Shunpeng Zou

Xiaohui Zou
email: zouxiaohui@pku.org.cn

Understanding:How to Resolve Ambiguity

Keywords: Linguistic cognition, Mind philosophy, Brain-Machine Integration, Attribute Theory Method

This article aims to explore the question: understanding, interpreting, translating, how to resolve ambiguity? Or: how does man-machine combination resolve ambiguity? In order to focus on the essence of the problem, the method is that: target analysis butterfly model and its use cases, macroscopic analysis ambiguity model and its use cases, microscopic analysis matrix model or search model within a series of bi-list and its use cases. The result is through the three examples, from manual translation to machine translation and translation memory on view, pointed out that the fundamental way to resolve ambiguity. Its significance is that the method can be advanced to the generalized translation and corresponding interpretation and final practical understanding, the specific performance is that through man-machine collaboration, and its verifiable results with this method, we can work to resolve various ambiguities better, to ensure accurate understand, prevent and eliminate all kinds of misunderstandings.

Introduction

This article aims to explore the question: understanding, interpreting, translating, how to resolve ambiguity? Or: how does human-machine better resolve ambiguity?

Nature language contains a variety of ambiguities. It is difficult to bypass problem of understanding, interpretation and translation. That is a great challenge for human and computer. We know that"ambiguity in language is an essential part of language, it is often an obstacle to be ignored or a problem to be solved for people to understand each other". [START_REF] Quiroga-Clare | Language Ambiguity: A Curse and a Blessing[END_REF] James Allen introduces the concepts required to build a NL system without losing you in the psycholinguistics, psychology and philosophy of language. [2] "Interpretation is a complex practice that requires the interpreter to fully understand, analyze, and process spoken or signed messages. The interpreter, after processing this information, renders the message into another language." [START_REF]Understanding Interpretation and Translation[END_REF] "The Language of food: a linguist reads the menu". [START_REF] Jurafsky | The Language of Food: A Linguist Reads the Menu[END_REF] Not only the difficulty of natural language understanding is ambiguity everywhere, but also generalized language or generalized text (including size character, formula, graphic, table, sound, image, 3D, living) passing information, imparting experience, expressing knowledge (expert knowledge acquisition and its formal representation and even repetitive reuse) is bound to be due to the various ambiguities (including disparity and disagreement) for both human and machine and their combination to make judgments or decisions, such problems are a major challenge to both human intelligence and artificial intelligence."We saw earlier how to use ctrl + = to enter natural language input. Now we're going to talk about how to set up functions that understand natural language. Interpreter is the key to much of this. You tell Interpreter what type of thing you want to get, and it will take any string you provide, and try to interpret it that way." [5] "While most of the neural network techniques are easy to apply, sometimes as almost drop-in replacements of the old linear classifiers, there is in many cases a strong barrier of entry." [START_REF] Goldberg | A Primer on Neural Network Models for Natural Language Processing[END_REF] "provides a more coherent and integrated framework for performing both bottom-up and top-down knowledge acquisition." [START_REF] Motta | A Methodology and Tool for Knowledge Acquisition. 8. Brad Allenby. Why it's a mistake to compare A.I. with human intelligence? 9[END_REF] "Turing himself provides a hint of the answer, noting that his deceptively simple question requires a robust definition of "think"; without one, the question itself is meaningless.""Indeed, Turing's original question""Brilliant as the Turing test is, its popularity has had one pernicious effect: It has reinforced for many people the comfortable illusion that human intelligence is a meaningful measure of intelligence in general. This is understandable: If you can't define think or intelligence, refer back to human intelligence as the gold standard." [8] "Artificial intelligence meets human intelligence". "Why did it take so long for neural networks to recognize speech and objects in images at human levels? [9] "Linguistically, Harmony maximization corresponds to minimization of markedness or structural illformedness." [START_REF] Smolensky | Harmony in Linguistic Cognition[END_REF]

Method

In order to focus on the essence of the problem, the following examples, from manual translation to machine translation and translation memory of practical point of view, and then advance to general translation and its corresponding interpretation and the final understanding of the basic point of ambiguity resolution (involving targeting analytic butterfly model, macro analytic triangular model and microscopic analytic enumeration matrix model or search model within a series of bi-list query).

Butterfly model and its use cases

From the "pyramid model"(figure 1a) through the reverse thinking and model reconstruction -that is, the construction of "butterfly model" (figure 1b,it is for us to "understand, explain, translate, how to resolve ambiguity?" What is the role, value and significance of these two models? ). We find a paradox existing in the translation pyramid model(figure 1a). [START_REF] Xiaohui | paradox existing in the translation pyramid model[END_REF] From artificial and machine translation "pyramid model"(figure 1a) to bilingual "butterfly model"(figure 1b), there are differences and links between the two basic assumptions involved in the conversion principle. The former is the father of machine translation Weaver (interlanguage hypothesis) and computational linguistics of the international master B. Vauquois (translation pyramid) combination, the latter is the younger generation Zou Xiaohui's unintentional discovery between the two sides both in the actual process of natural language understanding and in establishment of bilingual coprocessing system ManMachine interaction. To this end, published a corresponding articles on the model was introduced, the focus of which also led to the academic director Professor Feng Zhiwei's concern and the occurrence of the corresponding academic debate and exchange of ideas, and now, not only this academic idea exchange has been effectively promoted, but also the (the narrow sense, the generalized, the alternative) three kinds of bilingual co -processing system, and further discovered its implied the three basic laws (principles or rules) in logic, mathematics and linguistics. [START_REF] Xiaohui | From translation pyramid model to bilingual butterfly model[END_REF] Figure 1a You can see that from artificial and machine translation "pyramid model"(figure 1a) to bilingual "butterfly model"(figure 1b), there are differences and links between the two basic assumptions involved in the conversion principle.

Targeted analysis of butterfly model is characterized by first explained each kind of language both in bilingual model internally, and then translated from each other.

The specific performance of wisdom is for value-taken and confidence-building. Through using basic methods of value-taking and confidence-building of language by the author's fundamental research in general linguistics taking Chinese language as an example, particularly in exploring how to solve the sharp conflict between two schools of scholars among workers, who adhere to Chinese character based linguist theory and word based linguist theory respectively. Specifically, the purpose of this paper is to solve the conflict between both Chinese character based view and word based view by using the basic unit of Chinese theory. [START_REF] Xiaohui | Value-Taking and Confidence-Building of Language[END_REF] Identification, distinguishing and selection of lexical ambiguity are achieved by the selection of term or value-taken. Structural ambiguity distinguishes the function of the phrase and the sentence in their context. English morphemes and Chinese characters and their internal radicals, English words and Chinese words, English phrases and sentences and Chinese corresponding structural units, are almost all difficult to recommend a corresponding function of the relationship. Its roots lies in characters, not only the existence of the word information (its internal radicals), and the existence of inter-word information and even outside the word information. These three aspects of the value-taken and confidence-building on the two aspects, although the system can be used to do a series of processing, but a series of values, a series of confidence, how these two aspects of specific match? Human, computer, bi-brain, between the three, their operation and algorithm and the combination of the model there are differences. So, natural language understanding and how does resolve ambiguity? Be sure to follow the basic program given by the Butterfly Model, ie, (in the respective native language system, respectively) to explain them, (and then in both the phrase and the sentence) to translate them. Thus, understanding, interpreting, translating, three intellectual activities, incompetence is mental intelligence activity, or artificial intelligence activities, and even man-machine bi-brain wisdom, can be divided into two basic activities: the value-taken (select each match) and the confidence-building (sure that the common choice of the selected item is matched).

The following is illustrated and demonstrated in connection with the embodiments: In Chinese, the relationship between characters and their strings as partial word is understood as formal structure between the object language and the explanatory or interpretation language or meta -language. At the same time, in English, the formal structure is understood by the relationship of words, phrases and sentences.

Figure 1b covers: lexical ambiguity (1) and structural ambiguity (2)which divided into two levels: phrase (ZiZu1) and sentence (ZiZu2). As a result "first explanation" and "re-translation" of the principles, therefore, the above two types of ambiguity and confidence-building process, resolved in the mother tongue groups. In other words, the translation is based on the bilingual expression of knowledge ontology. That is, unambiguous text conversion between source language and target language (which means that bilingual formal expressions refer to a specific object or concept).

For example, the performances of physics, meaning, grammar in English are three words, but three phrases (ZiZu1) in Chinese. Therefore, from the level of Chinese characters to the level of English words, there is no way one by one correspondence. In other words, English words and Chinese words are not entirely at one level.

English and Chinese phrases (ZiZu1) and sentences (ZiZu2) these two levels can be in the same level. [14] How does this ambiguity resolve? We use butterfly model to try the list explained as follows:Table 1 with large string (of character) formula to interpret Chinese characters and their relationship between characters, thus, resolve ambiguity. The equation of string(character)formula 1.

x

+ y = z (1)

Triangular pyramid (tetrahedron) model and its use cases

From the "semantic triangle" and "physics, meaning, grammar" (that covering "grammar, semantics, pragmatics") through the combination of Chinese and Western create three-dimensional Triangular pyramid (tetrahedron) model (the most ambitious knowledge system frame and the most fundamental knowledge classification).

What is the use, value, and significance of that ? Understanding, explaining, translating:How better to resolve ambiguity by using the models?

Thing(1),Mind(2),Word(3) 0(Tao),1(Law),2(Principle),3 (Rule)

Figure 2a

Figure 2b Figure 2 Triangular Pyramid (Tetrahedron in Figure 2a) Model is Macro model. It is a broad text(文), Tao is basic true information (covering all phenomenon information).

Combining with Table 1, you can understand the four fundamental categories and the six basic concepts both in Figure 2a and Figure 2b. The four fundamental categories:physical world (real object),meaning thought (subject content), grammar language(virtual form),Tao(laws or principles or rules);the six basic concepts:object and subject,content and form,virtual and real.

First of all, the three composite concept of decomposition, in the Triangle model will be a character set of Chinese visualization of the intuitive advantages, specifically expressed as three sets of character string formula (See: Table 2).

In the fundamental categories (See: Figure 2 and Table 3), introduction of three pairs of concepts: object and subject, content and form, virtual and real, highlight the scope of the phenomenon (world/thing, thought/mind, language/word).

