Miao Song
email: miaosong@shmtu.edu.cn

Qian Qian
email: qianqian_yn@126.com

Shinomori Keizo
email: shinomorikeizo@kochi-tech.ac.jp

The effect of Expression Geometry and Facial Identity on the Expression Aftereffect 1

Keywords: Facial Expression, Visual Adaptation, Neural Representation

Few studies have systematically examined the effect of expression geometry and facial identity on this cross-identity expression aftereffect. This issue is, however, critical for understanding the nature of expression adaptation. We measured expression aftereffects using a cross-identity/cross-expression geometry factorial design. The results show that expression aftereffect would reduce if adaptor and tests have different identities or expression geometries, and that identity-independent expression aftereffect is relatively more robust to variance in expression geometry in comparison to identity-dependent expression aftereffect. Based on these results, we discussed their psychophysical and physiological implication for understanding the nature of identity-dependent and identity-independent expression representations.

Introduction

Adaptation is a universal phenomenon in the human visual system, which refers to that the previous sensory experiences would affect the subsequent perception and response properties of visual system [START_REF] Mather | The motion aftereffect: A modern perspective[END_REF]. For instance, an observer adapts to a smiling face for a minute, and then looks at a middle expression within a series of ambiguous expression images morphing between smiling and angry expressions, this middle expression is tended to be perceived as an angry expression. This illusion is called expression aftereffect induced by the visual adaptation [START_REF] Webster | Adaptation to natural facial categories[END_REF]. As visual adaptation can isolate and/or temporarily reduce the contribution of specific neural populations, visual adaptation is a time-honored tool for researchers to investigate the neural representation of human sensory system. Previous study has found expression aftereffect in different person condition was weaker than that in same person condition, which indicated that there are two different neural representations in facial expression system, i.e., identity-dependent neural *Corresponding Author representation and identity-independent neural representation [START_REF] Fox | What is adapted in face adaptation? The neural representations of expression in the human visual system[END_REF]. However, two issues should be further considered. First, although variance in identity impaired expression aftereffect, when adapting faces and test faces are of different person, not only facial identity but also expression geometry is also changed. It raises a possibility that the reduction of expression aftereffect in different person condition is simply induced by the difference of expression geometry instead of the difference of face identity between adaptor and test faces. Second, as the similarity between the adaptor and the tests is an important factor to modulate the aftereffect in the low-level visual adaptation, it is necessary to examine whether the similarity of facial identities also affects the magnitude of expression aftereffect.

To clarify these two issues and systematically examine the effects of expression geometry and facial identity on the expression aftereffect, five experimental conditions were tested in current study. The interaction of facial identity and expression geometry were considered in 2X2 combinations to constitute four conditions (same or different: facial identity and expression geometry between adaptor and test face): same identity/same geometry, same identity/ different geometry, different identity/same geometry, different identity/ different geometry. In fifth condition, to examine the effect of identity similarity on the expression aftereffect, we used the computer-morphing technology to generate the artificial faces of different similarity, and tested on the same ambiguous images.

Method

There were five adapting conditions in the experiment, in which test faces were always the same among conditions, but the identity and/or the expression geometry of adaptor were manipulated. The five conditions are respectively termed as SI/SG, SI/DG, DI/SG, DI/DG, and DIDS, where the first two letters indicates whether adaptor and test face are of the same identity (SI: Same Identity) or not (DI: Different Identity), and last two letters indicates whether the expression geometry of adaptor are the same with that of test face (SG: Same Geometry) or not (DG: Different Geometry). The last DIDS refers to the condition in which the expression aftereffect is measured with the face adaptor of different similarity.

Subject

The subjects were 8 paid students with normal or corrected-to-normal vision (5 from Kochi University of Technology and 3 from Shanghai Maritime University, mean age: 20.2, SD=3.7). All 8 subjects participated SI/SG, SI/DG, DI/SG, DI/DG conditions, and 5 subjects from Kochi University of Technology participated the DIDS condition.

Stimuli and Apparatus

The face stimuli were selected from the affiliated image set of the Facial Action Coding System [START_REF] Ekman | Facial Action Coding System: The Manual on CD ROM. A Human Face[END_REF] and the Cohn-Kanade AU-Coded Facial Expression Database [START_REF] Kanade | Comprehensive database for facial expression analysis[END_REF], which are coded by Face Action Coding System and enables us to select same expression configuration for different photographic subjects. In SI/SG, SI/DG, DI/SG, and DI/DG conditions, happy, angry, surprised and disgusted expressions constituted two expression pairs, i.e., happy-angry and surpriseddisgusted expression pairs. Illustrated by the case of surprised-disgusted expression pair, we selected two female photographic subjects (F01 and F02) depicting two expression configurations (C01 and C02) of surprise and disgusted expressions, resulting in four combinations of identity and expression configuration (F01 with C01, F01 with C02, F02 with C01, and F02 and C02) (see Fig. 1a). The same method was used to select the adaptors of happy-angry expression pair, except that the two photographic subjects showing happy and angry expressions were male.

Using two expression images of the same photographic subject, we morphed a series of test faces using Abrosoft FantaMorph 5.0 for surprised-disgusted and happyangry expression pairs, respectively, with the nine middle ambiguous images, which varied from 30% to 70%, served as test stimuli.

The test faces in SI/SG, SI/DG, DI/SG, and DI/DG conditions were always the ambiguous expression images morphed between F01 with surprised expression (C01) and F01 with the disgusted expression (C01). We used the same test faces but different adaptors in the four experimental conditions. For instance, In the same identity/same configuration condition, the same images used to construct the test faces were used as the adaptors (i.e., F01 with surprised expression C01, and F01 with disgusted expression C01). In different identity/different configuration condition, the adaptors were the face images with different identities and expression configuration that differed from test faces (i.e., F02 with surprise expression C02, and F02 with disgusted expression C02).

In DIDS condition, we used computer-morphing technology to create the similar faces, because the morphing faces resembled two original faces when the features of two face are blended together. The similar faces were created by morphing the test face between F01 with G01 and F02 with G01 on smiling and angry expression pair using the Fanta morphing software. We created the nine similar faces with identity proportion of test face from zero to 100% in steps of 10% in terms of the scale of the morphing software, with zero refer to that the created adapting face is dissimilar to the test face, while one denote the adapting face is absolutely same with test face, we select those face with proportion equal with 30%, 50% serves as adapting face. The reason not to select the face images with similarity strength greater than 50% as adapting face, because these faces are too similar with test face so that subject may consider them as the same person rather than similar person.

Procedure

The experiment consisted of 10 blocks, each for one of eight experimental conditions (2 expression pair x 5 experimental conditions), performed in an order that was randomized across subjects. Each block includes 2 adapting images and each adapting image was presented 20 times in 9 test images, resulting in a total of 360 trials for each block. The trials for different test in a block were also randomized. The duration for one block is approximately 40 minutes. The subject participated one block every other day and finished all experiments within 20 days. Each subject was tested individually. They learned the experimental task through oral instruction and a short training session. In each trial, adaptor and test image were sequentially presented in the center of screen (Fig. 1b). After the presentation of test image, subjects perform a two-alternative forced choice (2-AFC) task to classify presented image into one of two categories (i.e., two expression images used to create the morphed series). The subject was instructed to attend to the face stimuli but no fixation point was given. This is to prevent subjects from overly attending to local facial features near fixation point. The duration of adapt stimuli and test stimuli were determined to 4000 and 200 ms, respectively, with 100ms noise mask between adaptation and test stage to minimize the possible apparent motion effects.

