspective cogni-tive perspective perspective of self-determinationperspective of interest and perspective of emotion

Because Computer Assisted Language Learning (CALL)can be developed into Gamification Assisted LanguageLearning (GALL) GALL can maximize player interest tolearn compared to CALL

32 Datasets Japanese Language Before recognizing Japa-nesewriting required datasets are used as ameasuring tool forthe assessment standards of the games to be used as learningThese datasets are created by projecting the initial process ofline formation to produce the final form of a point where thedatasets are stored in the xml format containing the position(x y) of the writing As seen in Figure 3 for example thisresearch has canvas of 5 times 5 and letter written isKuThe letteris cut in accordance with the existing coordinates and thenprocessed with the resample algorithm of $1 and $P where$1 each scratch generates 64 points with the same distancebetween points and $P each letter produces 32 points withthe same distance between points Visualization of datasetsmodelmade can be seen in Tables 3 and 4The table describesthe correct sequence of writing and the number of strokescontained in the Japanese language

33 Expert Point Cloud ($EP) Generally the $-Family Rec-ognizer has a deficiency of reading a pattern based on theresults that have been formed not based on the manufactur-ing process To learn Japanese the process of writing the letteris very important because the pattern of writing symbolizesthe balance and neatness of writing someone therefore amethod is required that can read the process of makingJapanese from beginning to end

To accomplish this the $P algorithm was developedagain into an expert point cloud ($EP) recognizer where thealgorithm used is a combination and modification of expert


X

Y

1 2 3 4 5

1

2

3

4

5

X

Y

1 2 3 4 5

1

2

3

4

5

Figure 3 Example formation of dataset coordinates

Table 3 Hiragana Letter

A I U E O

K

S

T

N

H

M

Y

R

W

N


Table 4 Katakana Letter

A I U E O

K

S

T

N

H

M

Y

R

W

N

system methods unistroke ($1) recognizer from [29] andpoint cloud ($P) recognizer from [23]

Expert systems are used to make Japanese recogni-tion systems sequentially so that writing procedure frombeginning to end can be properly written (see Tables 3 and 4)Subsequently to detect every stroke (striation) $1 algorithmis used by doing resample rotation scale and translationAfter all strokes formed into letter the $P algorithm is usedto detect writing as a whole by doing greedy cloudmatch re-sample scale and translation Examples of model illustra-tions of $1 and $P modified to form $EP can be seen inFigures 4ndash7 and explanation of the algorithm can be seen inTables 5ndash11 Subsequently overall algorithm flow can be seenin Figure 8

331 Unistroke ($1) Recognizer AlgorithmModel The follow-ing are stages of $1 algorithm

(1) Resample Based on the illustration of Figure 4 step (a)describes the player being asked to create a Japanese letterthen systematically input from the player is processed to findout whether language is true or not Letter processing startsfrom step (b) step (b) explains that the input made by playerwill change to N point in accordance with the coordinates ofwriting After that at step (c) the calculation of the distance

Table 5 Description of Formula Resample

Symbol Descriptionavg119863 Average distance119889 Distance between starting points119863 119889 plus distance to the next point119901119894 Current point position119901119894minus1 Position point to 119894 minus 1119902119909 New coordinates of x-axis119902119910 New coordinates of y-axis

is done between the points until all points passed all andthe results can be seen in step (d) Step (e) describes N-thdistance divided into 64 points Here is the formula used inthe resample stages

(a) Formula for calculating the average distance

avg119863 = 119899sum119894=1

radic(119901119894 minus 119901119894minus1)2 + (119901119894 minus 119901119894minus1)2 (4)

(b) For each point if avg119863 ge 1 then the followingequation is used

119889 = 119901119894 + 119901119894minus1 (5)


Table 6 Description of Formula Rotation

Symbol Description119888119909 Coordinates of midpoint against the x-axis119888119910 Coordinates of midpoint against the y-axis1199090 Starting point coordinates to x-axis1199091 Coordinate 1st point of x-axis119909119899 Coordinates of n-point of x-axis1199100 Starting point coordinates to y-axis1199101 Coordinate 1st point of y-axis119910119899 Coordinates of n-th point of y-axis119896 Sum of all points120579 Angle formed by 1205791199091015840 Coordinate posts after rotation of x-axis1199101015840 Coordinate posts after rotation of y-axis

(a) (b) (c) (d) (e)