Chinese category Tao is fourth fundamental category (See: Figure 2) ,the basic concept different from these Western concepts "physics, meaning, grammar" and the two sides behind the outlook. The theory of "holism" and "reduction theory" are not clear (the biggest ambiguity problem is highlighted). Therefore, the categories of thing, mind, word and the essence (law, principle and rule) can be further Explored.

The fundamental category contains more basic categories, which is composed of two groups of three pairs of concepts, they are: object and subject, content and form,virtual and real. The following is illustrated in connection with the embodiments: Table 2. Resolve ambiguity by using the model in Figure 2a and Figure 2b. Word(词);Character(字);Yan (言)

Figure 3 Query Model (Three kinds of views in linguistics and three bilingual division methods for that from translation to query, laid the basis for both theoretical analysis and practical operation.) [START_REF] 邹晓辉 | 言和语的关系--在语言和言语的区分基础上进一步[END_REF] As it can be seen from Figure 3a, from English word and Chinese character, we extract bilingual Yan and Yu, for all kinds of object language and mete-language that is, out of semantic language, laid a solid foundation for formal understanding. [START_REF] Zou | FUNDAMENTAL LAW OF INFORMATION: PROVED BY DOUBLE MATRICES ON NUMBERS AND CHARACTERS[END_REF] It can be seen from Figure 3b that the relational data structure within generalized bilingual object language and mete-language, man-machine cooperation to complete both Chinese information processing (find the best algorithm) and natural language understanding (to resolve ambiguity), laid solid basis by using two categories formal strategy. It is a actually global(language)positioning system(GLPS).

Three types of bilingual co-processing with multiple interfaces and can reflect a series of bilingual conversion, not only to answer "understanding, interpretation, translation, how to resolve ambiguity?" for us,but also the problem has a practical effect, value and significance, moreover, it can also be seen as a test "how to better resolve ambiguity with man-machine combination?" Bi-brain and bi-intelligence collaboration system is intuitive prototype. [16][17] From "monolingual model" and "multilingual model", we abstract "bilingual model" (especially the "three kinds of bilingual co-processing" and its implied logical sequence and position, function linkage, translation promotion or explanation expansion. Resolve ambiguity and understand language.

Human-Machine Twin System as Collaboration Translation Based on the three basic laws of human-computer cooperation (three basic rules of logical sequence-position, function linkage, translation promotion) and constructed a series of bi-list, using computer-aided, not only can achieve the joint operation between arithmetic and language, but also to the joint operation combined fully automatic, semiautomatic, big collaboration, by the narrow language and its plain text of the string, through the promotion of characters such as the intermediary model, to further promote to generalized language or text (including characters, formulas, graphics, tables, sounds, images, 3D, living).

The following is illustrated and demonstrated in connection with the embodiments: It can be seen in Figure 4a that smart (city), mental intelligence and artificial intelligence (different from ancient Greek wisdom in philosophy), converge on the connotation and extension of research object of intelligent science.

As it can be seen in Figure 4b, Chinese as a typical natural language, we understand its own and its unambiguous formal expression, such as combination in Table 3, we can do further research and discussion. Later, in conjunction with Figure 5a, we can also classify it.

Sequence and location of syllables, select: value-taken and confidence-building.

Smart Mental Intelligence

Artificial Intelligence

The so-called value-taken, here is that: around a specific goal, given a series of Chinese characters with appropriate structures. For example, in the following table 3, two groups have been accurately displayed, and their features are clearly displayed. The equation of string(character)formula 2.

Yan (Zi, Sentence) + Yu (ZiZu1, Phrase; ZiZu2, Sentence) = Word or Sentence (2)

Figure 5b It can be seen in Figure 5a that the linguistic view introduced here is essentially a broad bilingual view. Between formal language shown in Figure 5a and formal system developed in Figure 5b by the part of reduction theory, its example highly consistent. In other words, the formal understanding natural language here with the arithmetic language (including: machine language) formal understanding, in the same way. The type calculation of the hierarchical set is combined with the attribute set, and the bridge between the simplest single set and the most complex cluster is built up. Thus, language theory, epistemology, object theory, can be unified into the overall logic of sequence and location in twin matrix. Pure formal various functions and a variety of property functions, between each other can be in the agreed under a variety of specific constraints, the formation of the corresponding linkage function. This is the general translation, paved the way. Furthermore, not only for various forms of class functions, but also for various attribute functions (involving the content) can be done: both can be calculated, but also can statistic and can do similar measures. [START_REF] Zou | Characteristics of Information and Its Scientific Research[END_REF]

Conclusion

If Global Positioning System(GPS) can help people to accurately determine the starting point and the target point and the optimal path between each other, then the language, knowledge concept, software object Global Positioning System also can help people clearly understand (interpret, translate or convert) them. In this study, the macroscopic triangular pyramid (tetrahedron) model and the microscopic table model are combined to form Global (Language, Knowledge, Software) Positioning System, and the target (language, knowledge, software) positioning effect. For example, the (eight) Yan and Yu, (three)Zhi (from wisdom to smart, mental intelligence and artificial intelligence), all can be particularly understood. Another example is the further understanding of physics, meaning and grammar (divided into three groups). With this basis, the other types of subdivision and rough (language, knowledge, software) target point of sequencing positioning can be accurate and correct. If both Figure 4a and Figure 2 are to look at the problem from a macro perspective, then Figure 4b and Figure 1b all look at the problem in the middle of the view, while Figure 3b looks at the problem in a microscopic point of view. So that the combination of macro and micro to do natural language understanding and formalized the expression of knowledge, can be accurate, of course, naturally can be resolved which contains a variety of ambiguities.

Result

The result is through the three groups of examples, from manual translation to machine translation and translation memory on practical view, pointed out that the fundamental way to resolve ambiguity.

The three models and their used cases tell us that each item of character or word as value-taken can be recorded, not only by computer-assisted enumeration into an electronic dictionary, but also by an enumerable corpus at all levels, in particularly through a large number of bilingual expressive knowledge bases that are precisely processed and validated for bilingual linkage. This has laid a large data environment for repeated application within the three models. Their function can be used by the majority of teachers and students to the extreme. Thus creating a basic development platform can be built for knowledge production.

Significance

Its significance is that the method can be advanced to the generalized translation and corresponding interpretation and the final practical understanding, the specific performance is that we can work to resolve various ambiguities, to ensure accurate understand, prevent and eliminate all kinds of misunderstandings,through humancomputer collaboration with this method and its specific verifiable results.

The practical significance of this paper is not only that it demonstrates how a series of ambiguities between Zi(字) and Zhi(智) are gradually resolved by the three models of macro and micro (and its practical application methods and tools), but also in the process of argumentation. The basic views and basic approaches adopted have universal roles, values and meanings. For the author's innovative ideas and innovative methods of the system argument, has been beyond the scope of this article, interested in it, can refer to the author's related works. Do they all together can be classified as Tao trying to characterize the object form and the subject content or idea? (To be further explored)

Figure 1b

  Figure 1b

Figure 1

 1 Figure 1 Butterfly Model (pyramid model to remove the spire after the reverse reconstruction of the reverse thinking results)You can see that from artificial and machine translation "pyramid model"(figure1a) to bilingual "butterfly model"(figure1b), there are differences and links between the two basic assumptions involved in the conversion principle.Targeted analysis of butterfly model is characterized by first explained each kind of language both in bilingual model internally, and then translated from each other.The specific performance of wisdom is for value-taken and confidence-building. Through using basic methods of value-taking and confidence-building of language by the author's fundamental research in general linguistics taking Chinese language as an example, particularly in exploring how to solve the sharp conflict between two schools of scholars among workers, who adhere to Chinese character based linguist theory and word based linguist theory respectively. Specifically, the purpose of this paper is to solve the conflict between both Chinese character based view and word based view by using the basic unit of Chinese theory.[START_REF] Xiaohui | Value-Taking and Confidence-Building of Language[END_REF]

String

  Figure 3b

  Figure 4aFigure4b

  字(Object Language) Yan (Grammatical) Yu语(链) (释)字(Meta-Language) Yan (Semantic +Grammatical) Yu语(块) (实)字(Semantic) Yan (虚)字(Grammatical) Yan (用)字(Pragmatic) Yan (解)字(Lexical) Yan (Pragmatic +Classical) Yu语(读) (Pragmatic +Vernacular) Yu语(句) (Pragmatic +Rhetoric) Yu语(段) (Pragmatic +Article) Yu语(篇)

Table 1 .

 1 Resolve ambiguity with string of character formula.

Table 3 .

 3 Characters=Yan, Chinese=Yu, word and sentence have attributes of both Yan and Yu.


Xiaohui Zhu
email: xiaohui.zhu@xjtlu.edu.cn

Yong Yue
email: yong.yue@xjtlu.edu.cn

Yixin Zhang
email: yixin.zhang@xjtlu.edu.cn

Prudence W H Wong
email: pwong@liverpool.ac.uk

Jianhong Tan
email: jianhong_tan@qq.com

Designing an Optimal Water Quality Monitoring Network

Keywords: Optimal water quality monitoring network, multi-objective optimization, SWMM, closeness centrality

The optimal design of water quality monitoring network can improve the monitoring performance. In addition, it can reduce the redundant monitoring locations and save the investment and costs for building and operating the monitoring system. This paper modifies the original Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the design of water quality monitoring network based on three optimization objectives: minimum pollution detection time, maximum pollution detection probability and maximum centrality of monitoring locations. We develop a new initialization procedure as well as a discrete velocity and position updating function to optimize the design of water quality monitoring network. The Storm Water Management Model (SWMM) is used to model a hypothetical river network which was studied in the literature for comparative analysis of our work. We simulate pollution events in SWMM to obtain all the pollution detection time for all the potential monitoring locations. Experimental results show that the modified MOPSO can obtain steady Pareto frontiers and better optimal deployment solutions than genetic algorithm (GA).