Result

All adapting conditions generated significant aftereffect (Fig. 2), confirming the expression aftereffect reported in the previous literatures [2][6]. A two-way analysis of variance (ANOVA) has been performed on SI/SG, SI/DG, DI/SG, and DI/DG conditions. There was a significant main effect for adapting condition (F(3,18)=10.19, P<0.001), indicating that the expression aftereffect were significantly different for four different adapting conditions. There was no significant main effect for expression pairs (F(1,36)=0.11, p=0.742) and no interaction between expression pair and adapting condition, thus, the data from different expression pair was merged for the further analysis.

The size of expression aftereffect in DI/SG condition was weak relative to that in SI/SG condition (t(17)=6.57, p<0.001), indicating that the reduction of expression aftereffect across identity is held even when the adaptor and test face has the same expression geometry. This result suggests that only variance in expression geometry cannot well explain the reduction of expression aftereffect, thus confirming the inter-ference from identity system to the expression system. On other hand, the size of expression aftereffect in SI/DG condition was weaker than that in SI/SG condition (t(17)=5.84, p<0.001). In contrast, the aftereffect size in DI/SG condition was approximately the same with that in DI/DG condition (t(17)=0.19, p=0.851). These are new findings suggesting that variance in expression geometry impairs the identity dependent expression aftereffect, but not the identity independent expression aftereffect.

For DIDS condition, the result reveals that identity similarity between the adapting images and test could significantly increase the expression aftereffect. The adapting effects by 30% and 50% similar faces are 10.4% (S.E.M. =2.3%) and 22.1% (S.E.M. = 3.9%), and the adapting effect by the 50% similar faces is significantly stronger than that by the 30% similar faces (t8 = 2.843, p < 0.02). It is worth noting that the adapting effect by the similar faces is still weaker than that in the SI/SG adapting condition (100% similar faces), but stronger than that in the DI/SG adapting condition (0% similar faces) (Fig. 2b). It suggests that the expression aftereffect would increase as the function of identity strength, thus confirming that the expression aftereffect is identity-dependent.

Discussion

We firstly found that the expression aftereffect is still much reduced when adaptors and test face have the identical expression geometry. Such finding is consistent with Ellamil et al.'s work [START_REF] Ellamil | Examinations of identity invariance in facial expression adaptation[END_REF], which used the artificial faces of anger and surprise expression as adaptors and found the reduction of adaptation effect when adaptor and test faces have same expression morphing prototype but different facial texture and contour. Together, our and Ellamil et al.'s observation suggests that the reduced aftereffect cannot be simply attributed to the dissimilarity of expression geometry between adaptor and test faces, it does reflect a functional interference from identity system on expression system. This observation consolidates Fox's proposal of the identity dependent neural representation. We next found that identity similarity between the a) b)

adapting images and tests could significantly increase the expression aftereffect, thus indicating that the expression aftereffect is identity-dependent in specific range of identity strength. Our data fits with the hypothesis of partially overlapping neural representations for the perception of expression and identity [START_REF] Baudouin | When the smile is a cue to familiarity[END_REF] [START_REF] Calder | Understanding the recognition of facial identity and facial expression[END_REF]. These two systems depend on the different facial component to extract the information and process the expression. As the structural reference hypothesis stated [START_REF] Ganel | Effects of familiarity on the perceptual integrality of the identity and expression of faces: The parallel route hypothesis revisited[END_REF], the face structure information is not only important in the identity discrimination, but also used by observers as a reference to compute and recognize expressions. The identity dependent expression system may more rely on the facial shape and/or structure information (e.g., local edge, facial contour) to perform the expression geometry analysis, while the identity independent expression system seems to depend on the abstract emotional information. It would be interesting for the future researches to locate the cortex of these two different neural representations.

Fig. 1 .

 1 Fig. 1. The adaptors of surprise-disgusted expression pair used in four conditions (a), and experimental procedure (b).

Fig. 2 .

 2 Fig. 2. The aftereffect sizes in SI/SG, SI/DG, DI/SG, DI/DG (a) and DIDS(b) conditions. Error bars denote SEM.

Acknowledgement

This work is supported by the National Natural Science Foundation of China under Grants No. 61403251, and Shanghai Municipal Natural Science Foundation under Grants No. 14ZR1419300.


Yongming Wang

W U Lin

A Functional Model of AIS Data Fusion

Keywords: maritime situation awareness, automatic identification system, AIS, data fusion, functional model, JDL model

In recent years, maritime situational awareness based on the fusion of AIS (Automatic Identification System) data has attracted more and more researchers. However, the diversity of terms and methodologies hinders the understanding and communication among them. Besides, AIS data was used mainly for traffic pattern discovery and abnormal vessel detection, more values await discovery. To overcome these two problems, this paper proposes a functional model of AIS data fusion. This model may provide a common frame of reference for discussions of AIS data fusion as well as a checklist for functions a maritime situational awareness system should provide. Based on this model, this paper introduces our works.

Introduction

The AIS1 is a self-reporting system installed on ships used to exchange kinematic (dynamic) and identity (static) information etc. with other nearby ships, base stations and satellites. The initial purpose of AIS is collision avoidance. It has interfaces for position (GNSS), heading (compass) and rate of turn (gyrocompass or ROT Indicator) sensors.

Other information like navigation status, Maritime Mobile Service Identity (MMSI) number, name of ship, type of ship, destination, Estimated Time of Arrival (ETA), etc. is required to input manually. Ships of 300 gross tons and upwards on international voyages, 500 tons and upwards for cargos not in international waters and passenger vessels are required to fit an AIS transceiver2. The time interval of receiving AIS messages from a ship is supposed to be no more than 3 minutes when within the range of shore-based stations (typically less than 60 miles) or on the order of hours otherwise.

Global AIS data collected from shore-based stations and satellites is now available on the Internet, through service providers (e.g., China Transport Telecommunications & Information Center (CTTIC)). However, as we will show in this paper, AIS data is rather dirty, full of noises and errors. As a main source of vessels' near real time information, AIS data has been studied by growing number of researchers for traffic pattern discovery356. In their research, vessel objects, waypoint objects and route objects are discovered and updated by fusing AIS data. Based on discovered patterns, abnormal vessels were detected by comparing vessels' behaviors with normal patterns9. For a more comprehensive maritime situational awareness, detailed types of entities should be identified, such as fishing areas4, ports, anchorages, etc.

Diverse terms and methodologies about AIS data fusion exist in literature, making the communication and reuse among the community difficult. For example, different terms were used to represent the assessment of traffic routes, such as "analysis of motion patterns"5, "learning for vessel trajectories" 6, "learning of maritime traffic patterns" 7, "extraction of knowledge"8, "traffic route extraction"3, "vessel pattern knowledge discovery"9, "traffic knowledge discovery"10, "vessel track information mining"11, etc. These terms stem from the field of machine learning and data mining, which aim at solving the problem of knowledge or pattern discovery. On the other hand, data fusion is about the assessment of entities of interest, including their states, relationships among them and impacts. Patterns discovered by machine learning or data mining is a source of information in data fusion, which is often referred to as "mod-els12". We study the problem of maritime situational awareness in the framework of data fusion rather than machine learning or data mining in this paper, and adopt those widely-used terms in fusion community.

To provide a common frame of reference as well as to explore more potential applications of AIS data, we propose a functional model of AIS data fusion in this paper. It describes what analysis functions or processes need to be performed13 in a maritime situational awareness system. Another category of model is process model (e.g., Boyd's Observe-Orient-Decide-Act model), which describes how analysis is accomplished13. A functional model is useful in system engineering by "providing visualization of a framework for partitioning and relating functions and serving as a checklist for functions a system should provide"12.

The JDL (Joint Directors of Laboratories) data fusion model15 is a well-accepted functional model in fusion community. Our model is an instantiation of its revised ver-sion14, and it focuses on Level 0 to Level 3. Based on this model, this paper introduces our work.

