Systems and Computers in Japan, Vol. 26, No. 11, 1995 Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J78-D-11, No. 1, January 1995, pp. 105-114

Tracking the Context in Man-Machine Communication

Toshikazu Nishimura, Nonmember, Shoichi Hirose, Michihiko Minoh and Katsuo Ikeda, Members

Faculty of Engineering, Kyoto University, Kyoto, Japan 606

SUMMARY

A method of tracking the context in man-machine communication is proposed. The "tracking the context" is defined as locating the place of context switching and dividing the user's command sequence into chunks, each of which corresponds to one work. For tracking the context, "a syntactic-context" is introduced. A syntactic-context is a working set of words in the com- mand lines a user enters. To locate the place of the syntactic-context switching, word-kind ratio and window contribution are defined. It is shown that the hit rate of command prediction is improved by using these indi- cators, which shows the effectiveness of the proposed method.

Key words: Man-machine communication; con- text tracking; context switching; syntactic context.

1. Introduction

A method of tracking the context in man-machine communication is proposed. We define "tracking the context'' as locating the place of context switching and dividing users' command sequence into chunks, each of which corresponds to one work.

One of the most important reasons why inter- human communication is much more sophisticated than man-machine communication is the utilization of context. Sharing context with each other enables participants to use ellipsis and demonstratives, and makes dialogue clear and concise, as they need not mention minor subjects in detail. Thus we direct at-

tention to the shared information in man-machine communication.

The shared information between participants is divided into two classes: one is static, the other is dynamic. Since the dynamically shared information depends strongly on the past, participants of dialogue must track it to maintain sharing.

Since the goal, the intention of the participant and the "context" may change from time to time, we regard them as the dynamically shared information. In this paper, the dynamically shared information is abbreviated "shared information."

Sharing information between man and computer will be a great help in advancing man-machine com- munication. Adaptive interface, intelligent help system, and so on, are conducted on this subject.

An adaptive interface is an interface that cooper- ates with users [l]. Though adaptive interfaces are considered to prevent the user from developing a co- herent model of the system and deprive the user of a feeling of control [2], they can provide users appropri- ate assistance to the situation. The advantages of adaptive interfaces are decreasing the mental and phys- ical workload of the user, increasing user proficiency with a new system, compensating for weakness, and so on.

An intelligent help system, a sort of an intelligent interface, is to assist users with advice suitable for their goal inferred from their input and questions. For ex- ample, Hecking's SINIX Consultant [3] is an intelligent

52 ISSN0882-1666/95/0011-0052 0 1995 Scri~ta Technica. Inc.

help system for novices. This system can answer the user's questions in English and advise the user on the redundancy of command usages by watching the user's input and inferring his or her goals. Uehara et al.'s Neo-ASSIST simulates UNIX system to trace the sta- tus of the system. It tries to infer the user's goal from ambiguous questions in natural language and deter- mine misunderstanding by tracing the process of the user's consideration.

However, the past methods of tracking shared in- formation are insufficient. For example, the con- ventional communication theory that dealt with ordinary interhuman communication was not well formalized and is hard to implement on computers.

Though these intelligent help systems are well formalized, they need the knowledge about the func- tions of commands and user's goals for all command sequences in advance. Thus it hard to implement an intelligent help system on a large and practical scale. Also, it is one of the weak points that the system cannot handle user-defined commands.

Tontext" in Reactive Keyboard [ 5 ] is well formal- ized and can handle user-defined commands. Since this "context" is defined as a sequence of the last n characters in user input (n is about S ) , it can easily be tracked on computers. However, it is not suitable for more complicated use, such as tracking user tasks or user goals.

Many of the aforementioned methods are fo- cused on the expressions of command language or vocabularies. Thus we focus on "context," the higher- level concept of the expressions, as the formalized shared information which can handle user-defined commands and is suitable for tracking user tasks and user goals.

This paper describes a method of the context in man-machine communication where a user enters com- mands through a keyboard. In this setting there is no ambiguity in users' input. Also, the response of a computer can be determined from users' input, thus we only have to pay attention to it.