Introduction

River systems play a crucial role in the sustainable development of a community. However, industry and living activities are creating more and more pollutants to freshwater sources. It is estimated that 40 billion dollars are lost each year in China due to freshwater pollution events [START_REF] Yang | How to Look at the GDP Receiving Much Attention[END_REF]. Water quality monitoring has become one of the routine efforts for environmental protection all over the world. However, monitoring water quality remains a very complex process due to the large number of factors to consider such as monitoring locations, selection of water quality parameters, monitoring frequency and identification of monitoring objectives [START_REF] Park | Design of a water quality monitoring network in a large river system using the genetic algorithm[END_REF]. The costs of building and operating an automatic monitoring station are also very high (about 500,000-600,000 dollars per station for construction and 14,000 dollars per year for operating and maintaining). Planning and optimizing water quality monitoring networks have been addressed since 1940s. Dozens of papers have been published on this subject [START_REF] Park | Design of a water quality monitoring network in a large river system using the genetic algorithm[END_REF][START_REF] Chilundo | Design of a water quality monitoring network for the Limpopo River Basin in Mozambique[END_REF][START_REF] Chang | A water quality monitoring network design using fuzzy theory and multiple criteria analysis[END_REF][START_REF] Ouyang | Design optimization of river sampling network using genetic algorithms[END_REF].

In this paper, we proposed a modified MOPSO algorithm to optimize the design of water quality monitoring network based on three optimization objectives: maximum pollution detection probability, minimum pollution detection time and maximum centrality of a monitoring network. Two discrete functions were developed to calculate particle's velocity and update particle's position respectively. Experimental results show that our algorithm can get steady Pareto frontiers and obtain better optimal deployment solutions than genetic algorithm (GA).

Methodology

Hypothetical River Network

To compare our study results with the achievements given by the literature (Quyang et al. 2008; [START_REF] Telci | Real time optimal monitoring network design in river networks[END_REF][START_REF] Telci | Optimal water quality monitoring network design for river systems[END_REF])), we use the same hypothetical river network as Figure 1 shows. There are 6 inlet locations [START_REF] Yang | How to Look at the GDP Receiving Much Attention[END_REF][START_REF] Park | Design of a water quality monitoring network in a large river system using the genetic algorithm[END_REF][START_REF] Chang | A water quality monitoring network design using fuzzy theory and multiple criteria analysis[END_REF][START_REF] Rossman | Storm water management model user's manual, version 5.0[END_REF][START_REF] Coello | MOPSO: a proposal for multiple objective particle swarm optimization[END_REF][START_REF] Telci | Optimal water quality monitoring network design for river systems[END_REF], 5 intermediate locations (2, 4 ,6 ,7, 9) and 1 outlet location (12) in the river network. We assume that a pollution event can occur at any location randomly with the same amount of pollutant spilling and there is only one pollution event at each time. We simulate the water flows for 24 hours from 00:00 to 23:59 with a steady water flow of 10ft 3 /s for each inlet location. The pollution event occurs at 10:00 and lasts for 1 hour during the simulation. We also assume that the pollutant concentration is 10mg/L. The remaining characteristics of the river network is shown in Table 1, which is the same as Telci used [START_REF] Telci | Real time optimal monitoring network design in river networks[END_REF].

Hydraulic Simulations

The Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas [START_REF] Rossman | Storm water management model user's manual, version 5.0[END_REF]. It is widely used for dynamically simulating storm water runoff and drainage systems in urban areas. Here we use SWMM to simulate the hydraulic model, pollution events and pollutants transport along the river system. We set the pollution detection threshold to 0.01mg/L and run hydraulic simulations in SWMM. Table 2 shows the simulation results of pollution detection time for each potential monitoring location. The "_" in Table 2 represents an infinite value, which means the pollution event cannot be successfully detected at a monitoring location.

Optimization objectives a) Minimum Pollution Detection Time and Maximum Pollution Detection Probability

Telci et al. (2008) proposed a real-time optimal monitoring network design in river networks. They designed two optimization objectives of minimum pollution detection

1 0 27 _ 81 _ 118 _ _ _ _ _ 198 2 _ 0 _ 40 _ 75 _ _ _ _ _ 152 3 _ 27 0 81 _ 118 _ _ _ _ _ 198 4 _ _ _ 0 _ 23 _ _ _ _ _ 96 5 _ _ _ 28 0 62 _ _ _ _ _ 139 6 _ _ _ _ _ 0 _ _ _ _ _ 62 7 _ _ _ _ _ 38 0 _ _ _ _ 113 8 _ _ _ _ _ 79 27 0 _ _ _ 157 9 _ _ _ _ _ 111 57 _ 0 _ _ 190 10 _ _ _ _ _ 133 78 _ 10 0 _ 213 11 _ _ _ _ _ 156 99 _ 27 _ 0 236 12 _ _ _ _ _ _ _ _ _ _ _ 0
time and maximum pollution detection. A genetic algorithm (GA) was used to optimize the water quality monitoring network according to these two optimization objectives.

Here we also use the same two objectives in our algorithm. The detailed definition of these two optimization objectives can be found in [START_REF] Telci | Real time optimal monitoring network design in river networks[END_REF].

b) Maximum Centrality of Monitoring Network

We argue that in practical environment, different potential monitoring locations may have different monitoring priorities, which should be considered when we design a monitoring network. In river network simulations, graph theory and network analysis are usually used to model river systems. The centrality is one of the most important indicators in graph theory. It can identify the importance of vertexes in a graph or network. Here we use the closeness centrality as an evaluation criterion of location priority. The closeness centrality for each potential monitoring location is described as Formula 1.

C(i) = [ ∑ 𝑑(𝑖, 𝑗) 𝑚 𝑗=1 𝑚 -1 ] -1 (1)
where m is the total number of potential monitoring locations, d(i,j) is the length from location i to location j. The total closeness centrality for a potential deployment solution S k is shown in Formula 2.

C(𝑆 𝐾 ) = ∑ 𝐶(𝑆 𝑘𝑖 ) 𝑛 𝑖=1 (2)
where n is the number of monitoring devices deployed in a river network, S ki is a monitoring location in deployment solution S k .

The third optimization objective is to maximize the total closeness centrality for all deployment solutions, which is shown in Formula 3.

C(S) = max{C(S 1 ), C(S 2 ), ..., C(S T )} ( 3
)
where T is the total number of potential deployment solutions.

MOPSO Algorithm

Assume that we will deploy n monitoring devices in a river system out of m potential monitoring locations (n <= m). It is easy to know that the total number of potential deployment combinations T is:

T= C m n (4)
We can find from Formula 4 that when we increase the value of m and/or n, the number of deployment solutions will be also increased exponentially, which is too large to calculate using enumeration search methods within a reasonable time. In addition, these optimization objectives normally conflict with each other, which means we aim to find some good trade-off solutions among multi objectives [START_REF] Reyes-Sierra | Multi-objective particle swarm optimizers: A survey of the state-of-the-art[END_REF][START_REF] Coello | MOPSO: a proposal for multiple objective particle swarm optimization[END_REF]. Multi-objective Particle Swarm Optimization (MOPSO) is one of the popular evolution algorithms used in recent year. Coello et al. (2004) compared MOPSO against i three state-of-the art multi-objective evolutionary algorithms of Nondominated Sorting Genetic Algorithm II (NSGA-II), Pareto Archived Evolution Strategy (PAES) and Microgenetic Algorithm for Multi-Objective Optimization (MicroGA) using 5 different test functions. Experimental results show that MOPSO has a highly competitive performance and can be considered a viable alternative to solve multi-objective optimization problems with low computational time. Here we use MOPSO to design an optimal water quality monitoring network. The velocity and position of particles during the computing iteration are updated by the following equations:

V i (t + 1) =w V i (t) + c 1 r 1 (pbest(i, t) -P i (t)) + c 2 r 2 (gbest(t) -P (t)) (5)
P i (t + 1) = P i (t) + V i (t + 1) (6)
where V denotes the particle's velocity, w is an inertia weight constant, r 1 and r 2 are uniformly distributed random variables within range [0, 1], pbest(i,t) is the best position that the particle i has had, gbest(t) is the best position in all current particles, and c 1 and c 2 are positive constant coefficients for acceleration.

procedure INITIALIZATION (Integer k)

for i=1 to k do particle(i).position = [ ]; particle(i).velocity = [ ]; for j=1 to n do particle(i).position(j) = randomi(1, m); particle(i).velocity(j) = 0; end for end for end procedure Fig. 2. Pseudocode of MOPSO initialization procedure VEL_POS_UPDATING (int k) MaxVel = round ((m -1)/10); for i=1 to k do for j=1 to n do particle(i).velocity(j) = round(w * particle(i).velocity(j) +c 1 * r 1 * (particle(i).best.position(j) -particle(i).position(j)) +c 2 * r 2 * (reph.position(j) -particle(i).position(j))); particle(i).velocity(j) = min(max(particle(i).velocity(j), -MaxVel), +MaxVel); particle(i).position(j) = particle(i).position(j) + particle(i).velocity(j); if particle(i).position(j) < 1 or particle(i).position(j) > m then particle(i).velocity(j).flag = -particle(i).velocity(j).flag; particle(i).position(j) = min(max(particle(i).position(j), 1), m); end if end for end for end procedure

Fig. 3. Pseudocode of velocity and position updating

The classical MOPSO is a powerful algorithm to get global optimal results for continuous definition domains. However, it cannot be applied to discrete problems directly. In this paper, we redesign the initialization procedure and the velocity and position calculation function to optimize this discrete issue.

a) Particle design and swarm initialization

Assume we select n locations to deploy water quality monitoring devices out of m potential monitoring locations in a river network (n <= m). Each potential monitoring location is named from 1 to m respectively resulting in a location set S={1, 2, 3, ..., m}. Each particle in a swarm denotes a deployment solution with n monitoring locations. Therefore, each particle has n positions and each position represents a monitoring location in set S. As a result, particle P can be defined as a vector with n elements shown in Formula 7. Assume we create k initial particles, we use a random integer function to initialize n positions and velocities for each particle in MOPSO. The swarm initialization procedure is as Figure 2 shows. The position value for all the particles is constrained between 1 and m. All the velocities are initialized to 0.