The remainder of the paper is organized as follows. Section 2 provides a detailed description of the proposed functional model. Section 3 summaries our existing works under the functional model. Section 4 draws the conclusion and future work. The revised version is more general and "provides a clear and useful partitioning while adhering as much as possible to current usage across the data fusion community14". Our model is entity-oriented and entities can be vessels, traffic route segments, traffic stops, fishing areas, anchorages, etc. These entities' aggregates or relations can also be regarded as an entity. The "products of processes at each level are estimates of some existing or predicted aspects of reality15", and they are stored in database.

Functional Model of AIS Data Fusion

To highlight the data fusion functions in this figure, we put data sources, support databases and fusion databases into the data management system together. According to CTTIC16, it is gathering about 10 billion records (around 1 TB) each year. So the data management system should be able to handle AIS data on the order of TB.

As level 4 (process assessment) and higher processes (user refinement) are not specifically fusion functions, they are not discussed in this paper.

2.1

Level 0: Preprocessing AIS data is prone to be highly erroneous and noisy due to the deficiencies of the system and improper use of it. Although this problem was revealed1718, limited work19-20 has been done on the preprocessing of AIS data.

As AIS was designed for collision avoidance, many issues may arise when applied for real-time surveillance. For example, an AIS message only contains the seconds part (6 bits) of the time that it was broadcast and it is up to receivers to affix the full time stamp. A comparison was made between the seconds part of the original time stamp and affixed one, and significant differences were found17. Besides, as clocks are not synchronized, a ship will jump on the map when its AIS messages are received from different receivers20.

In addition to flaws in the design of AIS, improper use of it makes the situation even worse. For instance, the MMSI of each ship is supposed to be unique, and it's commonly used9 to identify and track vessels. But it's not rare that a MMSI is shared by different vessels19, as this field is input manually and not reliable. To identify each vessel uniquely, we need an internal ID. For each AIS message, its association with an existed or new ship must be determined first. This problem can be solved by tracking vessels in real-time or off-line41910.

Level 1: Entity Assessment

In maritime situational awareness systems, entities of interest are mainly vessels21, ports527, fishing areas4, traffic route segments7810, turning points and junctions78, etc. When the area of interest is bounded, entry and exit points10 are also important entities.

The state vector of a vessel includes position, speed, direction, etc. Sequential states of a vessel constitute its tracks, which can be partitioned into moving segments and stops. These segments and stops can be further classified into traffic route segments, traffic stops, fishing areas, ports, anchorages, etc. The detection and assessment of traffic route segments is a basic problem in Level 1391022.

Level 2: Relationship Assessment

Level 2 process is usually referred to as "situation refinement15" or "situation assess-ment14". It has been pointed out that situations can be represented as sets of relations14, so we refer to Level 2 process as relationship assessment in this paper for clarity. Relations of interest for maritime situational awareness include but are not limited to:

 vessels VS vessels Maritime authorities are deeply concerned about the relationship among vessels31, because illegal activities and incidents can be detected by relation assessment. For example, we can discover smuggles by identifying vessels' rendezvous at sea. And encounter of vessels may indicate critical incidents like collision, piracy or arrestment, etc.

 vessels VS route segments

Original AIS data is made up of point sets. After traffic route segments have been extracted in level 1 process, we can represent the track of each vessel as series of segments. This can remove redundancy in raw AIS data and compress it by about two orders. It was modeled as a classification problem in previous works9, and solved by maximizing the posterior probability that a vessel is sailing over a certain route.

As discovered routes are characterized by spatial, temporal and other attribute-related features (e.g., type and size) of vessels sailing on them39, and features of vessels include their historical route patterns, we can detect anomalies when a vessel's attributes are not compatible with the route segment, or when it is not compatible with its historical patterns.

 ports VS ports

There is limited work on analyzing the relationship between ports. An example was identifying patterns of ships' transition among ports by AIS data mining using the software R23. Based on discovered patterns, they estimated destination ports.

 route segments VS route segments

Extracted route segments are separate lines22. We need to know their relations of con-nection8, in order to form the global route map.

Level 3: Impact Assessment

Level 3 process combines products of level 1 and level 2 to assess impacts. Functions provided at this level include predicting vessels' positions, assessing the intention and threat of vessels and detecting anomalies.

As time intervals between messages of the same ship varies from seconds to hours17 due to limited coverage and bandwidth of receivers, it is necessary to predict positions for situational awareness 171995.

Anomaly detection and risk assessment are usually based on the assessment of relationship among entities. Most existing literature only focused on detecting anomalous ships891124-27.

3

Our Existing Works

Level 0: Preprocessing

We have performed association on global AIS data of 34 months (from August 2012 to May 2015) based on spatial and temporal proximity28. When two records with the same MMSI were more than 100 kilometers away and the speed reckoned was more than 120 knots, then they were regarded as from different ships28. The results of pre-processing showed that, among 1,998,200 distinct MMSIs recorded, 1,607,664 were suspicious: the average number of AIS messages broadcast by each of them was only 11.3. The remaining 390,536 MMSIs were used by 491,346 vessels, 43,034 messages had been received from one vessel on average.

Level 1: Entity Assessment

We proposed a vessel trajectory partitioning method based on hierarchical fusion of position data reported by AIS, aiming at improving preciseness and processing speed29. Our method consists of two steps: position point fusion and sub-trajectory fusion. The key idea of our method is to describe trajectories by hierarchical concepts.

In the first step, trajectories are partitioned by grouping positions in raw AIS records into sub-trajectories represented as straight line segments. In the second step, we aggregate successive sub-trajectories into more abstract concepts: route segments and stops. We applied our method on a data set containing 473963 AIS records from 10 vehicle carriers along the coast of China. Algorithms were implemented by python 2.7.3 on a notebook with Intel Core2 Duo CPU T6600 @2.20GHz, 2GB RAM. The average execution time of our two level partitioning on each vessel is around 2.30 seconds and 0.22 seconds respectively. After partitioning, we got 250 route segments and 16 stops. Experimental results showed that our method split routes and identified stops precisely at the computational complexity of O(n).

To describe shipping density of each area, we defined vessel and traffic density28. The definition of vessel density in a region was taken as the expected number of vessels per unit area at any time, and traffic density as the average number of vessels crossing this region per unit area per unit time. We calculated vessel and traffic density using a grid-based method. The traffic density in 2014 is shown in Fig. 2.

Level 2: Relationship Assessment

We are developing an online system detecting vessels' rendezvous. In the system, we divide the earth into grids of the size 1° latitude by 1° longitude, and put ships into them according to their locations. Rendezvous detection is performed every 10 minutes by calculating distance among ships inside the same grid and surrounding grids.

We have mined ships' mooring positions base on AIS data, including position, speed and status. Each position was represented as a grid of 0.6 second longitude by 0.6 second latitude, and the membership that each grid belonged to a berth was calculated separately according to tracks located in this grid using fuzzy inference. After that, we clustered mooring positions into berths using DBSCAN, taking multiple attributes of positions into consideration in the function of distance. An example of a clustered berth is demonstrated in Fig. 3.

Level 3: Impact Assessment

Based on vessel and traffic density maps described in section 3.1, we are developing an online system detecting two types of anomalies: areas with vessel number deviating from expected and ships sailing in unusual routes. The former anomaly is detected by comparing current number of vessels with patterns in vessel density maps. The latter anomaly is detected by comparing each vessel's route with traffic density maps.

Conclusion and future work

We proposed a functional model of AIS data fusion in this paper. We hope that this model can provide a common frame of reference for maritime situational awareness based on AIS data fusion. This model is an instantiation of the well-accepted JDL model and serves as a checklist for functions a maritime situational awareness system should provide. Based on this model, this paper introduced our works.