Figure 4 Resample Stages

c(xy)Xc Xc X

c

(a) (b) (c)

0∘

Figure 5 Rotation Stages

Subsequently if (119863 + 119889) ge 119868 then the followingequation is used

119902119909 = 119901119894minus1119909 + (avg119863 minus 119863119889 ) times (119901119894119909 minus 119901119894minus1119909) (6)


If (119863 + 119889) le 119868 then the following equation is used119863 = 119863 + 119889 (8)

(2) Rotation In this step Figure 5 describes step (a) wherethe midpoint of the writing is processed by resampleThen in

step (b) withdrawal line is done from themidpoint to startingpoint of writing after that the angle of 120579 is determined Instep (c) a rotation is performed on the x-axis until 120579 reachesangle of 00 This matter is done so that writing can still bedetected even though writing canvas is upside down Here isthe formula used in the rotation stages

(a) Formula to determine the midpoint

119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)

(max x max y)(min x max y)

(max x min y)(min x min y)

(max x max y) (min x max y)

(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt

Figure 6 Scale Stages

(b) Formula for determining angular tilt

120579 = atan (119888119910 minus 1199090 119888119909 minus 1199100) for minus 120587 le 120579 le 120579 (11)

(c) Formula for rotation

1199091015840 = (119909119899 minus 119888119909) cos 120579 minus (119910119899 minus 119888119910) sin 120579 + 119888119909 (12)

1199101015840 = (119909119899 minus 119888119909) sin 120579 + (119910119899 minus 119888119910) cos 120579 + 119888119909 (13)

(3) Scale In this step scaling is performed to determinethe equalization of the large or small size of inputs made toexisting datasets Initially there is formation of a boundingbox by drawing line perpendicularly from the coordinatesof min (x y) and max (x y) so as to form a box that hascoordinates (min x max y) (max x max y) (minx min y)and (max x min y) (see Figure 6) Then bounding box ofplayer posts compared to datasets If bounding box playeris not the same as bounding box dataset then calculation isdone using the following formula

119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)

(4) Translation At this step is the determination of centerpoint of writing player and datasets so that center point ofboth writing models is in position (0 0) It means writing ofplayer coincides with writing of datasets Then look for alldifference distance between point of player and dataset (seeFigure 7) Here is formula used in the translation stages

(a) Formula to determine center point of writing

119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)

(b) Formula to determine average distance betweenpoints

119889119894 =sum119873119896=1radic(119868 [119896]119909 minus 119863119894 [119896]119909)2 + (119868 [119896]119910 minus 119863119894 [119896]119910)2

119873(18)

Table 7 Description of Formula Scale

Symbol Description119902119909 New coordinates of x-axis119902119910 New coordinates of y-axis119901119909 current coordinates of x-axis119901119910 current coordinates of y-axis119868119908119894119889119905ℎ length of bounding box of player input119868ℎ119890119894119892ℎ119905 Size of bounding box width of player input119863119908119894119889119905ℎ length of bounding box of dataset119863119908119894119889119905ℎ Size of bounding box width of dataset

Table 8 Description of Formula Translation

Symbol Description119902119909 Center point of x-axis119902119910 Center point of y-axis119901119909 Coordinate point input player against x-axis119901119910 Coordinate point input player against y-axis119888119909 Coordinate point datasets against x-axis119888119910 Coordinate point datasets against y-axis119889119894 Average distance119868 Input made by player119863119894 Dataset to i119896 Point to k119873 Total number of points

(5) Score Calculation accuracy of ratio is calculated from 0 to1 with the following formula

119904 = 1 minus 119889lowast119894(12) radic119868ℎ119890119894119892ℎ1199052 + 119868119908119894119889119905ℎ2 (19)

332 Point Cloud Recognizer ($P) Algorithm Model In $Palgorithm steps taken are not much different from $1 for theresample stage the scale and translation remain the same as$1 which distinguishes it is the N-distance calculation whichis divided into 32 points and calculated using greedy cloudmatch and $P algorithm does not have a rotation

Greedywhich is conducted by $P is looking forminimumdistance that is compared between input player and datasetsIn greedy usage there is distance calculation using clouddistance Cloud distance uses variable weight to determinelevel of accuracy of a comparison If a point in input player ispaired to the nearest datasets point then weight variable will


X

Y

(00)