P = [p 1 p 2 ... p i ... p n ] subject to n ≤ m & 1 ≤ p i ≤ m (7)

b) Velocity and Position Updating

Equations 5 and 6 show that the original velocity and position in MOPSO are both real values. However, we use integers to denote monitoring locations in a particle. Figure 3 shows a procedure to update particle's velocity and position. We let MaxVel be a maximum velocity during calculation. A round function is used to calculate a new integer value of velocity for each particle based on current gbest and pbest particles. Because the new velocity may be out of the boundary of [-MaxVel,+MaxVel], we use max and min functions to restrict the velocity scope. The detailed velocity updating process is shown in Figure 4.

Simulations and Analysis

We run the MOPSO algorithm several times based on data in Table 2 and get several Pareto frontiers as Figure 5 shows. The simulation results show that these Pareto frontiers are almost the same except for few (one or two) Pareto frontier particles. It means that the simulation results of MOPSO algorithm are quite steady and we can use it to design an optimal water quality monitoring network. Table 3 shows the optimal deployment solutions based on the Pareto frontier particles.

We can find from Table 3 that if we deploy 3 monitoring devices at locations 6, 9 and 12, we can detect all the potential pollution events and the pollution detection time The pollution detection time is significantly decreased to 34.9 minutes while the detection probability is slightly decreased to 91.7%, which is also the second highest pollution detection probability solution in our deployment solutions and we can get a better centrality of 0.05. However, the second highest pollution detection probability in Telci's paper is only 83%.

Conclusions and Future Work

We presented a novel method based on a modified MOPSO algorithm to design an optimal water quality monitoring network with three optimization objectives of minimum pollution detection time, maximum pollution detection probability, maximum closeness centrality. Results show that the modified MOPSO can get a better optimal deployment solutions than GA.

In the future, this novel approach will be applied to a real case of water quality monitoring network. Further research is planned to explore the feasibility of redesigning the velocity and position calculation procedure to avoid same positions in a particle, which can further improve the computing performance.

Fig. 1 .

 1 Fig. 1. Hypothetical river network

Fig. 4 . 2 Fig. 5 .

 425 Fig. 4. Velocity updating process

Table 1 .

 1 Hydraulic characteristics of the river network

	Catchment	Width (ft)	Channel's slope	Manning's coefficient	Flow rate (ft 3 /s)
	A	10	0.0001	0.02	10
	B	10	0.0001	0.02	10
	C	10	0.0001	0.02	10
	D	10	0.0001	0.02	10
	E	10	0.0001	0.02	10
	F	10	0.0001	0.02	10
	G	10	0.0001	0.02	20
	H	10	0.0001	0.02	20
	I	10	0.0001	0.02	30
	J	10	0.0001	0.02	30
	K	10	0.0001	0.02	60

Table 2 .

 2 Pollution detection time at each potential locations

	Locations	1	Pollution detection time and probability at each location 2 3 4 5 6 7 8 9 10 11 12

Table 3 .

 3 Optimal deployment solutions However, the pollution detection time is increased to 54.8 minutes. If the monitoring devices are deployed at locations 4, 6 and 9.

	Monitoring	Detection time	Detection	Centrality
	locations	(min)	probability
	6,9,12	45.8	100%	0.0414
	6,7,12	54.8	100%	0.0455
	4,6,9	34.9	91.7%	0.0500
	2,6,7	36.4	91.7%	0.0514
	4,6,7	44.6	91.7%	0.0561
	4,7,9	29.4	83.3%	0.0487
	2,4,7	34.3	83.3%	0.0505
	2,7,9	14.8	66.7%	0.0451
	2,4,9	14.9	66.7%	0.0455
	2,5,9	13	58.3%	0.0420
	5,7,9	10.7	50%	0.0447
	7,8,9	7.4	41.7%	0.0444
	2,4,5	10.8	41.7%	0.0466
	5,9,11	2.5	33.3%	0.0379
	2,3,5	6.75	33.3%	0.0401
	5,8,10	0	25%	0.0399

Acknowledgements

This work was partly supported by the Natural Science Foundation of Jiangsu Province (BK20151245), the Natural Science Foundation of Huai'an City (HAG2015007), the Natural Science Foundation of Nantong City (MS12016048).


Xianzhe Li1
email: ixianzhe1314@163.com

Weiming Zeng1

Yuhu Shi1

Shaojun Huang1

Resting state fMRI data classification method based on K-means algorithm optimized by Rough Set

Keywords: functional MRI, Rough Set, K-means, resting state

With the development of brain science, a variety of new methods and techniques continue to emerge. Functional magnetic resonance imaging (fMRI) has become one of the important ways to study the brain functional connection and of brain functional connectivity detection because of its noninvasive and repeatability. However, there are still some issues in the fMRI researches such as the amounts of data and the interference noise in the data. Therefore, how to effectively reduce the fMRI data dimension and extract data features has become one of the core content of study. In this paper, a K-means algorithm based on rough set optimization is proposed to solve these problems. Firstly, the concept of important attributes is put forward according to the characteristics of Rough Set, and the attribute importance is calculated by observing the change of attribute positive domain. Then, the best attributes reduction is selected by the attribute importance, so that these important attributes are the best attributes reduction. Finally, the K-means algorithm is used to classify the important attributes. The experiments of two datasets are designed to evaluate the proposed algorithm, and the experimental results show that the K-means algorithm based on rough set optimization has more classification accuracy than the original Kmeans algorithm.

Introduction

The field of neuroinformatics mainly concludes: data collection, organization and analysis of neuroscience data, data calculation model and development of analytical tools, etc. Treated as a comprehensive subject of information science and neuroscience, neuroinformatics plays a vital role in information science and neuroscience research [START_REF] Liu | A swarm-based rough set approach for fMRI data analysis[END_REF]. Functional magnetic resonance imaging (fMRI) technology is one of the most significant approaches to obtain the data of neuroinformatics. It has been widely used in human behavior experiment and pathology because of its noninvasive, repeatability and other advantages [START_REF] Taylor | fMRI and MEG in the study of typical and atypical cognitive development[END_REF][START_REF] Shad | Neurobiology of insight deficits in schizophrenia: an fMRI study[END_REF][START_REF] Huang | Schizophrenia brain resting explore networks and perform network centers and to highlight the relationship between network[END_REF][START_REF] Li | Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a met analysis of 75 fMRI studies[END_REF][START_REF] Yang | Complex network-based connectivity study in patients with brain function[END_REF][START_REF] Shi | A novel fMRI group data analysis method based on data-driven reference extracting from group subjects[END_REF][START_REF] Shi | A new method for independent component analysis with priori information based on multi-objective optimization[END_REF]. FMRI can be used to obtain high-resolution threedimensional images of the brain through the BOLD (blood oxygen level dependent) effect, which can dynamically reflect changes in brain activity signals. However, there are some problems such as large amount of data and excessive interference noise in the research of fMRI. Therefore, how to effectively reduce and extract the feature of fMRI data has become the core contents of the research.

Rough set theory provides a new method that can extract attribute reduction set from fMRI data and obtain feature rules by subtracting the set. Rough set is a mathematical idea proposed by Polish mathematician Pawlak for dealing with uncertainty data in 1980 [START_REF] Pawlak | Rough set[END_REF]. The main idea is to keep the classification ability under the premise of the same, get the problem of classification rules and decision rules through the knowledge reduction [START_REF] Wang | A review of research on rough sets theory and Applications.Journal of Computer Science[END_REF][START_REF] Zhou | Rough Sets Theory &Approaches for Knowledge Acquisition in Incomplete Information Systems[END_REF]. The ultimate goal of rough set theory is to generate the final rule from the information (decision) system. There are two principles for the derivation of a feature rule: first, the rules should be used for the classification of database objects. That is, to predict the categories of unlabeled objects. Second, the rule should be used to develop a mathematical model in the field of research, and this knowledge should be presented in a way that can be understood by people. The main steps to process the data using rough set theory are as follows: (1) mapping information from the original database to the decision table (2) data preprocessing (3) Calculated attribute reduction (4) from the data reduction derived rules (5) rules filtering. One of the most critical tasks is the reduction of the attributes of the data. In general, as a decision table elements, the real object often produces a large amount of data, and these data is not all valuable from the calculation point of view. Therefore, it would be meaningful to be able to extract the most valuable information from the large decision table effectively.

In this paper, a kind of K-means algorithm based on rough set optimization is proposed to apply the classification of fMRI data. First, use the rough set of ideas in the training set of fMRI data on the property reduction. Then, gain the best attribute reduction by calculating the importance of the property, and regard the best attribute reduction as an important attribute. Finally, treat the important attributes as data features, and classify the test fMRI data by k-means algorithm. Furthermore, various fMRI data experiments are used to demonstrate the effectiveness of the proposed method. With each subset XU  , we associate two subsets:

Knowledge of rough sets

(P) [x] [X] ind R PR   (1) (2)
Called the R-upper and R-lower approximations of X, respectively. X's boundary of R

(3)

X's positive region of R (4)
X's negative region of R

Their relationship can be explained by Fig.

1

(Q) (X) {x | x U [x] [x] } P P p Q X U Q Pos Pos       = {W U/ R : W X} ii R  (X) = {W U/ R : W X } i i    R(X) (X) R bn RX RX  (X) R pos RX  (X) R neg
U RX  resolution was 3.5 mm × 3.5 mm, and the slice thickness was 3.5mm.

( The specific parameters for data acquisition are: Magnetic field strength 3.0T

Philips with a gradient echo EPI with 33 slices providing whole-brain coverage and 215 volumes, a TR of 2s and a scan resolution of 64 * 64. The in-plane resolution was 3.13 mm × 3.13 mm, and the slice thickness was 3.6mm.

Attribute importance calculation

Suppose that S=<U, A, V, f> is a knowledge expression system. A C D  , CD  C is the condition attribute set, D is the decision attribute. If

1 2 / C {X , X , , X } n U  , 12 / D {Y , Y , , Y } n U 
the degree of dependence between P and D can be computed as [START_REF] Barnali | Hyper spectral Image Analysis using Neighborhood Rough Set and Mathematical Morphology[END_REF]:

(7)
Thus, the significance of an attribute a ∈ C can be calculated from the set of conditional attributes C as follows:

11 (x) 1 1 (D) [1 ] _(Y ) (Y ) mm c i C i ii cord k C pos d U U      {c} {C} (c) K (D) K (D) D C C C sig   (8)

Best attribute reduction

The attribute significance of each condition attribute from each subject was calculated, and the attribute significance of the same condition attribute was added, the order table of attribute significance was obtained according to the order from large to small as well. We counted the attribute significances which were obtained for the combination of attribute reduction. In the premise of keeping the attribute table decision-making ability unchanged, we selected the least amount and the highest attribute significance from the combination of attribute reduction as the best attribute reduction.