Our future work would be to achieve more comprehensive global maritime situational awareness based on AIS data fusion. In the entity assessment step, we will detect and estimate more kinds of entities. Previous work mainly focused on stops and routes. To achieve global maritime situational awareness, we would consider more types of entities: vessels, traffic route segments, traffic stops, fishing areas, anchorages and ports, etc. With those entities, we can assess more interesting kinds of relations in level 2 process, such as fleet identification, which has not been studied yet as far as we know. Although anomaly detection is a hot topic in level 3 process, most efforts were made on identifying ships whose tracks are not compatible with traffic patterns. In our future work, those ships deviating from their historical patterns will also be marked as anomaly. Taking more types of entities into consideration, we plan to detect more kinds of anomaly based on the analysis of their states and relations among them. Possible anomalies include birth and death of fishing areas and anchorages, changes of traffic route segments, etc. Apart from anomaly detection, we will also perform other level 3 processes such as route planning, trade monitoring, piracy detection, etc.

Fig. 1 .

 1 Fig. 1. Proposed functional model of AIS data fusion, which focuses on Level 0 to Level 3. AIS data as well as prod-ucts of fusion is managed by the data management system on the left of this figure. AIS data

Fig. 2 .

 2 Fig. 2. Global traffic density in 2014, at the spatial resolution of 10 minutes longitude by 10 minutes latitude

Fig. 3 .

 3 Fig. 3. A clusters of mooring points, belonging to a berth


Wu Zheng-Tao

Application of Ant Colony Optimization in Cloud Computing Load Balancing

Keywords: cloud computing, load balance, Ant Colony Optimization

In cloud computing, because all the tasks are submitted and requested by the users, it is the key to improve the quality of service whether all tasks are scheduled to every host to achieve the shortest completion time for all tasks. In this paper, the basic ant colony algorithm is modified appropriately to enable task scheduling and load balancing. The simulation results show that the ant colony algorithm can effectively obtain the global optimal solution and realize the shortest task completion time.

Introduction

Cloud computing is a new business and computing model which is proposed in recent years. Thousands of hosts are linked together over the network to form an ultra large pool of resources. Google corporation is the first to use cloud computing service, its scale has reached millions of machines, and other companies such as Microsoft, Amazon, Aliyun and so on, also reached the scale of several hundred thousand units [START_REF] Peng | Cloud computing[END_REF]. Depending on the type of service, cloud computing is divided into three layers, namely, IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). No matter which layer, there is the need to balance the task of scheduling to the host, that is, load balancing. However, due to the large number of computers in the cloud computing platform, the complexity of the structure and the difference of the allocation of resources, it is difficult to achieve load balancing for such large-scale, heterogeneous and non-centralized task scheduling. The unbalanced load will lead to the decrease of system efficiency and throughput, and the instability of the daily operation, which will seriously affect the quality of service of cloud computing [START_REF] Fenghua | Research on load balancing scheduling algorithm of cloud computing resource based on ant colony algorithm[END_REF].

Ant Colony Optimization (ACO) is a kind of bionic intelligent algorithm to find the optimal solution, simulating the foraging behavior of ants. The wisdom of a single ant is not high, but it achieves an optimal population effect by sharing information among groups. Ant colony algorithm is a kind of positive feedback algorithm, which has good robustness and adaptability, and is easy to combine with other algorithms [START_REF] Ning | Research on task scheduling of cloud computing based on adaptive ant colony algorithm[END_REF]. Therefore, ant colony algorithm can be used in load balancing. The ant colony system was firstly proposed by Italy scholars Dorigo, Maniezzo and others in 1990s. When they studied ants foraging, they found that the behavior of a single ant was relatively simple, but the whole ant colony could show some intelligent behavior. For example, an ant colony can find the shortest path to a food source in different environments. This is because ants in the ant colony can transfer information through some kind of information mechanism. Further studies have shown that ants release a substance called "pheromones" on their path.

The ants in the colony can apperceive pheromones, they will follow the path with high concentration pheromone, and every ant will leave the pheromones on the roa d, which forms a kind of positive feekback mechanism. Thus, after a period of time, the whole ant colony will arrive at the food source along the shortest path.

The basic idea of applying ant colony algorithm to solve the optimization problem is to use the path of the ant to represent the feasible solution of the problem to be optimized. All the paths of the whole ant colony form the solution space of the optimization problem. The shorter path ants release more pheromones, and as time goes on, the pheromone concentration on the shorter path increases gradually, and the number of ants in the path is increasing. Finally, the whole ant will concentrate on the best path under the positive feedback. At this time, the corresponding solution is the optimal solution of the problem to be optimized.

Researchers based on the study of ACO use traveling salesman problem as a test of performance, not others. In this paper, the basic ACO is modified accordingly, using cloudsim platform as simulation experiment, and design, modify the corresponding parameters, in order to design a load balancing algorithm.

Mathematic Model of Task scheduled

ACO is a new bionic intelligent algorithm. The first ant guides and influences the selection of the following ants by means of pheromone, so as to achieve the goal of cooperation among ants. The application of ant colony algorithm in the task scheduling of cloud computing is studied in this paper.

In a data center of cloud computing, there are n virtual hosts, they are set as 𝑉𝑚 1 , 𝑉𝑚 2 , 𝑉𝑚 3 , ⋯ , 𝑉𝑚 𝑛 ,Each virtual host has a different MIPS (millions of instructions per second). At the same time, there are m tasks that need to be scheduled to work on the n virtual hosts. The total amount of time spent in these tasks is:

T 𝑡𝑜𝑡𝑎𝑙 = max(𝑇 1 , 𝑇 2 , ⋯ , 𝑇 𝑛 ) (1) Where T 𝑖 indicates that the i virtual host completes the time allocated to all of its tasks, calculated by formula (2) and formula [START_REF] Ning | Research on task scheduling of cloud computing based on adaptive ant colony algorithm[END_REF].

t 𝑗 = 𝑀𝐼 𝑗 𝑀𝐼𝑃𝑆 (2)
T 𝑖 = ∑ 𝑡 𝑗 𝑘 𝑗=1 (3)
In the above two formulas, suppose that the performance of platform I virtual host is MIPS, and there are k tasks allocated on it, the number of instructions corresponding to each task is 𝑀𝐼 𝑗 (Million Instructions), so the time for each task is t 𝑗 .

Ant Colony Optimization

Theory of Ant Colony Optimization

After finding the food, the ants will randomly explore the way from home to food, and leave on the road with pheromones that are inversely proportional to the length of the journey. Subsequent ants will be based on the concentration of pheromones, select the nearest road, so as to achieve the group to find the best path of the results. If the shortest path is interrupted, the ants will randomly explore and converge quickly to the new shortest path.

The basic ACO in the TSP (traveling salesman problem) on the state as follows: at time 0, all the ants and n city will initialize m ants randomly in a city as a starting point, each edge between the city has an initial pheromone concentration 𝜏 𝑖𝑗 (0),Each ant places its city as the first element of the tabu list. Each ant then selects the next city to move, based on the probability function of the two parameter(𝛼,𝛽) (formula ( 6)), moving from the city I to the city J, and fill out the corresponding tabu list. After a cycle of N times, all ants go through all the cities and fill out the tabu list. After that, calculate the path length of each ant 𝐿 𝑘 ,and then update corresponding pheromone ∆𝜏 𝑖𝑗 𝑘 according the formula (4)。Save the shortest path that ants walk through, and get the shortest path 𝑚𝑖𝑛𝐿 𝑘 after one traversal,And empty all the tabu tables and start the next traversal loop. Repeat the process until the number of iterations reaches the required number of iterations(𝑁 𝐶 ),get the shortest path 𝑚𝑖𝑛𝑁 𝐶 in the global situation。 𝜏 𝑖𝑗 (t + 1) = ρ𝜏 𝑖𝑗 (t) + ∑ ∆𝜏 𝑖𝑗 𝑘 [START_REF] Kong Lingjun | Basic ant colony algorithm and its improvement [J][END_REF] where 𝜏 𝑖𝑗 (t + 1) is the updated pheromone concentration, ρ is the rate of pheromone emission, ∑ ∆𝜏 𝑖𝑗 𝑘 is the sum of pheromones left by all ants.