Since natural language contains ellipsis, demon- strative pronouns, ambiguity, and so on, not only ex- pressions but also the situation of dialogue are in- dispensable to recognizing natural language. In the field of natural language processing based on artificial intelligence, "context" is defined as the situation of dialogue and plays an important role on recognizing

the speaker's intention [6] . The context is well formalized and many excellent results of the former works has been obtained. However, the method in this field focused mainly on ambiguity and its recognition peculiar to natural language, thus it is difficult to apply them to man-machine communication.

In many cases, a user's work is carried out by combinations of some commands. We can locate the place of work switching and can divide the command sequence into chunks, each of which corresponds to one work even if we do not know the functions of the command well. Thus we consider that computers can simulate it without the knowledge about the functions of commands and user's goals if the mechanism to locate the place of work switching is elucidated. We define ''tracking the context" as locating the place of context switching and dividing the user's command sequence into chunks, each of which corresponds to one work.

We call primitives of a dialogue, dialogue-prim- itives and focus on them for tracking the context. Some "dialogue-primitives" related to a context appear repeatedly while that context goes on. However, after the context has switched, different "dialogue-primitives" start to appear repeatedly

We have noticed the similarity between the ap- pearance of the dialogue-primitives and the memory references of programs in the field of operating systems. Denning formalized the locality of the memory references as the working set model [7]. Thus we focus on a working set of the dialogue-primitives, which appear repeatedly in a certain period of time, and call it a syntactic-context. In this paper, a word- kind ratio and a window contribution are introduced to locate the point of syntactic-context switching.

To show the effectiveness of a syntactic-context and its indicators, we introduce command prediction which is to predict which command the user will enter. The command prediction is the kind of the restoration method for the elliptical input. The syntactic-context defined in this paper is evaluated by comparing the hitting rate of a simple prediction with the hitting rate of the prediction improved by using this syntactic- context.

In section 2, we introduce two indicators, the word-kind ratio and the window contribution, to locate the point of syntactic-context switching. We evaluate the indicators in section 3. Section 4 describes con- clusions and future works.

53

History

Window

Time

Fig. 1. History.

2. Detection of Syntactic-Context

When a user repeats some sequence of com- mands, he or she is repeating some tasks for the goal. We focus on such a repetition of commands and intro- duce a syntactic-context for tracking the context.

2.1. History and syntactic-context

A history is a sequence of characters entered in a command interpreter that interprets user’s com- mands. A word is a string of characters separated by punctuation such as a space character. Let H(h) be the hth* command line which is separated by new-line character in history. The sequence of the command lines from H(h - n + 1) to H(h)** is represented as window W(n, h) where 2 I n I h. We call n the size of the window (refer to Fig. 1).

We define a syntactic-context (abbreviated as SC below) as a working set of dialogue-primitives which appear repeatedly in a certain period of time. The dialogue-primitive suitable for our subject is a word. We assume that SC satisfies the following properties.

*h is a natural number **Both h and n are natural numbers where n I

h.

Property 1. Only one SC corresponds to each command line in a history.***

Property 2. For each command line in a history, there exists a window which contains the command lines which correspond to a single SC.****

Property 3. There exists a certain command line where the SC may switch from one context to another.

When the SC in window W(n, h), and that in W(n’, h + n) are not identical, we call the first command line of the second window H(h + 1) the point of contat switching (refer to Fig. 1).

2.2. Detection of syntactic-context switching

Shimazu et al. introduced atmosphere [lo], the collection of atmospheres of words appearing most recently in input sentence in natural language pro- cessing, expressed them as Hinton’s microfeature re- presentation [ll], and managed them by pushing them into a First-In-First-Out buffer. Since frequent words change as the situation does, we can roughly grasp what is being talked about and can recognize the ex- pressions which depend on the situation by atmos- phere.

Similarly, there are frequent words and infrequent ones in each SC, and different words are used fre- quently in different SC. By utilizing this feature, this paper proposes the methods of tracking SC, i.e.,

the method of detecting SC in history and locating the place of switching; and

the method of detecting identical SCs in two windows which are isolated by another SC.

In Shimazu’s method, the atmosphere of the current situation is the set of words whose number of appearances in the FIFO buffer is more than some threshold. However, our goal is not to grasp what is being talked about but to divide the users’ command sequence into chunks, each of which corresponds to one work. Thus we focus on the change of the number of word-kind in a certain period of time.