K-means algorithm

Step1 From N data objects, K objects were selected as the initial clustering centers;

Step2 According to the mean (central object) of each clustering object, calculate the distance between each object and the central object, and divide the corresponding object according to the minimum distance;

Step3 Recalculate the mean of each cluster (central object) ;

Step4 Calculate the standard measure function. When a certain condition is satisfied, such as meet the convergence of the function, the algorithm terminates; if the condition is not satisfied, then go back to step 2.

From the training set of data calculating the attribute significance, relying on the attribute significance to select the best attribute reduction of the rough set, the attribute of best attribute reduction was defined as an important attribute, and the rest of the attributes other than the best attribute were defined as non-significant attributes. The test sets for the two sets of data were tested separately; the K-means algorithm was used to classify the important and non-important attributes. Take K = 2, randomly generated the initial center of mass, the classification result was compared with the original data label to obtain the classification accuracy n. Considering that the correspondence between the label after K-means classification and the original label was uncertain, therefore, only values with a classification accuracy greater than 0.5 were selected. If n is less than 0.5, then 1-n is the final classification accuracy. That is, the classification accuracy is at least 0.5, and the highest is 1.

Definition 1 : 12 {

 112 Suppose PR  , P  , P represents the intersection of all equivalence relations in P, as ind(P),and .([x] is the nondistinguishable set of x) Definition 2: Four-tuples=(U, A, V, f) is a knowledge representation system, U represents the domain of non-empty set of object, as non-empty finite set of the indicators, as 12 {a ,a , ,a } the condition attribute and D represents the decision attribute);V is the range of indicator a, as () A V  is an information function which given the value of a message to each attribute, where () (Pawlak approximation space) Let U be a finite nonempty universe of discourse. Let R be an equivalence relation over U. Then the ordered pair <U, R> is a Pawlak approximation space. Approximation operators:Let<U, R>be a Pawlak approximation space U/R = {W1, W2… Wm} is a partition where W1, W2… Wm are all equivalence classes of R.

Fig. 1 ( 1 )

 11 Fig.1 Sketch map of concept of Rough Sets Attribute positive field:Suppose P and Q are the two knowledge of the Domain of U. Definition positive field P of Q in fellow:(6)

Fund Project :National Natural Science Foundation of China (Grants No. 31470954)

Result Analyses

Reductions in fMRI Data for Different Eye States

We randomly selected 30 groups of subjects, a total of 13800 data as a training set, and 4600 data of the remaining 10 subjects as a test set. After acquiring the attribute reductions, the best attribute reduction is obtained by calculating the attribute significance. The best reduction in fMRI data for different eye states consists of 20 brain regions listed in Table 1. Rank in descending order according to attribute importance: {49,4, [START_REF] Shad | Neurobiology of insight deficits in schizophrenia: an fMRI study[END_REF][START_REF] Wang | A review of research on rough sets theory and Applications.Journal of Computer Science[END_REF]15,40,[START_REF] Pawlak | Rough set[END_REF]47,21,46,16,42,41,50,13,22,[START_REF] Zhou | Rough Sets Theory &Approaches for Knowledge Acquisition in Incomplete Information Systems[END_REF]44,39,35}.

Data Atlas

In order to more intuitively reflect the differences between important attributes and non-important attributes, the mean values of the different brain regions of each subject were obtained. The data maps are produced as follows:

( We can clearly see from the two sets of data, in the data of the same control group, the distinction in the data of important attributes is greater, and different categories of data are more clearly distinguished. Therefore, the use of important attributes for classification can have a better effect. And the non-important attribute data is usually more disorganized and less distinct, so using it to classify is often ineffective and may even affect judgement.

Clustering Algorithm Based on Rough Set Optimization

The result of the attribute reduction is taken as an important attribute, and the data that has been subtracted is taken as a non-important attribute. Then the K-means algorithm is used to classify the raw data, the data containing only the important attributes, and the data containing only the non-significant attributes, respectively, where K=2. The results are as follows: It can be found that, using the important attributes that obtained by the attribute reduction of the rough set to classify, can have a better effect. However, using the nonimportant attributes that has been subtracted to classify, its accuracy will be less than the raw data, and much less than the important attributes.

Discuss

In this paper, a K-means algorithm based on rough set optimization is proposed and applied to the hierarchical fMRI data classification. First, the concepts of important and non-important attributes were put forward. Applicate the rough set theory in the training data set for reduction of attributes, and select the best attribute reduction through the importance of the property. Regard the best attribute reduction of the attribute as an important attribute, the rest attributes as non-important attributes. The important attributes are the most significant cluster of data in the original attributes, and the rest of the attribute often plays a very weak role in the classification, and even affects the classification result. It can be seen from the experiment that the accuracy of classification using the important attributes will be higher than the original data, and the accuracy of the use of non-important attributes of the classification will be lower than the original data, and far below the important attributes. Therefore, the use of important attributes for classification calculation can greatly reduce the workload and can achieve better classification results. In the next step, the experiment can be improved by optimizing the attribute reduction algorithm, selecting the more reasonable strategy of superiority, dividing the more detailed brain region and so on, and obtain the scientific result.


Qianzi Shen

Zijian Wang

Yaoru Sun

Sentiment Analysis of Movie Reviews Based on CNN-BLSTM

Keywords: Natural Language Processing, CNN, LSTM, Sentiment Analysis

Sentiment analysis has been a hot area in the research field of language understanding, but complex deep neural network used in it is still lacked. In this study, we combine convolutional neural networks (CNNs) and BLSTM (bidirectional Long Short-Term Memory) as a complex model to analyze the sentiment orientation of text. First, we design an appropriate structure to combine CNN and BLSTM to find out the most optimal one layer, and then conduct six experiments, including single CNN and single LSTM, for the test and accuracy comparison. Specially, we pre-process the data to transform the words into word vectors to improve the accuracy of the classification result. The classification accuracy of 89.7% resulted from CNN-BLSTM is much better than single CNN or single LSTM. Moreover, CNN with one convolution layer and one pooling layer also performs better than CNN with more layers.

Introduction

As the significant carries of emotion, texts are in an irreplaceable position on the Internet. The sentiment analysis is usually used to measure the emotional attitude of texts and related studies grow quickly. In the literature, Hazivassiloglou and McKeown investigated semantic orientation of adjectives based on cluster methods [START_REF] Hatzivassiloglou | Predicting the semantic orientation of adjectives[END_REF]. Turney and Littman chose seven pairs of words that have strong orientation and then set SO-PMI (semantic orientation-pointwise mutual information) to judge a word with a standard word. According to SO-PMI, they chose two groups of words in which one is positive ( P word ) and the other is negative ( N word ) as standard words [START_REF] Turney | Thumbs up or down Semantic orientation applied to unsupervised of reviews[END_REF]. Kim chose a method based on sentiment knowledge and summed up the weighing of phrases and describing words [START_REF] Kim | Automatic detection of opinion bearing words and sentences[END_REF]. Pang studied sentiment analysis of English texts with machine learning [START_REF] Pang | Thumbs up?: sentiment classification using machine learning techniques[END_REF]. With the development of artificial intelligence and deep learning, more and more related networks and methods have been involved in natural processing. In a variety of networks, CNN [START_REF] Bouvrie | Notes on convolutional neural networks[END_REF] is widely used for its generalization. Because of its advantage in learning the higher level features, CNN has achieved a great breakthrough [START_REF] Lecun | Deep learning[END_REF] and being applied to many areas, such as image classification, face recognition and other tasks related to image [START_REF] Gu | Recent advances in convolutional neural networks[END_REF]. Among them, LSTM [START_REF] Hochreiter | Long short-term memory[END_REF] is a kind of recurrent neural networks and is able to keep the information for a long time because it has memory cells. In other words, LSTM is a time-order network [START_REF] Sundermeyer | From feedforward to recurrent LSTM neural networks for language modeling[END_REF],while BLSTM (Bidirectional Long Short Term Memory) is a kind of network combining LSTM with BRNN [START_REF] Schuster | Bidirectional recurrent neural networks[END_REF], which has two directions to input data and two hiding layers to save the information in both directions and sharing the same output layer. In this paper, we designed a more complex model containing both CNN and BLSTM for sentiment analysis.

The rest of the paper is organized as follows: Section 2 reviews the related work to CNN and LSTM. Section 3 describes the model we used in this work. Section 4 shows the experiments and their results. Section 5 presents the conclusion and future works.

2

Methods and Models

CNN

Usually, convolutional neural networks [START_REF] Bouvrie | Notes on convolutional neural networks[END_REF] are used in image classification. In this study, CNN was used as a feature important part in our sentiment analysis model. Convolutional neural networks provide a learning model which is end to end and the parameters of the model can be trained with gradient descent algorithm. A typical convolutional neural network consists of an input layer, convolution layers, pooling layers, a full connection layer and an output layer. Its architecture is shown in Figure 1. Both images and texts can be presented as a matrix. The matrix V is the input of a CNN. We suppose that 𝐻 𝑖 is the 𝑖 layer of the network ( 𝑉 = 𝐻 0 ).

If 𝐻 𝑖 is a convolutional layer: 𝐻 𝑖 = 𝑓(𝐻 𝑖-1 ⊙ 𝑊 𝑖 + 𝑏 𝑖 ) (1) In formula (1) 𝑊 𝑖 is the weight vector of 𝐻 𝑖 layer. Operator ⊙ is the convolution operation of the convolution kernel and the 𝐻 𝑖-1 layer. The output of the convolution will be added with the offset 𝑏 𝑖 . And finally, 𝐻 𝑖 could be calculated by a nonlinear excitation 𝑓(𝑥).