∆𝜏 𝑖𝑗 𝑘 = { 𝑄 𝐿 𝑘 𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝑡ℎ𝑒 𝑟𝑜𝑎𝑑 0 𝑛𝑜𝑡 𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝑡ℎ𝑒 𝑟𝑜𝑎𝑑 ( 5
)
where Q is a constant,𝐿 𝑘 is the path length of ants.

The probability function of the first k ant moving from city I to city J is:

𝑝 𝑖𝑗 𝑘 = { (𝜏 𝑖𝑗 ) 𝛼 (𝜂 𝑖𝑗 ) 𝛽 ∑ (𝜏 𝑖𝑗 ) 𝛼 (𝜂 𝑖𝑗 ) 𝛽 𝑘 , 𝑗 ∈ 𝐽(𝑖) 0 , 𝑜𝑡ℎ𝑒𝑟 (6)
where 𝐽(𝑖) means In the tabu list of K, the J city has not passed yet. In summary, fig. 1 shows the flow chart using ACO to solve the TSP problem.

Realization of Ant Colony Optimization

In the solution of the shortest time, m ants were placed in the initial position, according to the rules of a certain probability choice,every ant can arrange Vm for next task, until all tasks completed, and then pheromone increment table will be updated based on the total time. After all ants have finished their work, the best solution is selected from all candidate allocation and the historical optimal solution, and it is recorded, then the homologous pheromone table is updated. Then the pheromone is corrected, simulating the ants releasing pheromone on the optimal path and the natural volatilization of pheromone, leading the ant to find the global optimal solution. Fig. 2 shows the flow chart of the algorithm implementation. (1) calculation rules of pheromone increment table After each walk, each ant leaves a certain amount of pheromones according to the length of the path. The more ants there are in the same path, the more pheromones they leave behind at the same time. The pheromone increment table records the size of the pheromone emitted by the ants after each cycle. In this paper, the pheromone increment with ∆ 𝑖𝑗 𝑘 , ∆ 𝑖𝑗 𝑘 calculation rules such as formula [START_REF] Qinghua | An Abstract of Ant Colony Optimization[END_REF] for:

∆ 𝑖𝑗 𝑘 = { 𝑄 𝐿 , 𝑗 ∈ 𝑉𝑚 𝑖 0, 𝑗 ∉ 𝑉𝑚 𝑖 (7)
In the formula, the Q is expressed as a constant, and L is the total time after which the current ant is assigned the task. Each ant will leave pheromones on its path, and in the end, the global pheromone increment is the sum of the pheromone left by all the ants.

(2) calculation rule of task transfer probability When the ant chooses the virtual host for the next task, it is determined by the pheromone left before it and a heuristic information. The probability formula for assigning the task J to the virtual host I is shown in formula [START_REF] Xiajun | Cloudsim, cloud computing simulation tools[END_REF], where 𝜏 𝑖𝑗 is pheromone concentration, 𝜂 𝑖𝑗 is the heuristic information. The formula of 𝜂 𝑖𝑗 is given by formula ( 8) and (9):

𝜂 𝑖𝑗 = 1 𝐷 𝑖𝑗 (8) 𝐷 𝑖𝑗 = 𝑇𝑎𝑠𝑘 𝑖 𝑀𝐼𝑃𝑆 𝑖 + 𝐿 𝑗 𝐵𝑊 𝑖 (9)
Where 𝑇𝑎𝑠𝑘 𝑖 represents the total number of tasks allocated on table I Vm, 𝑀𝐼𝑃𝑆 𝑖 is the execution speed of the CPU for this Vm, 𝐿 𝑗 is the length of the task to be assigned, and 𝐵𝑊 𝑖 is the interface bandwidth of this Vm, which is a constant in this paper. α, β are control parameters that control the relative importance of pheromone and heuristic information.

(3) Amendment rules for pheromone tables When all ants completing one iteration and getting an optimal solution, pheromone table is needed to have a comprehensive revision according to the pheromone emitted by ants and the phenomenon that simulates the natural evaporation of residual pheromones, the correction rule is such as formula (10):

𝜏 𝑖𝑗 (𝑡 + 1) = (1 -𝜌)𝜏 𝑖𝑗 (𝑡) + ∑ ∆ 𝑖𝑗 𝑘 (10)
Where 𝜏 𝑖𝑗 (𝑡 + 1) is the pheromone table after correction,𝜏 𝑖𝑗 (𝑡) is pheromone table before correction,𝜌 is the control parameter, indicates the degree of evaporation of pheromones,∑ ∆ 𝑖𝑗 𝑘 is the sum of pheromone increments left by all ants. The purpose of pheromone correction is to assign as many pheromones as possible to the optimal allocation mode, and better guide the selection of ants. The rule will not only store the pheromones left by the ants, but will also evaporate them properly.

Algorithm parameter design

According to the above principle and various rules, this paper has designed and stipulated a series of parameters. In the formula [START_REF] Qinghua | An Abstract of Ant Colony Optimization[END_REF], the value of the constant Q will affect the left ant pheromone on global pheromone influence, it will weaken the influence of a better path on later ants if Q is too small,otherwise it will faster convergence which can easily fall into local optimal solution. According to the task length and the MIPS value of Vm, repeated experiments were conducted, and finally 100 was selected. In the formula ( 6) and (10), α,β,ρ are control parameter, where α,β respectively indicate the importance of pheromone and heuristic information, and ρ is the volatilization rate of pheromones. Studies have shone that when α is 1, the resulting optimum is better than the other. When value of β is from 1 to 5, the optimal solution quality will gradually become better, and when β is more then 5, the quality of the solution can be reduced, therefore, 5 is assigned to β. Ρ value changes between 0.3~0.9, the solution changed little, when its value is 0.5, the quality of solution is the best, ρ is suggested to value of 0.5 [START_REF] Zhiwei | The parameters alpha, beta in the ant colony algorithm, with an example of TSP problem[END_REF]. In formula (9), MIPS is randomly chosen in 100~300, and BW is the interface bandwidth. In this paper, the default value of simulation software is taken. The task length of all tasks is also random, with a range of 10000~50000.

Simulation and results

This paper uses cloudsim3.0 simulation platform to do simulation experiments. By inheriting the DatacenterBroker class, we extend the DatacenterBrokerACO class to ACO, and compile the Ant class and ACO class. We can regulate single ant activity and ant colony activity separately.

Introduction of cloudsim

Cloudsim is a cloud computing simulation platform developed by Melbourne University in Australia on the basis of Gridsim. CloudSim has several versions, and has now grown to version 3 of CloudSim. CloudSim 3 uses a layered architecture. Simulation layer provide a support for the configuration and simulation of virtual data center environment simulation for cloud computing, including virtual machines, memory, capacity, and bandwidth interface. This layer is used to study strategy of virtual machine to the host distribution, and the implementation of virtual machine scheduling function expansion core. The top layer is the user code layer, which provides basic entities such as hosts, applications, virtual machines, user data and application types, and agent scheduling policies. CloudSim 3 provides virtualization engines that build and manage multiple virtualized tasks on data center nodes and flexibly switch between time sharing and spatial sharing policies when virtualized services are allocated. Cloudsim 3 is open source, users can expand the open source code by expanding the interface, realizing their own scheduling strategy, and test and verify the corresponding function based on specific environment and configuration.