Point Player

Point Datasets

Distance Point Player VS Datasets

(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00

Figure 7 Translation Stages

Table 9 Description of Formula Score

Symbol Description119904 Score119889lowast119894 Distance to i119868ℎ119890119894119892ℎ119905 Size of bounding box width of player input119868119908119894119889119905ℎ length of bounding box of player input

Table 10 Description of Formula Greedy Cloud Distance

Symbol Description119908 Weight119908119894 Weight to i119894 1 sdot sdot sdot 1198991199010 Coordinate starting point119899 Number of points already paired119868119894 Coordinate point input player to i119868119894119909 Coordinate point input player to i against x-axis119868119894119910 Coordinate point input player to i against y-axis119863119895 Coordinate point datasets to j119863119895119909 Coordinate point datasets to j against x-axis119863119895119910 Coordinate point datasets to j against y-axis

decrease Here is the formula used for calculation of variableweight

119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)

Table 11 Description of Formula Score Final ($EP)

Symbol Description119865 Score final119904$1 Score $1119904$11 Score $1 to 1119904$1119899 Score $1 to n119904$119875 Score $P

n Number of strokes plus overall stroke thatmake up the writing

In addition within cloud distance there is an Euclideandistance that is used for distance calculation at point cloudHere is formula used for calculating distance of point cloud

119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1

radic(119868119894119909 minus 119863119895119909)2 + (119868119894119910 minus 119863119895119910)2

(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)

333 Score Final Expert Point Cloud ($EP) Recognizer Forassessment of overall accuracy the following formula is used

If stroke = 1 then the following equation is used

119865 = 119904$1 (23)


Start

Expert System

$1 RecognizerResample ndash64 Points

Rotation

Scale

$P RecognizerGreedy Cloud Match

Resample ndash32 Points

Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No

Figure 8 Expert Point Cloud Recognizer Algorithm

If stroke gt 1 then the following equation is used

119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)

$EP algorithm is used as the standardization of writingof language performed since the input of the player will becompared with datasets that have been created where it willaffect accuracy of assessment

4 Results and Discussion

In created RPG game the battle system is lifted using turnbased and has an active time battle (ATB) where when thebattle is done the player and enemy alternately attack inaccordance with the ATB that has been determined Then toattack the player must write the Japanese language correctlyand damage is obtained by enemy in accordance with theaccuracy of the writing

In general order of Japanesewriting system can be seen inFigure 8 That figure explains the process of making datasetswith algorithm process that runs up to get the accuracy of thewriting player

Then after the game has beenmade the game experienceevaluation and pretest and posttest are given to 150 playersto see players ability improvement where there are 2 playercategories 46 players have learned Japanese and 104 playernever learned Japanese Test is done by asking player to makeall the letters contained in Tables 3 and 4 along with theromaji

On the other hand to conduct game experience evalua-tion a game experience questionnaire (GEQ) is made below

(1) Are the features in this game is running well

(a) Yes (100)(b) No (0)

(2) Whether in-game display of buttons portals and alldisplays on each screen with the same model canmake it easier to remember the function of interfaceused

(a) Yes (100)(b) No (0)


(3) Do tutorial and help feature can help you in playing

(a) Yes (100)(b) No (0)

(4) Is the system in the game easy to learn

(a) Yes (100)(b) No (0)

(5) How do you think about aesthetics are displayed inthis game

(a) Attractive (97)(b) Not attractive (3)

(6) Does game you have played help you understandJapanese

(a) Yes (100)(b) No (0)

(7) How do you think difficulty level of the game afterplayed

(a) Appropriate (100)(b) Not appropriate (0)

(8) Whether achievements and punishments providedmatches the effort you are providing


(9) Is overall learning content provided worthy to belearn

(a) Worth learning (100)(b) Not worth learning (0)

(10) How do you think a game you have played

(a) Fun (100)(b) Not fun (0)

From GEQ it can be concluded that conditions found inthe game experience have been met For aesthetic problemsit is a matter of taste of player because the developer cannotforce someone to like aesthetics of the game made

Before performing GEQ do pretest and posttest wherepretest is done before playing and posttest is done afterplaying in which case player is asked to play for one weekHere are pretest results with 150 players 10 players answeredall questions correctly 36 players answered with an averageof 30-50 wrong answers and 104 players did not answerend answer but all wrong answers