***When SC A corresponds to a command line H(i), we call the SC of H(i) as A.

****When a window W(n, h) satisfies Property 2, we say that W(n, h) corresponds to SCA, or the SC of W(n, h) is A.

54

Window contribution 2 1

r..tiii _..............

..............~

Word-kind ratio

-Y I

.... .I I. lt . ...

.I . .. , f .....

History

SC: Syntactic-context

Fig. 2. Word-kind ratio and window contribution.

To discuss SC and its detection, we introduce the word-kind ratio which is denoted by S(n, h). We de- fine S(n, h) as a ratio of number of the word-kind to the number of words in window W(n, h). We regard a word-kind as a group of the words which are literally the same.

The number of words in a command line is not fixed. Thus the number of words in W(n, h) is not fixed either even if n is fixed. We did not adopt the mere number of the word-kind in a window as an indicator since it is not appropriate to compare the number of the word-kinds in each window.

0 < S(n, h) I 1. When S(n, h) is small, it shows that some words appear repeatedly in the window. S(n, h) = 1 means that the words in the window are different from each other.

When a window corresponds to a certain SC, the word-kind ratio is fairly small since some particular words appear repeatedly in the window. The word- kind ratio of a window which includes the point of SC switching, is larger than that of a window corre- sponding to just one SC, even if the two windows are

of the same size, since the words particular to an SC are different from those particular to another.

Thus the function S(n, h) should take the maximum value at the point of context switching in W(n, h), and should be roughly constant while W(n, h) is included in one SC whose size is larger than n (refer to Fig. 2).

This function, however, only shows whether or not a point of context switching is included in the win- dow. Thus we introduce the window contribution C(n, h) which directly shows the points of context switching. C(n, h) is defined as S(n, h)/S(n - 1, h - 1).* Since C(n, h) < 1 is equivalent to S(n, h) < S(n - 1, h - l), C(n, h) < 1 means that addingH(n) to W(n - 1, h - 1) will not increase the word-kind ratio, that is, some words in H(h) also appear in window W(n - 1, h - 1). Thus SC does not switch on H(h) and the SC of H(h) and that of W(n - 1, h - 1) are identical. On the other hand, many words in H(h) do not appear in window W(n - 1, h - 1) when C(n, h) 1 1 since C(n, h) 1 1 is equivalent to S(n, h) 1 S(n - 1, h - 1). In this case, H(h) is the point of context switching (refer to Fig. 2).

Window contribution is over 1 for some command lines after SC switches since the window contains two or more SCs.

23. Detection of identical syntactic-contexts

Consider the sequence of three windows W, = W(nl, h) , W, = W(n2, h + n2) and W, = W(n3, h + n2 + n3), corresponding to SCs A, B and C, re- spectively, where nl > n2, that is, W, is wider than W, (no limitation on n3). The history in Fig. 3 shows the relation of these windows.

When the size of window is less than n2

The word-kind ratio and the window contribution will show that both H(h + 1) and H(h + n2 + 1) are the points of context switching.

When the size of window is larger than n2

Using the window whose size is larger than n2, H(h + 1) also is detected as a point of context switching, while the detection of H(h + n2 + 1) de-

*n, h are natural numbers where 2 I n I h.

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Window sizes n2 Window size >n2 Window contribution 2 1 Window contribution 2 1

.........

Word-kind ratic S ( n , b )

4) History

v SC: Syntactic-context

a I

ord-kind ratio =

Fig. 3. Detection of the identical syntactic contexts.

pends on whether or not A and C are identical, since certain windows include W, and parts of WA and Wc also.

WhenA f C

window contribution in windows of larger size, the SC of the command line before H(g) and the SC of the command line after H(g’) are identical. The size of the latter window is not sensitive to the detection of identical SC if the size is larger than g’ - g.

H(h + n2 + 1) also is detected as a point of context switching.

3. Experiments WhenA = C

Since the words in Wc and those in WA are the same, the word-kind ratio and the window contribution will not show that H(h + n2 + 1) is the point of con- text switching.

Thus the method of detecting identical SC is as follows.