Result of Simulation

Before the simulation starts, the paper specifies some parameters in the ant colony algorithm, as shown in table 1:

Conclusion

In this paper, a method of load balancing for Cloud Computing Based on ant colony algorithm is proposed, and simulation experiments are carried out. The method guarantees that all virtual hosts can receive task assignments as evenly as possible, and that the total time of task completion is as short as possible. It can be seen that ACO has the following characteristics in load balancing:

As the ants continue to spread pheromones to the environment, new information will soon be updated to the environment, with dynamic characteristics.

Each ant makes its own choice according to the environment, and also sends out its pheromones. Therefore, ACO has distributed features.

Ants share environmental information and influence environment at the same time, so ACO has the characteristics of cooperative operation.

These characteristics are consistent with the super large scale, heterogeneous and real-time characteristics of cloud computing, so ACO is important for load balancing. Although ACO can solve the problem of load balancing, but also has the following problems need further study:

Load balancing is a multi-dimensional and multi constrained QoS problem. In this paper, the shortest time problem in load balancing is discussed, as well as the minimum transfer and the economic principle.

ACO needs to initialize some parameters, and the choice of parameters has an important influence on the performance and the final results. Therefore, in the cloud computing environment, the setting of various parameters needs further study.

The search time of ACO is too long, and the convergence speed is slow. How to combine ACO with neural network and particle swarm algorithm to improve the performance and efficiency of the algorithm in order to meet the requirements of response time and response speed of cloud computing.

initializeFig. 2

 2 Fig.2 the flow chart of the algorithm implementation

Table . 1. parameters of ACO

 . In order to ensure the heterogeneity of all virtual hosts, Java is used to randomly specify 20 different MIPS hosts. The range of MIPS is 100~300, and the value of MIPS is shown in table 2:At the same time, 100 tasks with different task lengths are generated randomly, and the length is 10000~50000. In order to determine the optimal solution, the number of ants is taken as half of the number of virtual hosts, 1000 iterations, and the final total running time is 813.1402877697841. Task assignment is shown in table3:

	VmId Mips VmId 18	6 275 31 11 23	34 65	7 202 57 12 96	8 278 80 13 76	81 2	9 109 14 60	48	10 195 15
	Mips VmId 19	208 16 25	69	294 17 79	5	196 18	41	143 19	140 20
	Mips	209		292		194		212	255
					Table.3. task allocation
	VMID TASK ID
	0	27	73		97	50	16	15
	1	72	0		56	58	68	94	42	14
	2	9	84		43
	3	3	74		46	54	13	17
	4	11	49		86	24
	5	90	88		98	93	47	8
	6	7	71		62	30
	7	63	59		22	37	55	95	87
	8	38	70		77
	9	51	89		75	10
	Q 100 10 11	52 85	36 1.0 44	𝛂 91 53	18 5.0 92	𝛃 45	12	0.5 19	𝛒
	12	33	61		99
	13	29	39		83	35
	14	32	78		4
	15	28	66	Table.2.the MIPS of 20 Vm 1 26 20
	VmId 16	1 6	21	2	64	3 67	40	4 82	5
	Mips	228		299		108		204	152


Di Zang
email: zangdi@tongji.edu.cn

Jiawei Ling

Jiujun Cheng

Keshuang Tang

Xin Li

Using Convolutional Neural Network with Asymmetrical Kernels to Predict Speed of Elevated Highway

Keywords: convolutional neural network, kernels, intelligent transportation system, speed prediction

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan'an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them.

Introduction

It is an attractive topic for human beings to have the ability to foresee the future, and it is the same in transportation management. It is of great importance for traffic management department to learn the traffic evolution to provide a guide of tomorrow's traffic for people to select an unobstructed route. It is also of value for traffic management department to adjust the traffic strategy in advance [START_REF] Zhang | Data-driven intelligent transportation systems: A survey[END_REF][START_REF] Park | In Real time vehicle speed prediction using a neural network traffic model[END_REF]. However, it is challenging to define a high-performance prediction model, because the utilization of spatiotemporal relationship was not high, we did not have the ability to form a more efficient prediction model to deal with the spatiotemporal correlation of traffic flow in roads expanding on a two-dimensional field, we were not able to forecast long-term future. Conventional traffic data prediction models usually treat the traffic data as sequential data, so these models usually cannot have a good performance, because of the limitations in implementation, assumptions and hypotheses, noisy or missing data, ineptness to deal with outliers and incapability to determine dimensions [START_REF] Karlaftis | Statistical methods versus neural networks in transportation research: Differences, similarities and some insights[END_REF].

In the existing models, there are two main research methods which dominate the study in traffic forecasting: methods based on statistic and methods based on neural networks [START_REF] Karlaftis | Statistical methods versus neural networks in transportation research: Differences, similarities and some insights[END_REF].

In traffic prediction, statistical methods are widely used. The classic method is autoregressive integrated moving average (ARIMA) model. It is a time-series prediction model which considers the correlations in successive time sequences of traffic variables. Seasonal ARIMA model [START_REF] Tran | A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction[END_REF], KARIMA model [START_REF] Voort | Combining kohonen maps with arima time series models to forecast traffic flow[END_REF] and ARIMAX model [START_REF] Williams | Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling[END_REF] which are the extensions of basic ARIMA model are widely researched and applied. In [START_REF] Davis | Nonparametric Regression and Short-Term Freeway Traffic Forecasting[END_REF], knearest neighbors (KNN) has been used to forecast traffic flow. In [START_REF] Wu | Travel-time prediction with support vector regression[END_REF], support vector machines (SVM) were employed in traffic prediction. Online-SVM and Seasonal SVM were used in [START_REF] Castro-Neto | Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions[END_REF] and [START_REF] Hong | Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm[END_REF] to improve the prediction accuracy. Methods based on statistics have been widely applied in traffic prediction because of their easy implementation and promising results. However, these models did not consider the significant spatiotemporal feature of traffic data, so these models cannot achieve a higher accuracy than models based on neural networks. Besides, some statistical methods are powerless because the model takes a very long time and consumes copious computer memory when it deals with big data.

Neural network based methods, such as artificial neural network(ANN) are usually applied to solve traffic prediction problems. ANN is able to deal with multi-dimensional traffic data. Because of its easy and flexible implementation, strong generalization ability and high performance in traffic prediction, ANN model is favored in recent research in traffic prediction. In [START_REF] Asif | Spatiotemporal patterns in large-scale traffic speed prediction[END_REF], ANN was used to predict traffic speed with consideration of weather conditions. In [START_REF] Huang | An Application of Neural Network on Traffic Speed Prediction Under Adverse Weather Condition[END_REF], a real-time traffic speed prediction algorithm based on ANN was proposed by Park et al. A model based on ANN combined with conventional Bayes theorem to predict short-term freeway traffic flow was proposed in [START_REF] Zheng | Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach[END_REF]. Moretti et al. [START_REF] Moretti | Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling[END_REF] used statistical and ANN bagging ensemble model to predict city traffic flow.