Subsequently here are posttest results from the sameperson with pretest there are 10 players who answered all theanswers correctly (same player with pretest) and 134 peopleanswered the correct average answer of 20-100 while 6players answered questions with all wrong answers

5 Conclusions

Inference of this research is as follows

(i) Japanese datasets are based on expert knowledge(ii) Currently $-Family has a new family of Expert Point

Cloud Recognizer ($EP)(iii) RPG game battle system using turn-based and ATB

plus attack system using $EP can attract players tolearn to write Japanese

(iv) A game that is made already meets rules of gameexperience

(v) Increased ability of a person is based on the capabilityof the person because everyone has different capabil-ities It can be said that the more diligent a person isto learn the more science is absorbed

(vi) A good game is a game that makes player feel happyto play it and unknowingly player can understand thescience implicit in it

(vii) Overall GALL in a matter of a week can increaseplayers ability from 20 to 100

Data Availability

The data I sent is the same as in the paper

Conflicts of Interest

The author declares that there are no conflicts of interest

References

[1] A Deif ldquoInsights on lean gamification for higher educationrdquoInternational Journal of Lean Six Sigma vol 8 no 3 pp 359ndash376 2017

[2] M Sailer J U Hense S K Mayr and H Mandl ldquoHow gami-fication motivates An experimental study of the effects of spe-cific game design elements on psychological need satisfactionrdquoComputers in Human Behavior vol 69 pp 371ndash380 2017

[3] C J Costa M Aparicio and I M S Nova ldquoGamification Soft-ware Usage Ecologyrdquo Online Journal of Science and Technologyvol 8 no 1 2018

[4] J Kasurinen andA Knutas ldquoPublication trends in gamificationA systematic mapping studyrdquo Computer Science Review vol 27pp 33ndash44 2018

[5] H Korkeila and J Hamari ldquoThe Relationship Between PlayerrsquosGaming Orientation and Avatarrsquos Capital a Study in Final Fan-tasy XIVrdquo in Proceedings of the Hawaii International Conferenceon System Sciences

[6] L E Nacke and S Deterding ldquoThe maturing of gamificationresearchrdquo Computers in Human Behavior vol 71 pp 450ndash4542017

[7] F Xu D Buhalis and J Weber ldquoSerious games and the gami-fication of tourismrdquoTourismManagement vol 60 pp 244ndash2562017

[8] L J Hilliard M H Buckingham G J Geldhof et al ldquoPerspec-tive taking and decision-making in educational game play Amixed-methods studyrdquo Applied Developmental Science vol 22no 1 pp 1ndash13 2018


[9] Yanfi Y Udjaja and A C Sari ldquoA Gamification InteractiveTyping for Primary School Visually Impaired Children inIndonesiardquo Procedia Computer Science vol 116 pp 638ndash6442017

[10] F E Gunawan A Maryanto Y Udjaja S Candra and B Soe-wito ldquoImprovement of E-learning quality by means of a recom-mendation systemrdquo in Proceedings of the 11th InternationalConference on Knowledge Information and Creativity SupportSystems KICSS 2016 Indonesia November 2016

[11] M B Armstrong andRN Landers ldquoAnEvaluation ofGamifiedTraining Using Narrative to Improve Reactions and LearningrdquoSimulation amp Gaming vol 48 no 4 pp 513ndash538 2017

[12] J S Hong D H Han Y I Kim S J Bae S M Kim and PRenshaw ldquoEnglish language education on-line game and brainconnectivityrdquo ReCALL vol 29 no 1 pp 3ndash21 2017

[13] M E D M Perez A P Guzman Duque and L C F GarcıaldquoGame-based learning Increasing the logical-mathematicalnaturalistic and linguistic learning levels of primary schoolstudentsrdquo Journal of New Approaches in Educational Researchvol 7 no 1 pp 31ndash39 2018

[14] P A Tsividis T Pouncy J L Xu J B Tenenbaum and S JGershman ldquoHuman learning inAtarirdquo inProceedings of the 2017AAAI Spring Symposium Series Science of Intelligence Com-putational Principles of Natural and Artificial Intelligence 2017

[15] E Shohamy Critical language testing Language Testing andAssessment 2017

[16] A Kirkpatrick and A J Liddicoat ldquoLanguage education policyand practice in East and Southeast Asiardquo Language Teachingvol 50 no 2 pp 155ndash188 2017