Consider a sequence with two points of context switching H(g) and H(g’), detected by the word-kind ratio and the window contribution in windows of a certain size. When H(g’) is neither detected as a point of context switching by the word-kind ratio nor the

3.1. Hierarchical model of dialogue and the eval- uation of SC

There are various levels in dialogue. In this section, we propose a hierarchical model of dialogue and show the important issues in evaluating SC.

Noguchi proposed an intelligent communication [12]. An intelligent communication is a framework for constructing computer systems that can imitate inter- human communication on man-machine communica- tion and intercomputer communication and can be realized by appending the functions of human-like intelligent information processing to computer systems

56

Method we propose:

ntactic-context:

Concept level ' sy working set of words in history _ _ ~ _ Context

Transpaa m,

Transfomi

Try$' Character ----:::_:1 Voice

Analysis j I I

I

I Language 4 1 Presentation I

I

J u,

User Computer

Fig. 4. Hierarchical model of dialogue.

and computer networks. In the intelligent communica- tion, dialogue is divided into three levels-concept level, presentation level and transport level.

Dialogue is carried out by sending information to each other and reading the right understanding of them. Dialogue is a process of expression and inter- pretation by a sender and a receiver. The process contains the following processes of actions:

1) perception; 2) conceptualization; 3) corre- spondence to language function; 4) expression; 5 ) transmission; 6) vision and hearing; 7) correspondence to language expression; 8) conceptualization; 9) under- standing; and 10) response.

The processes related to the dialogue level men- tioned before are placed from the 2nd to the 9th. The relations between each level of dialogue and each pro- cess of dialogue in user-computer communication on which we are focusing are depicted in Fig. 4.

Information is formed as "whole work" or "con- cept" in a user's mind (conceptualization on concept level). Then information on concept level is expressed by language which is defined by vocabularies and syn- tax (correspondence to language function on presenta- tion level). The expressions in the language are trans- mitted to the receiver by media suitable to the dia- logue (expression and transmission on transport level).

When the receiver is a human, the messages transmit- ted by the media are analyzed (vision and hearing on transport level) and transformed into expressions in the language (correspondence to language expression on presentation level). Thus the receiver restores the information intended by the sender and understands it (conceptualization and understanding on concept lev- el). On the other hand, since conventional computer systems do not interpret on the concept level, the user must express concepts and whole works by language.

In many cases on interhuman communication, in- formation expressed by language is not the same as the one in the mind of the sender, and is rather ambigu- ous. The receiver stores the concepts intended by the sender using the information shared between the sender and the receiver in these cases.

SC proposed in this paper is one of the methods to restore the information intended by the sender on the concept level rather than on the presentation level where the conventionalcomputer interpretscommands. Word-kind ratio and window contribution show the switching of SC, i.e., word-kind ratio and window con- tribution interpret the expressions on the presentation level and track SC.

By showing the relationbetween the shared infor- mation "context" and our two indicators for SC, the points of issue of the evaluation of SC are listed as

57

Word-kind ratio

I I I I I I I I I I I I

follows:

I I I I I I I I I I I I I I I

. 8

Word-kind ratio

. 6 .4 .Z Window Window contribution History contribution 0

I I I I I I I I I I I I I I I I I I I I I I I I

I

TI

Window size = 7

1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 1 6 17 1 8 19 20 21 22 23 24 25 26 27 28

le

t Time

Window size=l2

Fig. 5. Example 1 (locating the place of the syntactic-context switching).

Do our indicators realty track the switching of SC as defined?

Is SC really the information shared between the user and the computer?

We will answer the first question by showing that the arrow from the presentation level to the concept at the computer in Fig. 4 points the proper place i.e., that the word-kind ratio and window contribution locate the point of SC switching properly. We will discuss it in section 3.2.

Instead of showing the second question directly, we will show that the hitting rate of command predic- tion is improved by using these indicators. We will discuss it in section 3.3. We can answer the issue with assertion that man-machine communication is im- proved greatly by using shared information.

3.2. Context and syntactic-context

In this section, we present some examples of his- tories collected from UNLX system and the graphs of the word-kind ratio and the window contribution in order to show that they can track SC. The size of the window in these examples is fixed to 7 and 12.