ANN can make use of large amounts of data, but it cannot take advantages of spatiotemporal correlations from large amounts of traffic data. ANN are not able to achieve a better performance than methods based on deep learning. Recently, more and more deep learning models are applied to predict traffic flow because deep learning models are able to learn the deeper level features from the given data. Nowadays, Deep Belief Networks (DBNs) are widely used in traffic volume prediction. The model [START_REF] Huang | Deep Architecture for Traffic Flow Prediction: Deep Belief Networks with Multitask Learning[END_REF] used the method of heterogeneous multitask learning and K-means clustering to improve the prediction accuracy. Ridha et al. [START_REF] Soua | Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory[END_REF] combined DBN with weather condition to predict traffic flow using streams of data. Ma et al. [START_REF] Ma | Large-scale transportation network congestion evolution prediction using deep learning theory[END_REF] proposed a new model combined Restricted Boltzmann Machine (RBM) and Recurrent Neural Network (RNN) forming a RBM-RNN model, it achieves the advantages of both RNN and RBM. In [START_REF] Lv | Traffic Flow Prediction with Big Data: A Deep Learning Approach[END_REF], a Stacked Autoencoder based model was proposed to forecast traffic flow. Based on [START_REF] Lv | Traffic Flow Prediction with Big Data: A Deep Learning Approach[END_REF], Duan et al. [START_REF] Duan | A deep learning based approach for traffic data imputation[END_REF] further improved the SAE model by choosing different appropriate hyperparameters at different times. Tian et al. [START_REF] Tian | Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network[END_REF] first introduce Long Short-term Memory (LSTM) into traffic prediction. The LSTM model outperforms other neural networks in both stability and accuracy. In [START_REF] Zhang | Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction[END_REF], a deep spatio-temporal residual network was applied in crowd flows prediction. Ma et al. [START_REF] Ma | Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[END_REF] proposed a Convolutional Neural Network (CNN) based model which learns traffic data as image, the model achieves a good result in Beijing road network speed prediction. However, the CNN model in [START_REF] Ma | Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[END_REF] treats the traffic dynamic in time series and space equally, and cannot work with the whole-day traffic data.

To solve the problems in [START_REF] Ma | Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[END_REF], this paper introduces asymmetrical kernels to CNN, which can treat the spatial features and temporal features of traffic data differently. Because of different treatment between spatial features and temporal features, our model gets lower mean squared error (MSE) and mean related error (MRE) than common CNN. In addition, the improved model is applied to predict the whole-day traffic speed of the next day with the help of whole-day traffic speed data of the previous day.

Proposed Approach

In this section, we will introduce the method of transforming the loop detectors' data to matrix and the basic theory of our CNN model.

Loop Detector Data Transformation

The traffic speed of elevated highway can be provided by the loop detectors deployed on the highway. In the time dimension, the loop detectors' data range from 0:00 am to 12:00pm. The time intervals are usually 5 minutes. On the elevated highway, each two loop detectors are deployed between 400 meters. The loop detector data of elevated highway also can be converted to matrices by a similar method. We let x-axes represents time, and y-axes represents space. We arrange the loop detector data of elevated highway in the order of loop detectors' position and time series to form a 2D matrix. Each row in the matrix denotes speed data in different time periods recorded from a same loop detector in the elevated highway. Each column in the matrix denotes speed data from different loop detectors at a same time period. The time-space traffic speed matrix can be represented as follow:

𝑆 = [ 𝑠 11 𝑠 12 𝑠 21 𝑠 22 ⋯ 𝑠 1𝑛 𝑠 2𝑛 ⋮ ⋱ ⋮ 𝑠 𝑚1 𝑠 𝑚2 ⋯ 𝑠 𝑚𝑛 ] (1)
In the matrix, m denotes the amount of loop detectors, n denotes the length of time intervals and s ij denotes the average speed on the loop detector i at time period j. We also can represent the matrix as a heat map. Fig. 1 is the illustration of a heat map transformed from the matrix.

2.2

The architecture of the improved CNN CNN has been widely used in the research of image understanding, because of its strong ability in extracting critical features from images. In the field of image classification, CNN performs better than other deep learning models, even surpasses human beings.

As shown in Fig. 2, the Input of our model are matrices of spatiotemporal traffic data. In our model, there are 3 convolution layers and 2 pooling layers. The input matrix goes through two convolution layers, one pooling layer, one convolution layers, one pooling layer and one fully-connected layer in turn. The output of the model is a vector which can be reshaped to a matrix with the same size of the input matrix.

The model's input and output

Like common CNNs, our improved CNN accepts matrices (images) as input. However, in this paper, instead of being used to solve classification problems, we use CNN to finish regressive task. Thus, the output of our model is a vector that can be reshaped to a matrix just the same size as the input matrix which is the prediction of the next day.

Convolution layers

The previous layer's feature maps or the input matrices are convolved with trainable kernels and then the feature maps are put through the activation function to make up the output feature maps. Each output feature map combines convolutions with multiple input feature maps. In common, the relationship between input and output maps of convolution layers is as follow:

𝑥 𝑗 𝑙 = 𝑓(∑ 𝑥 𝑖 𝑙-1 * 𝑘 𝑖𝑗 𝑙 + 𝑏 𝑗 𝑙 ) (2)
Where x l-1 denotes the input of the convolution layer, k l means the convolution layer's kernels, b denotes an additive bias, f is the activation function. We usually use sigmoid (3) function or ReLu (4) function as the activation function.

𝑓(𝑥) = 1 1 + 𝑒 -𝑥 (3)
𝑓(𝑥) = { 0, 𝑥 ≤ 0 𝑥, 𝑥 > 0 (4)

Asymmetrical Kernels.

In common CNN models, the convolution layers' kernels are square, however, in our model, kernels in convolution layers are asymmetrical rectangle matrices, because we consider spatial dynamics and temporal dynamics differently. A traffic congestion event can impact for a long time, sometimes for several hours, while the time intervals in matrices are too small. To solve the problem demonstrated before, we use asymmetrical rectangle kernels, which can capture more temporal dynamics of traffic data. In the models, we use asymmetrical rectangle kernels with 3×13 size, 3×5 size and 3×5 size in different convolution layers.

Pooling layers.

The output of a pooling layer are down-sampled versions of input maps. If there are N input maps, then the number of output maps will be exactly N, but the size of output maps will be smaller. More formally, 𝑥 𝑗 𝑙 = 𝑓(∑ 𝛽 𝑗 𝑙 𝑑𝑜𝑤𝑛(𝑥 𝑖 𝑙-1 ) + 𝑏 𝑗 𝑙 )

Where the function down() denotes a sub-sampling (pooling) function, usually we use max pooling function or average pooling function. Generally, the pooling function will transform each distinct n-by-n block into one pixel of the output map. Then the output maps will be multiplied by a multiplicative bias β and add a bias b.

Although the pooling layer can reduce the amount of model's trainable parameters, it also brings some information loss. In order to reduce the information loss, in our model, we cancel the pooling layer after the first convolution layer. This operation can achieve a bit performance improvement.

Fully connected layer.

The fully connected layer is similar to the artificial neural network, if we use x to denote the input of fully connected layer, y to represent the output of the layer, the corresponding relation between x and y is as follow:

𝑦 𝑗 𝑙 = 𝑓(∑ 𝑤 𝑗 𝑙 𝑥 𝑖 𝑙-1 ) + 𝑏 𝑗 𝑙 ) (6)
In the formula ( 6), w denotes the trainable weights between the input and output, f represents the activation function described before.

Model optimization.

To get an optimized model, we use stochastic gradient decent method to minimize the model's mean squared error (presented in formula 7) with the batch size is one.

𝑀𝑆𝐸 = 1 𝑛 ∑(𝑦 𝑖 -𝑦 ̂𝑖) 2 𝑛 𝑖=1 (7)
In formula [START_REF] Davis | Nonparametric Regression and Short-Term Freeway Traffic Forecasting[END_REF], y denotes the model output, and 𝑦 ̂ represents the model's expected value.