[17] JASSO ldquoStudent Guide to Japan 2017-2018rdquo 2017 httpwwwjassogojpenstudy j icsFilesafieldfile20170522sgtj 2017epdf

[18] JASSO ldquoInternational Students in Japan 2016rdquo 2017 httpwwwjassogojpenaboutstatisticsintl student icsFilesafieldfile20170329data16 brief epdf

[19] JASSO ldquoStudent Guide to Japan 2016-2017rdquo 2017 httpwwwjassogojpidstudy j icsFilesafieldfile20161130sgtj 2016id 2pdf

[20] J S Lee ldquoChallenges of international students in a Japaneseuniversity Ethnographic perspectivesrdquo Journal of InternationalStudents vol 7 no 1 pp 73ndash93 2017

[21] T Ogino K Hanafusa T Morooka A Takeuchi M Oka andY Ohtsuka ldquoPredicting the reading skill of Japanese childrenrdquoBrain amp Development vol 39 no 2 pp 112ndash121 2017

[22] M C Chan Multimedia Courseware for Learning JapaneseLanguage Level 1 [PhD thesis] UTAR 2016

[23] R-D Vatavu L Anthony and J O Wobbrock ldquoGestures aspoint clouds A $p recognizer for user interface prototypesrdquoin Proceedings of the 14th ACM International Conference onMultimodal Interaction ICMI 2012 pp 273ndash280 USA October2012

[24] R Ramadan and Y Widyani ldquoGame development life cycleguidelinesrdquo in Proceedings of the 2013 5th International Confer-ence on Advanced Computer Science and Information SystemsICACSIS 2013 pp 95ndash100 Indonesia September 2013

[25] P Hubbard ldquoFoundaton of Computer-Assisted LanguageLearningrdquo 2017 httpswebstanfordedusimefscallcourse2CALL1htm

[26] N Gunduz ldquoComputer assisted language learningrdquo Journal ofLanguage and Linguistic Studies vol 1 no 2 2005

[27] S J Franciosi ldquoAcceptability of RPG Simulators for ForeignLanguage Training in Japanese Higher Educationrdquo Simulationamp Gaming vol 47 no 1 pp 31ndash50 2016

[28] T Heift and M Schulze ldquoTutorial computer-assisted languagelearningrdquo Language Teaching vol 48 no 4 pp 471ndash490 2015

[29] J O Wobbrock A D Wilson and Y Li ldquoGestures withoutlibraries toolkits or training A $1 recognizer for user interfaceprototypesrdquo in Proceedings of the 20th Annual ACM Symposiumon User Interface Soware and Technology UIST 2007 pp 159ndash168 USA October 2007

[30] S Kim K Song B Lockee and J Burton Gamification inLearning and Education Springer International PublishingCham 2018

[31] D P Kristiadi Y Udjaja B Supangat et al ldquoThe effect of UIUX and GX on video gamesrdquo in Proceedings of the 2017 IEEEInternational Conference on Cybernetics and ComputationalIntelligence (CyberneticsCom) pp 158ndash163 Phuket November2017

[32] E Adams Fundamentals of game design Pearson Education2014

[33] E C Contreras and I I Contreras ldquoDevelopment of Commu-nication Skills through Auditory Training Software in SpecialEducationrdquo in Encyclopedia of Information Science and Technol-ogy pp 2431ndash2441 IGI Global 4th edition 2018

[34] D Lightbown Designing the user experience of game develop-ment tools CRC Press 2015

[35] MKors EDVander Spek andBA Schouten ldquoAFoundationfor the Persuasive Gameplay Experiencerdquo FDG 2015

[36] Y Udjaja ldquoEkspanpixel Bladsy Stranica Performance EfficiencyImprovement of Making Front-End Website Using ComputerAided Software Engineering Toolrdquo Procedia Computer Sciencevol 135 pp 292ndash301 2018

[37] H Joo ldquoA Study on Understanding of UI and UX andUnderstanding of Design According to User Interface ChangerdquoInternational Journal of Applied Engineering Research vol 12no 20 pp 9931ndash9935 2017

[38] Y Udjaja V S Guizot and N Chandra ldquoGamification forElementary Mathematics Learning in Indonesiardquo InternationalJournal of Electrical and Computer Engineering (IJECE) vol 8no 6 2018