If 𝐻 𝑖 is a pooling layer:

𝐻 𝑖 = 𝑠𝑢𝑏𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔(𝐻 𝑖-1 ) (2)
The purpose of this layer is to reduce dimensionality and keep the features stable to some extent.

What we want is a probability distribution 𝑌. With several convolution layers and pooling layers, we get a model:

𝑌(𝑖) = 𝑃(𝐿 = 𝑙 𝑖 |𝐻 0 ; (𝑊, 𝑏)) (3)
Gradient descent algorithm is used to train the parameters (𝑊and 𝑏) of each layer.

𝐸(𝑊, 𝑏) = 𝐿(𝑊, 𝑏)

+ 𝜆 2 𝑊 𝑇 𝑊 (4)
𝑊 𝑖 = 𝑊 𝑖 -𝜂 𝜕𝐸(𝑊,𝑏) 𝜕𝑊 𝑖 (5)
𝑏 𝑖 = 𝑏 𝑖 -𝜂 𝜕𝐸(𝑊,𝑏) 𝜕𝑏 𝑖 (6)
𝜆 is the parameter to control the overfitting and 𝜂 is the learning rate.

LSTM and BLSTM

LSTM [START_REF] Hochreiter | Long short-term memory[END_REF] is a kind of recurrent neural networks (RNN). Because of its ability to make use of long term information, it is suitable for sequential data processing in texts. LSTM unit is shown in Figure 2. And BLSTM [START_REF] Schuster | Bidirectional recurrent neural networks[END_REF] is included in LSTM, which is bidirectional. A LSTM memory block unit contains three gates, input gate, forget gate and output gate. These gates facilitate saving, reading, resetting and updating the long term information. We supposed x as the input, c as the memory cell and the h as the output. We calculate the candidate cell 𝑐̃𝑡, 𝑊 𝑥𝑐 and 𝑊 ℎ𝑐 are weights of input and the output from previous time.

𝐻 𝑖 = 𝑓(𝐻 𝑖-1 ⊙ 𝑊 𝑖 + 𝑏 𝑖 )

Input gate value 𝑖 𝑡 is used to control the impact of memory cell. Forget gate value 𝑓 𝑡 is used to control the impact from history information to current memory. And output gate value is used to control the output of the unit.

𝑖 𝑡 = 𝜎(𝑊 𝑥𝑖 𝑥 𝑡 + 𝑊 ℎ𝑖 ℎ 𝑡-1 + 𝑊 𝑐𝑖 𝑐 𝑡-1 + 𝑏 𝑖 ) (8) 𝑓 𝑡 = 𝜎(𝑊 𝑥𝑓 𝑥 𝑡 + 𝑊 ℎ𝑓 ℎ 𝑡-1 + 𝑊 𝑐𝑓 𝑐 𝑡-1 + 𝑏 𝑓 ) (9) 𝑜 𝑡 = 𝜎(𝑊 𝑥𝑜 𝑥 𝑡 + 𝑊 ℎ𝑜 ℎ 𝑡-1 + 𝑊 𝑐𝑜 𝑐 𝑡-1 + 𝑏 𝑜 ) (10) 𝑥 𝑡 is the current input value. ℎ 𝑡-1 is the previous output value . And 𝑐 𝑡-1 is the previous memory cell. 𝜎 is an activation function that we usually choose logistic sigmoid algorithm for it.

𝑐 𝑡 = 𝑓 𝑡 ⊗ 𝑐 𝑡-1 + 𝑖 𝑡 ⊗ 𝑐̃𝑡 (11) ℎ 𝑡 = 𝑜 𝑡 ⊗ tanh(𝑐 𝑡 ) (12)
3 CNN-BLSTM model (13) In this formula, 𝑓(𝑥) is a truncation function to reduce the disturbance of frequently used words.

CNN-BLSTM model

In this paper, our model combined one-layer CNN and BLSTM. Each word in the texts has been processed to be a 50-dimensional vector in the preprocessing of the model. The embedding processing is shown in Figure 1. The pretrained words in the dataset were projected into word vectors in input part of the our model. A convolution layer, a maxpolling layer, a dropout layer and a BLSTM layer was followed sequentially. The whole structure is shown in Figure 2. The input is the result from word embedding process. The input layer receive a maximum of 1000 word vectors, each of them has 50 dimensions. We used convolution kernel size of 5 in CNN layer with activation function of LeakyRelU,a max pooling layer followed it with pool size of 4. In order to prevent model overfitting, we used a dropout layer after the last max pooling layer. The last part of the model is a BLSTM layer and two dense in which the last dense used softmax as activation and output 2 dimension vector refer to probabilities of the positive and negative classes.

4

Experiments and results

Dataset

We used the dataset about the sentiment classifications of movie reviews in IMDB. The labeled dataset consists of 50000 IMDB movie reviews. They only have two types of reviews, positive or negative. The whole dataset was divided into two parts. 40000 labeled reviews were training set and the other 10000 were test set.

Experiments

Experiment 1.

We compared simple BLSTM and CNN-BLSTM model for sentiment classifications. BLSTM model used a BLSTM layer with 128 unit outputs. CNN-BLSTM model connected a CNN layer with the size of 5-unit kernel, a max pooling layer with pool size 4 and an BLSTM layer with 128-unit output. The maximum feature numbers are 20000, maximum lengths are 1000 and the loss function is categorical cross entropy in the both models. The results are shown in Table 1. The accuracy of each single model is respectively 82.5% and 85.3%. CNN-BILSTM performed better than BLSTM model. The local semantic information in a sentence extracted by the convolution layer could lead to the better performance of CNN-BLSTM.

Experiment 2.

We compared six different models. Three of them are models with respectively 1,2 or 3 CNN layers with a BLSTM layer and a full connected layer with pre-trained word embedding (using GloVe, dimension: 50), and the others are models without pretrained word embedding. In every model, each CNN layer used 64 filters with kernel size 5, each CNN layer was followed by a max-pooling layer with pool size 4, the last pool layer was followed by a dropout layer and a BLSTM layer with 128 unit outputs, the last layers were two full connected layers with respectively 128 unit outputs / activation function of LeakyReLU and 2-unit outputs / activation function of softmax. Every model was trained with 10 epochs, the training batch size is 128 with loss of categorical cross entropy and RMSProp optimizer. The comparison results are shown in Table 2. At first we could compare the models with pretrained word embedding and models without those, the result showed that average accuracy of the former is 84.5% while average accuracy of the latter is 82.0%. It's obviously that the pretrain word embedding enhanced the performance of CNN-BLSTM, which could be caused by the extra semantic information brought by word embedding.

We could also compared models with different numbers of CNN layer. It was kind of weird that the models with more CNN layer got worse performance. This consequence might be on the contrary of the cognition of us that the model with more complex structure should perform better. But it's known that IMDB datasets only have 50000 samples (40000 for training and 10000 for testing). The consequence could be the results of under-fitting of the complex model.

Conclusion and Future Work

In this work, we described a series of experiments with CNN and BLSTM and obtained a satisfactory accuracy that could reach up to 89.7%. The whole process contained two steps. The first step was the data pre-processing and each word was turned into a 50D word vector. The result was obviously better if we did word embedding before the data was put into our model directly.

The second step was to accomplish the sentiment analysis with CNN and BLSTM. In this step, we changed the numbers of layers of convolution and pooling in CNN to find a simple CNN with one layer of convolution and one layer of pooling which performed best by comparison. The accuracy of two-layer CNN which contained two convolution layers and two pooling layers was 86.2% and the accuracy of three-layer CNN which contained three convolution layers and three pooling layers was 77.7%.

In the model, the dropout function was also added to make the result reliable because of its reduction of over fitting. We also investigated whether the additional pre-processing of the data could give the result an advantage like adding POS (part of speech) tagging to the experiment, because not all of the words make sense. In general, adjectives and adverbs describe the feelings of the authors and therefore in the future work, we will devote to exploring more significant methods to do pre-processing of the natural language data.

Fig. 1 .

 1 Fig. 1. structure of typical CNN.

Fig. 2 .

 2 Fig. 2. LSTM memory block.

Fig. 3 .

 3 Fig. 3. word embedding.

Fig. 4 .

 4 Fig. 4. CNN&BLSTM model structure.

Table 1 .

 1 Comparison of BLSTM and CNN-LSTM.

		BLSTM	CNN-BLSTM
	Accuracy	82.5%	85.3%

Table 2 .

 2 Comparison of 6 models.

		1CNN-	2CNN-	3CNN-	Average
		BLSTM	BLSTM	BLSTM
	With pretrained word embedding	89.7%	86.2%	77.7%	84.5%
	Without pretrained word embed-	85.3%	81.6%	79.1%	82.0%
	ding
	Accuracy	87.9%	83.9%	78.4%
	The results showed that the 1CNN-BLSTM model with pretrained word embedding
	got the best accuracy 89.7%, and the 3CNN-BLSTM model with pretrained word em-
	bedding got the lowest accuracy 77.7%.

Acknowledgment

This work was supported by the Grant from the National Natural Science Foundation of China (61173116).


Zhongzhi Shi
email: shizz@ics.ict.ac.cn

, Gang Ma

Jianqing Li

Collaborative Model in Brain-Computer Integration

Keywords: brain-computer integration, environment awareness, ABGP-CNN model, motivation driven collaboration, motivation learning

Brain-computer integration is a new intelligent system based on braincomputer interface technology, which is integrated with biological intelligence and artificial intelligence. In order to make this integration effective and co-adaptive biological brain and machine should work collaboratively. ABGP-CNN based environment awareness and motivation driven collaboration will be presented in the paper. Motivation is the cause of action and plays important roles in collaboration. The motivation leaning method and algorithm will be explored in terms of event curiosity, which is useful for sharing common interest situations.