Example 1 in Fig. 5 shows that the word-kind ratio and the window contribution can locate the place of the SC switching. The user was making an image processing program using a text editor in another win- dow on a multiwindow environment. The user re- peated debugging the program through trial and error from the 1st line to the 15th line (lightly hatched in the figure), watched the output of the program from the 16th line to the 22nd line, and then consulted reference manual pages for obscure passages from the 23rd line (thick hatched in the figure). In this example, each work corresponds to each SC, and the 16th line and the 23rd line, the lines where new work began, are the points of context switching.

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Word-kind ratio Word-kind ratio

I l l I l l I l l I l l I l l

. e . 6 .4 . 2 o zgtion ,

I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I

History contribution 0 .2 . 4 . 6 Window

b I I I I 1- I

I :- I 5 6 I 8 9

10 11 12 13 14 15 16 11 18 19 20 21 22 23 24 25 26 21 28 29 30 31 32 I

ne lime

I I I I I I I I 1 I )I I

Window size = 7 Window size=12

Fig. 6. Example 2 (detection of the identical syntactic-contexts).

When the size of the window is 7, the word-kind ratio is maximum near the 20th line and the 26th line, which indicates that the points of SC switching are in windows W(7,20) and W(7,26). The window contribu- tion is over 1 from the 16th line to the 18th line and on the 23rd line and the 24th line.

Though it is rather obscure, the word-kind ratio is also maximum near the 10th line and the 25th line when the size of the window is 12. The window con- tribution is over 1 on the same lines. This means that the indicators can locate the place of SC switching.

Example 2 in Fig. 6 shows that the word-kind ratio and the window contribution can detect the identical SC in two sequences of commands which are isolated by another SC. The user was making an image format converter using a text editor in another window on the multiwindow environment. The user described the dependencies of the source files of the program in Makefile from the 1st line to the 5th line, and repeat-

I I I I I I I I I I 1 I I I I I I I I I I I I I I I I I I I I I I

ed debugging the program "a" through trial and error from the 6th line to the 18th line (hatched in the figure). Then the user resumed debugging it from the 26th line (hatched in the figure) after examiningthe bit patterns of the output of the program from the 19th line to the 25th line. The works from the 19th to the 25th differ from one another, and the points of context switching are on the 19th line and on the 26th line.

When the size of window is 7, the window con- tribution is over 1 on the 19th, the 20th line and the 26th, the 27th line, which locates the site of SC switching. On the other hand, the window contribution is over 1 only on the 19th, the 20th line and does not locate the site of SC switching on the 26th, the 27th line. Consequently, we can tell that the work before the 18th line and one after the 26th line is the same.

As Denning's working set model [7] depends on the locality of the memory references, the ability of these indicators for tracking SC in other history than

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Word-kind ratio Window History contritmtiod

Window 1.0 - 8 - 6 - 4 - 2 0 contrit,,,(ion

I I I I I I l l 1 I I I I I I I I I I I I I I I I

I l l 1 I l l 1

I I I I I I I I I I I I I I I I I I I I I I I I

T ie T

Word-kind ratio

. 2 .I .6 .8 1.0 - I I I I I I I I I I I I I I I I I I I I I I I I

I l l 1 I l l 1 I l l 1 I I I I I l l 1 I l l 1 I I I I I I I I I I I I I I I I

l [ l I I I I I I I I I I

I l l 1 I 1 I I 1 I I I I I

Window size = 7 Window size= 12

Fig. 7. Example 3 (a failed example).

these examples depends on the locality of the words in history. When SC does not satisfy the properties in section 2.1, it is hard to track SC properly since the locality of the words is weak.

Figure 7 is an example in which various kinds of works are interleavedwith each other. In this history, the user described the dependenciesof the source files, repeated debugging the program "a" through trial and error and examined the bit patterns of the output of the program at random. With any size of window, the word-kind ratio do not present maximum clearly and the window contribution is over 1 continuously. The indicators will not locate the SC switching in such a case.

Example in Fig. 7 was logged under the special circumstance; the user was extremely fatigued with making the program in the middle of the night (actu- ally, he terminated the session to go home after he entered the last command). This is an extreme exam- ple through all the history on which the experiment described in section 3.3 is conducted. Though we do not prepare statistics of the occurrence of such a case, we merely observed them in our experience.