[39] M Sailer J Hense H Mandl and M Klevers ldquoPsychologicalPerspectives on Motivation through Gamificationrdquo Psychologi-cal Perspectives on Motivation through Gamification vol 19 pp28ndash37 2013

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018


Active and Passive Electronic Components

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of



Journal ofEngineeringVolume 2018

SensorsJournal of



RotatingMachinery


Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation


Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom


Table 1 Number of International Students Based on Nations

No CountryRegion Number of Students2016 2015 2014 2013 2012 2011

1 China 98483 94111 94399 81884 86324 875332 Vietnam 53807 38882 26439 6290 4373 40333 Nepal 19471 16250 10448 3188 2451 20164 Republic of Korea 15457 15279 15777 15304 16651 176405 Taiwan 8330 7314 6231 4719 4617 45716 Indonesia 4630 3600 3188 2410 2276 21627 Sri Lanka 3976 2312 1412 794 670 7378 Myanmar 3851 2755 1935 1193 1151 11189 Thailand 3842 3526 3250 2383 2167 239610 Malaysia 2734 2594 2475 2293 2319 241711 USA 2648 2423 2152 2083 2133 145612 Mongolia 2184 1843 1548 1138 1114 117013 Bangladesh 1979 1459 948 875 1052 132214 Philippines 1332 1028 753 507 497 49815 France 1299 1122 957 793 740 53016 Other 15264 13881 12243 42291 33313 34098

Total 239287 208379 184155 168145 161848 163697

163697 161848168145

184155

208379

239287

As of May 1 each year

150000

170000

190000

210000

230000

250000

Num

ber o

f Stu

dent

s

2012 2014 20162010Year

Figure 1 Movement of number of international students in Japanfrom 2011-2016

statistically from a survey conducted by JASSO by 2016 thenumber of Indonesian international students is ranked 6thafter Taiwan (see Table 1) [18 19]

Along with the increase of international students Japanis also the 6th most popular country with many devoteesto continue education On the other hand the increasingnumber of international students and the popularity of thecountry resulted in various problems and challenges thatmust be faced According to [20] one of the factors which is acommon problem often found against international studentsin Japan is the language skills Of the 100 internationalstudents studying in Japan 81 say that Japanese is hard tounderstand [20] Based on these results preferably beforecontinuing education to Japan prospective students mustlearn Japanese first so as to facilitate life in Japan andstudents who are interested in continuing education in

Japan are easier to be accepted in schools or universities indestination (schools or universities that have the main termsof Japanese as a language of daily learning)

In the process of learning Japanese has several elementsthat must be considered the letters (moji) vocabulary (goi)and grammar (bunpo) One of the first elements to be learnedin Japanese is to memorize letters such as hiragana katakanaand kanji because Japanese lettersmodel is different from thealphabetic language in general [21] In fact it is quite difficultto memorize Japanese because of its form and complexwriting Therefore many researchers are comparing severalmethods of learning Japanese language so that someonebetter understand the language

One of the learning models is conventional or usingtextbooks In general conventional Japanese learning makesit difficult for a person to understand the material given [22]Ref [22] said that Japanese language learning methods thatuse multimedia elements can attract motivation and encour-age someone to continue learning Japanese to understandthe use of the grammar Meanwhile the problem when usingmultimedia elements is that the learner focuses more on theeffects raised of the multimedia elements provided instead offocusing on Japanese content such as graphics animation orother multimedia elements Therefore gamification is madeusing Japanese as the most important part of the game

In its implementation this educational game uses pointcloud ($P) recognizer algorithm developed by [23] becausethe $P recognizer has fast computing and high accuracy Onthe other hand the weakness of the algorithm is that it canonly detect the result of the pattern that has been made so itcannot detect the process of making it Then the algorithm isdeveloped again into expert point cloud ($EP) recognizer forthe player who can learn to write Japanese well and truly







2 Related Work










$1 = 119874 (119899119879119877) (1)


$119873 = 119874 (1198991198782119904119879) (2)




$119875 = 119874 (11989925119879) (3)








More Fun

Less Fun

LowerLearning

Performance

HigherLearning

Performance












(iv) Game Balancing



3 Propose Method












X

Y

1 2 3 4 5

1

2

3

4

5

X

Y

1 2 3 4 5

1

2

3

4

5



A I U E O

K

S

T

N

H

M

Y

R

W

N



A I U E O

K

S

T

N

H

M

Y

R

W

N









avg119863 = 119899sum119894=1



119889 = 119901119894 + 119901119894minus1 (5)