Introduction

Brain-computer integration is a new intelligent system based on brain-computer interface technology, which is integrated with biological intelligence and artificial intelligence [START_REF] Shi | Mind Computation[END_REF]. Brain-computer integration is an inevitable trend in the development of braincomputer interface technology. In the brain-computer integration system, the brain and the brain, the brain and the machine is not only the signal level of the brain machine interoperability, but also need to integrate the brain's cognitive ability with the computer's computing ability. But the cognitive unit of the brain has different relationship with the intelligent unit of the machine. Therefore, one of the key scientific issues of brain computer integration is how to establish the cognitive computing model of brain-computer integration. At present, brain-computer integration is an active research area in intelligence science. In 2009, DiGiovanna developed the mutually adaptive brain computer interface system based on reinforcement learning [START_REF] Digiovanna | Coadaptive brain-machine interface via reinforcement learning[END_REF], which regulates brain activity by the rewards and punishment mechanism. The machine adopts the reinforcement learning algorithm to adapt motion control of mechanical arm, and has the optimized performance of the manipulator motion control. In 2010, Fukayarna et al. control a mechanical car by extraction and analysis of mouse motor nerve signals [START_REF] Fukuyama | RatCar: a vehicular neuro-robotic platform for a rat with a sustaining structure of the rat body under the vehicle[END_REF]. In 2011, Nicolelis team developed a new brain-machine-brain information channel bidirectional closed-loop system reported in Nature [START_REF] O'doherty | Active tactile exploration using a brain-machine-brain interface[END_REF], turn monkey's touch information into the electric stimulus signal to feedback the brain while decoding to the nerve information of monkey's brain, to realize the brain computer cooperation. In 2013, Zhaohui Wu team of Zhejiang University developed a visual enhanced rat robot [START_REF] Wang | Ratbot: A rat "understanding" what humans see[END_REF]. Compared with the general robot, the rat robot has the advantage in the aspects of flexibility, stability and environmental adaptability.

Brain-computer integration system has three remarkable characteristics: (a) More comprehensive perception of organisms, including behavior understanding and decoding of neural signals; (b) Organisms also as a system of sensing, computation body and executive body, and information bidirectional exchange channel with the rest of the system; (c) Comprehensive utilization of organism and machine in the multi-level and multi-granularity will achieve system intelligence greatly enhanced.

The core of brain-computer integration is the cognitive computation model of brain computer collaboration. Cognitive process of brain computer collaboration is composed by the environment awareness, motivation analysis, intention understanding and action planning and so on, in support of the perception memory, episodic memory, semantic memory and working memory to complete brain machine group awareness and coordinated action. Supported by the National Program on Key Basic Research Project we are engaging in the research on Computational Theory and Method of Perception and Cognition of Brain-Computer Integration. The main goal of the project is the exploration of cyborg intelligence through brain-computer integration, enhancing strengths and compensating for weaknesses by combining the biological cognition capability with the computer computational capability. In order to make this integration effective and co-adaptive, brain and computer should work collaboratively. We mainly focus on four aspects, environment awareness, cognitive modeling, joint intention and action planning, to carry out the research of cognitive computational model.

(1) The environmental awareness of brain computer collaboration. For brain computer bidirectional information perception characteristics, the integration of visual features of the Marr visual theory and Gestalt whole perception theory in the wide range, research on the environment group awareness model and method by combination of brain and computer. The discriminative, generative and other methods are applied to analyze the features of environment perception information, mine perception information patterns and knowledge, generate high-level semantics, and understand well the environment awareness, build brain computer collaborative group awareness model.

(2) Cognitive modeling of brain computer collaboration. This involves combining the mutual cognitive characteristic of the brain computer, utilize and study the achievement made in intelligent science about consciousness, memory, study the cognitive cycle in brain computer collaborative information processing. According to the characteristic of the collaborative work of the brain computer, utilize the research results of physiological mechanism of brain information processing, study information representation method and reasoning mechanism in episodic memory and semantic memory of human brain, carry on cognitive modeling to information processing procedure in the brain computer collaboration.

(3) The joint intention driven by motivation. The essential characteristic of the brain computer collaborative work is that the agents have common goals, commitments, intentions, etc. that are jointly restrained. In order to describe the characteristics that should have for the brain computer system, study the joint intention theory to describe union restrictions of autonomous agents and reason balance of agent mental state; To the needing of brain computer collaborative work, study the essence of the behavior motivation and generation mechanism, put forward the intention model driven by motivation; Study the joint intention method for multi-agents, offer theory support for constructing the brain computer collaborative work of multi-agents.

(4) Action planning for brain computer collaboration. Under the support of ontology knowledge system, study action planning method of brain computer collaboration; using reinforcement learning and Markov decision theory, study the part of perception of the planning method, present the action planning theory with learnability and optimization methods.

Collaborations occur over time as organizations interact formally and informally through repetitive sequences of negotiation, development of commitments, and execution of those commitments. Both cooperation and coordination may occur as part of the early process of collaboration, collaboration represents a longer-term integrated process. Gray describes collaboration as a process through which parties who see different aspects of a problem can constructively explore their differences and search for solutions that go beyond their own limited vision of what is possible. [START_REF] Gray | Collaborating: Finding Common Ground for Multiparty Problems[END_REF] In this paper, a collaborative model for cyborg intelligence will be proposed in terms of external environment awareness and internal mental state. The collaboration between brain and computer is driven by motivation which is generated dynamically.

Conceptual Framework of Brain-Computer Integration

An effective approach to implementing engineering systems and exploring research problems in cyborg intelligence is based on brain-computer integration methods [START_REF] Wu | Cyborg Intelligence: Research Progress and Future Directions[END_REF]. Using these methods, computers can record neural activity at multiple levels or scales, and thus decode brain representation of various functionalities, and precisely control artificial or biological actuators. In recent decades, there have been continuous scientific breakthroughs regarding the directed information pathway from the brain to computers. Meanwhile, besides ordinary sensory feedback such as visual, auditory, tactile, and olfactory input, computers can now encode neural feedback as optical or electrical stimulus to modulate neural circuits directly. This forms the directed information pathway from the computer to the brain. These bidirectional information pathways make it possible to investigate the key problems in cyborg intelligence. How to interact between brain and computer at various is a critical problem in braincomputer integration. On the basis of the similarity between brain function partition and corresponding computing counterparts, a hierarchical and conceptual framework for brain-computer integration is proposed. The biological part and computing counterparts are interconnected through information exchange, and then cooperate to generate perception, awareness, memory, planning, and other cognitive functions.

For the brain part, abstracted the biological component of cyborg intelligence into three layers: perception and behavior, decision making, memory and consciousness (Figure 1). We also divided the computer functional units into three corresponding layers: awareness and actuator, planning, motivation and belief layers. We also defined two basic interaction and cooperation operations: homogeneous interaction (homoraction) and heterogeneous interaction (heteraction). The former represents information exchange and function recalls occurring in a single biological or computing component, whereas the latter indicates the operations between the function units of both biological and computing parts. Homoraction is also modeled as the relationship between units within the same part. In the case of a single part in a brain-computer integration system, it will reduce to a biological body or computing device just with homoraction inside. Consequently, verifying the existence of heteraction is necessary for cyborg intelligent systems. As typical brain-computer integration systems of "animal as the actuators", rat cyborgs [START_REF] Berger | A Cortical Neural Prosthesis for Restoring and Enhancing Memory[END_REF] [9], were developed to validate how the animals can be enhanced by the artificial intelligence. Ratbots are based on the biological platform of the rat with electrodes implanted in specific brain areas, such as the somatosensory cortex and reward area [START_REF] Feng | A Remote Control Training System for Rat Navigation in Complicated Environment[END_REF]. These electrodes are connected to a backpack fixed on the rat, which works as a stimulator to deliver electric stimuli to the rat brain. Figure 2 shows the physical implementation of the rat-robot navigation system [START_REF] Yu | Automatic training of ratbot for navigation[END_REF]. In the automatic navigation of rats, five bipolar stimulating electrodes separately are implanted in medial forebrain bundle (MFB), somatosensory cortices (SI), and periaqueductal gray matter (PAG) of the rat brain. There is also a backpack fixed on the rat to receive the wireless commands.

Fig. 2. Rat cyborg

There are two components which are necessary to implement the automatic navigation. Firstly, the communication between a computer and a rat needs to be solved. The stimulation signals are delivered by a wireless backpack stimulator which is comprised of stimulating circuit, control processor and Bluetooth transceivers. The control processor receives the computer instructions through the Bluetooth transceivers. Then it sends commands to the stimulator to control the rat behaviors. By receiving commands from the machine, the rat can perform a lot of navigation tasks, e.g. walking around mazes, climbing bridges, and stopping at a special place. Secondly, a video camera device used to capture the rat movement is installed above the scenario. With the video captured by the birdeye camera, the machine can establish a map of the environment and analyze the real time kinetic state of the rat [START_REF] Wu | Maze Learning by a Hybrid Brain-Computer System[END_REF]. For vison-enhanced ratbots, a mini-camera is connected to the backpack to capture movement or the surrounding environment. A computer analyzes video stream input and generates stimulation parameters that are then wirelessly sent to the backpack stimulator to control the rat's navigation behavior by manipulating virtual sensation or reward. Paper [START_REF] Wang | Visual Cue-Guided Rat Cyborg for Automatic Navigation[END_REF] reported that vision-enhanced ratbots can precisely find "human-interesting" objects, i.e., human faces and arrow signs, identified by object detection algorithms.

ABGP-CNN Based Environment Awareness

Agent can be viewed as perceiving its environment information through sensors and acting environment through effectors. As an internal mental model of agent, BDI model has been well recognized in philosophical and artificial intelligence area. As a practical agent existing in real world should consider external perception and internal mental state of agents. In terms of these considerations we propose a cognitive model through 4-tuple <Awareness, Belief, Goal, Plan>, and the cognitive model can be called ABGP model as shown in Figure 3 [START_REF] Shi | A cognitive model for multi-agent collaboration[END_REF].

Motivation Driven Collaboration

Motivation is an internal motive force and subjective reasons, which direct drive the individual activities to achieve a certain purpose, and the psychological state initiated and maintained by individual activities. Psychologists define motivation as the process that initiates, guides, and maintains goal-oriented behaviors. All kinds of behaviors and activities of the people can't be separated from the motivation. Consider the dual nature of motivation, that is implicit and explicit, the motivation process is complexity. In general, implicit motivational processes are primary and more essential than explicit motivational processes. Here we only focus on explicit motivation and hypothesize that the explicit motivational representations consist mainly of explicit goals of an agent. Explicit goals provide specific and tangible motivations for actions. Explicit goals also allow more behavioral flexibility and formation of expectancies. In cyborg intelligent system we have developed two approaches for brain computer integration, that is, needs based motivation and curiosity based motivation.