33. Command prediction

Command prediction is to predict which com- mand the user will enter. The command prediction is the extreme elliptical input method. In this section, the improvement of Command prediction is described to show that tracking the context will adopt elliptical input and be a great help to advance man-machine communication.

Greenberget al. analyzed the command history in UNIX system and studied the history mechanism for supporting repetition of the user's command input [ 141. They show that many of command inputs are repetitions of the past, and they proposed a method of supporting repetition by storing user's input into First- In-Out buffer. This means command prediction using history is possible.

In this paper, we engage the method of command prediction by storing the sequences of two commands in history into Least-Recently-Used buffer and search the buffer for the command which is a successor of the last entered command. We conducted an experiment to compare the hitting ratio of two command predic-

Table 1. The hitting rates of the command prediction with SC

Number Hitting rates Hitting rates of lines without SC(%) with SC (%)

2 74 526 145 312 205 155 124 121 112 153 137

1171 269 620

32.12 32.51 27.59 30.45 19.51 39.35 33.87 32.23 33.04 34.64 32.12 39.54 22.30 27.10

33.94 32.66 30.34 31.73 21.95 40.65 36.29 38.12 36.66 37.25 33.58 40.22 25.28 28.23

average 31.17 33.35

SC: a syntactic-context The median values are underlined

tions, one is the method described here, the other is the method using SC to limit the candidates to the command whose SC is identicalwith the one of the last command.

We collected histories from the 18 UNIX users in our laboratory, the total number of command lines of which is about 400,000. We selected randomly the histories in one session having more than 100 lines to apply the methods of command prediction. Since the methods cannot give any candidate when the history is too short, the selection of sessions is needed not to make the evaluation of SC obscure.

Refer to Table 1 for the hitting rates. The hitting rates of the command prediction with SC is higher than that of the command prediction without SC. This re- sult shows that ellipsis in the user’s input can be re- stored with the points of SC switching located by word- kind ratio and window contribution, and SC is effective in advancing man-machine communication.

4. Conclusion and Future Works

A method of tracking the context in a man-ma- chine communication to advance it has been proposed.

First, we defined a syntactic-context as a working set of words in command lines that a user enters, and introduced two indicators, the word-kind ratio and the window contribution focusing on the locality of words. We showed that the indicators can locate the point of context switching and identify the identical SCs. Se- cond, we showed that SC can be tracked with the indi- cators by examples. Finally, we showed that the point of SC switch located by the word-kind ratio and the window contribution is effective in advancing man- machine communication by comparing the first candi- dates of two prediction methods.

Applying this method to nonlanguage actionssuch as pointing object by mouse, and so on, is left for the future work.

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AUTHORS (from left to right)

Toshikazu Nishimura received his B.E. and M.E. degrees from Kyoto University in 1990 and 1992, respectively. He is currently an Instructor in the Division of Information Science, Kyoto University. He is a member of IPS.

Shouichi Hmse received his B.E., M.E. and Dr. of Eng. degrees from Kyoto University, Kyoto, Japan in 1988, 1990 and 1995, respectively. He is an Instructor in the Division of Information Science, Kyoto University. His current interests include cryptography, computer security, computational complexity and Boolean functions.

hiichihiko Minoh received his B.E., M.E. and Dr. of Eng. degrees from Kyoto University, in 1978,1980 and 1983, respectively. He has engaged in research on image processing and artificial intelligence. He is currently an Associate Professor at Kyoto University. He is a member of IPSI; IEEE; and ACM.

Katsuo Ikeda received his B.E., M.E. and Dr. of Eng. degrees from Kyoto University in 1960, 1962 and 1978, respectively. He is currently a Professor in the Department of Information Science, Kyoto University. His primary research interests include construction of intelligent information media environment. He is the author of Structure of a Computer Utility (in Japanese; Shokodo, 1993); and the translator of System Programming (J. J. Donovan, 1974); and Operating System (J. J. Donovan, 1976). He is a member of the IPSI; IEEE; ACM; and the editorial board of Informa- tion Processing Letters, Elsevier.

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