(a) (b) (c) (d) (e)


c(xy)Xc Xc X

c

(a) (b) (c)

0∘









119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia








2 Related Work










$1 = 119874 (119899119879119877) (1)


$119873 = 119874 (1198991198782119904119879) (2)




$119875 = 119874 (11989925119879) (3)








More Fun

Less Fun

LowerLearning

Performance

HigherLearning

Performance












(iv) Game Balancing



3 Propose Method












X

Y

1 2 3 4 5

1

2

3

4

5

X

Y

1 2 3 4 5

1

2

3

4

5



A I U E O

K

S

T

N

H

M

Y

R

W

N



A I U E O

K

S

T

N

H

M

Y

R

W

N









avg119863 = 119899sum119894=1



119889 = 119901119894 + 119901119894minus1 (5)




(a) (b) (c) (d) (e)


c(xy)Xc Xc X

c

(a) (b) (c)

0∘









119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



More Fun

Less Fun

LowerLearning

Performance

HigherLearning

Performance












(iv) Game Balancing



3 Propose Method












X

Y

1 2 3 4 5

1

2

3

4

5

X

Y

1 2 3 4 5

1

2

3

4

5



A I U E O

K

S

T

N

H

M

Y

R

W

N



A I U E O

K

S

T

N

H

M

Y

R

W

N









avg119863 = 119899sum119894=1



119889 = 119901119894 + 119901119894minus1 (5)




(a) (b) (c) (d) (e)


c(xy)Xc Xc X

c

(a) (b) (c)

0∘









119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



X

Y

1 2 3 4 5

1

2

3

4

5

X

Y

1 2 3 4 5

1

2

3

4

5



A I U E O

K

S

T

N

H

M

Y

R

W

N



A I U E O

K

S

T

N

H

M

Y

R

W

N









avg119863 = 119899sum119894=1



119889 = 119901119894 + 119901119894minus1 (5)




(a) (b) (c) (d) (e)


c(xy)Xc Xc X

c

(a) (b) (c)

0∘









119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




A I U E O

K

S

T

N

H

M

Y

R

W

N









avg119863 = 119899sum119894=1



119889 = 119901119894 + 119901119894minus1 (5)




(a) (b) (c) (d) (e)


c(xy)Xc Xc X

c

(a) (b) (c)

0∘









119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





(a) (b) (c) (d) (e)


c(xy)Xc Xc X

c

(a) (b) (c)

0∘









119888119909 = 1199090 + 1199091 + sdot sdot sdot + 119909119899119896 (9)

119888119910 = 1199100 + 1199101 + sdot sdot sdot + 119910119899119896 (10)


(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



(min x min y)




(max x min y)

=

Dwidtℎ

Dℎeigℎt

Iwidtℎ

Iℎeigℎt








119902119909 = 119901119909 ( 119868119908119894119889119905ℎ119863119908119894119889119905ℎ) (14)

119902119910 = 119901119910 ( 119868ℎ119890119894119892ℎ119905119863119908119894119889119905ℎ) (15)



119902119909 = 119901119909 minus 119888119909 (16)

119902119910 = 119901119910 minus 119888119910 (17)



119873(18)










X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



X

Y

(00)

Point Player

Point Datasets


(00((00(0(0(0(0(0(0((000)0)0)0000)00)000)00







119908 = 1 minus ((119894 minus 1199010 + 119899)mod 119899)119899 (20)





119899sum119894=1

10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 = 119899sum119894=1


(21)

sum119894

119908119894 sdot 10038171003817100381710038171003817119868119894 minus 11986311989510038171003817100381710038171003817 (22)



119865 = 119904$1 (23)


Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



Start

Expert System


Rotation

Scale



Scale

Translation

Error

Translation

Score $P

Create Data Set

Data SetYes

Final Score

Finish

Score $1

S = n

i = 1

Yes

No

n = 1

No

i = i + 1

S ge i

Yes No



119865 = 119904$11 + sdot sdot sdot + 119904$1119899 + 119904$119875119899 (24)








(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)



(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




(a) Yes (100)(b) No (0)


(a) Yes (100)(b) No (0)




(a) Yes (100)(b) No (0)








(a) Fun (100)(b) Not fun (0)




5 Conclusions









Data Availability




References











































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


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