Needs based motivation

In 1943, humanistic psychologist Maslow put forward the demand theory of motivation. Maslow's assumption that people in need, the sequence of human motivation, from the most basic physiological and safety needs, through a series of love and respect, the complex needs of self-realization, and need level has great intuitive appeal [START_REF] Maslow | Motivation and Personality[END_REF]. Green and others advocated that the theory of motivation is divided into 3 categories of physiology, behavior and social [START_REF] Green | Human motivation: physiological, behavioral and social approaches[END_REF]. Merrick proposed that the motivation theory is divided into 4 categories, namely, the biological theory, cognitive theory, social theory and the combined motivation theory [START_REF] Merrick | Modelling Motivation for Experience-Based Attention Focus in Reinforcement Learning[END_REF]. The biological motivation theory tries to explain the motivation of the work process based on the biological level of natural system. The mechanisms of these theories are often explained by energy and motion, which make the organism toward a certain behavior. The existing research on artificial system has been used to create the simulation of software agent and natural system using the theory of biological motivation. Bach proposed the MicroPsi architecture of motivated cognition based on situated agents [START_REF] Bach | Principles of Synthetic Intelligence -An architecture of motivated cognition[END_REF]. MicroPsi explores the combination of a neuro-symbolic cognitive architecture with a model of autonomous, polytelic motivation. The needs of MicoPsi cognitive system fall into three groups: physiological needs, social needs and cognitive [START_REF] Bach | Modeling Motivation in MicroPsi 2[END_REF]. Physiological needs regulate the basic survival of the organism and reflect demands of the metabolism and physiological well-being. Social needs direct the behavior towards other individuals and groups. They According to brain computer integration requirements, a motivation could be represented as a 3-tuples {N,G,I}, where N means needs, G is goal, I means the motivation intensity [START_REF] Shi | A Motivational System For Mind Model CAM[END_REF].

A motivation is activated by motivational rules which structure has following format: R=(P, D, Strength(P|D)) where, P indicates the conditions of rule activation; D is a set of actions for the motivation; Strength(P|D) is a value within interval [0,1].

Curiosity based motivation

Curiosity based motivation is through motivation learning algorithm to build a new motivation. Agent creates internal representations of observed sensory inputs and links them to learned actions that are useful for its operation. If the result of the machine's action is not relevant to its current goal, no motivation learning takes place. This screening of what to learn is very useful since it protects machine's memory from storing unimportant observations, even though they are not predictable by the machine and may be of sufficient interest for novelty based learning. Novelty based learning still can take place in such a system, when the system is not triggered by other motivations. Motivation learning requires a mechanism for creating abstract motivations and related goals. Once implemented, such a mechanism manages motivations, as well as selects and supervises execution of goals. Motivations emerge from interaction with the environment, and at any given stage of development, their operation is influenced by competing event and attention switching signals.

The learning process for motivations to obtain the sensory states by observing, then the sensed states are transformed mutually by the events. Where to find novelty to motivate an agent's interestingness will play an important role. Once the interestingness is stimulated, the agent's attention may be selected and focused on one aspect of the environment. Therefore, it will be necessary to define observations, events, novelty, interestingness and attention before descripting the motivation learning algorithm.

Definition 1: Observation Functions Observation functions define the combinations of sensations from the sensed state that will motivate further reasoning. Observations containing fewer sensations affect an agent's attention focus by making it possible for the agent to restrict its attention to a subset of the state space. Where, a typical observation function can be given as:

𝐎 S(t) = {(o 1(t) , o 2(t) , ⋯ , o L(t) , ⋯ )|o L(t) = s L(t) (∀L)} (1)
The equation defines observation function 𝐎 S(t) in which each observation focuses on every element of the sensed state at time t.

Definition 2: Difference Function A difference function  assigns a value to the difference between two sensations S L(t) and S L(t ′ ) in the sensed states S (t) and S (t') as follows:

∆ (s L(t) , s L(t ′ ) ) = { s L(t) ; if ¬∃s L(t ′ ) s L(t ′ ) ; if ¬∃s L(t) s L(t) -s L(t ′ ) ; if s L(t) -s L(t ′ ) ≠ 0 0; otherwise (2)
Difference function offers the information about the change between successive sensations it calculates the magnitude of the change.

Definition 3: Event Function

Event functions define which combinations of difference variables an agent recognizes as events, each of which contains only one non-zero difference variable. Event function can be defined as following formula:

𝐄 S(t) = {E L(t) = (e

Events may be of varying length or even empty, depending on the number of sensations to change.

Definition 4: Novelty Detection Function

The novelty detection function, N, takes the conceptual state of the agent, c ∈ 𝐂, and compares it with memories of previous experiences, m ∈ 𝐌, constructed by long term memory to produce a novelty state, n ∈ 𝐍: 𝐍 ∶ 𝐂 × 𝐌 → 𝐍 (5) Novelty can be detected by introspective search comparing the current conceptual state of an agent with memories of previous experiences [START_REF] Shi | A cognitive model for multi-agent collaboration[END_REF].

Definition 5: Interestingness Function

The interestingness function determines a value for the interestingness of a situation, i ∈ 𝐈, basing on the novelty detected, n ∈ 𝐍: 𝐈 ∶ 𝐍 → 𝐈 (6) Definition 6: Attention Selection Selective attention enables you to focus on an item while mentally identifying and distinguishing the non-relevant information. In cyborg we adopt maximal interestingness strategy to select attentions to create a motivation.

The following describes the basic steps of novelty based motivation learning and goal creation algorithm in the cyborg system.

Motivation learning algorithm

Motivation execution

In cyborg system, the realization of the motivation module is through agent model ABGP. The current belief of the belief memory storage contains the agent motivation base. A desire is a goal or a desired final state. Intention is the need for the smart body to choose the current implementation of the goal. In agent, the goal is a directed acyclic graph by the sub goal composition, and realizes in step by step. According to a directed acyclic graph a sub goal is represented by a path to complete, the total goal will finish when all sub goals are completed.

Collaboration

In brain-computer integration rat brain should work with machine collaboratively. Here rat brain and machine can be abstracted as agent, so the collaboration can be viewed as joint intention. Joint intention is about what the team members want to achieve. Each team member knows the intention specifically and achieves it by collaboration [START_REF] Shi | Computational Cognitive Models for Brain-Machine Collaborations[END_REF].

In the joint intention theory, a team is defined as "a set of agents having a shared objective and a shared mental state". The team as a whole holds joint intentions, and each team member must inform others whenever it detects the goal state change, such as goal is achieved or the goal is no longer relevant.

For the joint intention, rat agent and machine agent have three basic knowledge: first, each one should select its intention; second, each one knows its cooperator who also select the same intention; and last, each one knows they are a team. They can know each other through agent communication.

Simulation Experiments

ABGP-CNN as the detailed implementation for the conceptual framework of braincomputer integration, here we give a simulation application to significantly demonstrate feasibility of conceptual framework of brain-computer integration based ABGP-CNN Agent model. The following will mainly represent the actual design of the rat agent based on ABGP-CNN supported by the conceptual framework of brain-computer integration.

Under belief knowledge conditions, the goals (here mainly visual information) constantly trigger the awareness module to capture environment visual information, and the event module converts the visual information into the unified internal motivation signal events which are transferred to action plan module. Then the action plan module will select proper actions to response the environment.

In simulation application, we construct a maze and design a rat agent based on ABGP-CNN to move in the maze depending on the guidepost of maze path in Figure 4. The task of the rat agent is to start moving at the maze entrance (top-left of maze), and finally reach the maze exit (bottom right of maze) depending on all guideposts. In order to fulfill the maze activity, the rat agent is implemented all the three basic modules, <Awareness>, <Motivation>, <Action Plan>. In the rat maze activity experiment, the rat agent is designed to have 3 basic motivation behaviors moving on, turning left and turning right in the maze. In order to guide rat's behaviors we construct a true traffic guidepost dataset means 3 different signals, moving on, turning left and turning right. The different signal corresponds to different guidepost images like in Figure 5. When rat agent moves on the path, its goals constantly drive awareness module to capture environment visual information (here guideposts in the maze) and generate the motivation signal events to drive its behaviors plan selection. In the experiment, there are 3 motivation signals, moving on, turning left and turning right according to the guideposts in the maze path. Which means the agent can response 3 types of action plans to finish the maze activities.

Conclusions

This paper described the collaborative model of brain-computer integration, which is a new intelligent system based on brain-computer interface technology. In order to make this integration effective and co-adaptive biological brain and computer should work collaboratively. ABGP-CNN based environment awareness and motivation driven collaboration have been proposed in the paper. Motivation is the cause of action and plays important roles in collaboration. The motivation leaning method and algorithm has been explored in terms of event curiosity, which is useful for sharing common interest situations.

The future of brain-computer integration may lead towards many promising applications, such as neural intervention, medical treatment, and early diagnosis of some neurological and psychiatric disorders. The goal of artificial general intelligence (AGI) is the development and demonstration of systems that exhibit the broad range of general intelligence. The brain-computer integration is one approach to reach the AGI. A lot of basic issues of brain-inspired intelligence are explored in the book [START_REF] Shi | Mind Computation[END_REF] in detail.

Fig. 1 .

 1 Fig. 1. Cognitive Computational Model

Fig. 3 .

 3 Fig. 3. Agent ABGP model

  are satisfied and frustrated by social signals and corresponding mental representations. Cognitive needs give rise to open-ended problem solving, skill-acquisition, exploration, play and creativity. Urges reflect various physiological, social and cognitive needs. Cognitive processes are modulated in response to the strength and urgency of the needs.

Fig. 4 .

 4 Fig. 4. Rat agent activities in maze

Fig. 5 .

 5 Fig. 5. Traffic guideposts in maze

Acknowledgements

This work is supported by the National Program on Key Basic Research Project (973) (No. 2013CB329502), National Natural Science Foundation of China (No. 61035003), National Science and Technology Support Program (2012BA107B02).