Experimental Results

The model is evaluated using loop detectors data of Yan'an elevated highway for the year of 2011. In the experiments, we use a whole day's speed data, to predict the next day's whole-day speed data. The first 320 days of data are selected for training and the left days of data are used for testing. We use the matrix of previous day as the input of the model, the reshaped output vector is applied as the predicted value.

Handle the data

We handle the data in the way which is similar to the method used in [START_REF] Ma | Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[END_REF]. There are 35 loop detectors deployed on the Shanghai Yan'an elevated highway. In addition, the observed data is recorded every 5 minutes. Because of some limitation of loop detectors, we did some work in data cleaning. First, we reset the abnormal data, for example, some elements in the speed matrix are larger than 200(km/h), we set these data to be 100, because there are few cars can run at this speed which is also against Chinese law. According to Chinese law, the max speed on the elevated highway is 80km/s, we take the slight overspeed into account, thus, we choose 100(km/s) as the max speed in the matrix. Second, sometimes, the loop detectors employed on the Yan'an elevated highway do not work during 0:00 am to 4:00 am, in addition, we think that most people do not travel from 0:00 am to 6:00 am so we take no account of the data from 0:00 am to 6:00 am. Third, there are 3 loop detectors often cannot work, thus, 3 rows in the matrix are deleted. Last, in order to reduce the impact of abnormal elements in the matrix, we aggregate the data in time-dimension to obtain a 20-minutes interval. Eventually, the size of matrix is 32×54.

Experimental Settings

There are three convolution layers and two pooling layers. There 16 kernels with the size of 3×13 in the first convolution layer, 512 kernels with 3×11 size in the second convolution layer and 1024 kernels with 3×5 size in the last convolution layer. In the pooling layers, we do max pooling on the input. The experiments are conducted on a sever with i7-5820K CPU, 48GB memory and NVIDIA GeForce GTX1080 GPU. We implement these models on TensorFlow framework of deep learning. The configurations of our CNN model are listed as follow:

Where y denotes model's predicted value using the data of the previous day as the model's input, 𝑦 ̂ denotes the observed traffic speed value of the next day and n denotes the number of samples.

Experiment result

As shown in figures 3, 4 and 5, we visualize some kernels in different convolution layers, different feature maps and the output matrix during the experiment. In Fig. 3, The three images in the first row are kernels we choose from the first convolution layers, then the images in the next row are kernels selected from the second convolution layers, the last images are kernels chosen from the third convolution layers. In Fig. 4, The images in the first row are feature maps extracted from the first convolution layers, then second and third. The left image in Fig. 5 is the model's reshaped output which is used as prediction and the right is the visualized speed data in reality. In Fig. 6 the real data of the next day and the prediction of our model are represented as polylines.

Conclusion

In this paper, we proposed an CNN based deep learning model to predict whole-day traffic speed of elevated highway. In our model, we use asymmetrical kernels in the convolution layer. Our model focuses more on temporal dynamics which solve the problem that common methods cannot treat the special features and the temporal features differently. The experimental result proved that our model can achieve a good performance when comparing with other conventional method.

Fig. 1 .

 1 Fig. 1. The visualization of whole-day traffic speed of Shanghai Yan'an elevated highway.

Fig. 2 .

 2 Fig. 2. The architecture of our improved CNN model.

Fig. 3 .

 3 Fig. 3. Kernels' visualization from different convolution layers. The images in the first line are asymmetrical kernels of the first convolution layer and so forth.

Fig. 4 .

 4 Fig. 4. Feature maps extracted from different convolution layers. The images in the first line are feature maps of the first convolution layer and so forth.

Fig. 5 .

 5 Fig. 5. The left image is the output of the model, which is transformed to heat map and the right image is the visualized real traffic speed of the next day. We can see that the output of our model is very similar to the real data.

Fig. 6 .

 6 Fig. 6. The blue polyline is the model's prediction, and the red polyline represents the reality of the next day. The blue polyline can reflect the trend of the reality, and fit the reality well.We compare our CNN model with the most widely used methods of traffic flow prediction, such as ARIMA, KNN, ANN and common CNN. The performance of these models mentioned before is listed below:

Table 1 .

 1 The configurations of our CNN modelThe accuracy of traffic speed prediction is mainly assessed by two performance metrics which are Mean Relative error(MRE) and Mean Squared Error(MSE). MSE evaluate the model's absolute error while MRE shows the relative error of the model. MSE is demonstrated before and MRE is presented as the following:

		Layer	name	description
		Input	-	A matrix with 32×54 size
		Layer1 convolution	16 kernels with 3×13 size
		Layer2 convolution	512 kernels with 3×11 size
		Layer3 pooling	2x2 max pooling
		Layer4 convolution	1024 kernels with 3×5 size
		Layer5 pooling	2x2 max pooling
		Layer6 fully-connected
		Output -	A vector with 1728 elements
	3.3	Evaluation Metrics
			𝑀𝑅𝐸 =	1 𝑛	𝑛 ∑ 𝑖=1	|𝑦 𝑖 -𝑦 ̂𝑖| 𝑦 𝑖

Table 2 .

 2 The performance of different model mentioned before.As is shown in the list, neural network based models get lower MSE and MRE than KNN and ARIMA who are not based on neural networks. In addition, our model achieves the lowest MSE and MRE. The MRE of our model is less than the rest models' over 3%. And for MSE, the MRE of our model is less than the second-best model about 30%.

	Model	MSE	MRE
	Our Model	58.7607	0.0998
	Common CNN	94.0019	0.1346
	KNN	169.1917	0.2105
	ANN	126.9821	0.1592
	ARIMA	331.6745	0.2562

Acknowledgments. This work has been supported by the Fundamental Research Funds for the Central Universities of China and by National Natural Science Foundation of China under grant 61472284.


Qiu Jie1

Gao Junbo2

master graduate. Qiu Jie

Depression Tendency Recognition Model Based on College Student's microblog Text

Keywords: computing intelligence, data mining words:Depression tendency recognition, emotional decay factor, support vector machine(SVM), depression emotional dictionary, sina micro-blog

Abstract:In order to solve the issue of identifying depression tendency hidden in microblog text, a depression emotional inclination recognition model based on emotional decay factor is proposed. Make the self-rating depression scale, collect students' microblog text and ask the psychology specialist to annotate the microblog artificially. Construct the depressive emotion dictionary, and then build a depression emotion classifier based on support vector machine. Considering the continuity of depression mood swing, the mathematical model of emotional decay factor is constructed to realize the continuity of discrete emotional state. The experimental results show that the model can effectively identify the depression of user for a period of time, the recognition accuracy rate is 83.82%.

Introduction

Web2.0 era, micro-blog style format because the free, easy to use and a large number of users to publish their own thoughts and feel. Many researchers use microblogging text for emotional analysis research [START_REF] Liu Nan | Emotional analysis for microblogging essay[END_REF] , but the research is mainly for a specific thing [START_REF] Li | Fine-grained Emotion Analysis for Product Reviews [J][END_REF] , such as film critics, product reviews, etc., and for the text researched on depression emotion a bit less. How to timely and effectively identify the user through the microblogging tendencies, to prevent the long-term spread of depression has become an urgent problem that to be solved.

In the world, depression is one of the most common mental that illness people are faced with [3] . With the development of social networking platform such as microblogging, many researchers use the characteristics of user network to determine their psychological depression [START_REF] Youn | Using Online Social Media, Facebook, in Screening for Major Depressive Disorder among College Students [J][END_REF] ; literature [START_REF] Choudhury | Predicting Postpartum Changes in Emotion and Behavior via Social Media[END_REF] based on the frequency of maternal postpartum social networks, to analyze language style and establish a statistical model of maternal depression. Wang [START_REF] Xinyu | A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network[END_REF] and so on. The depressive patient is regarded as a node, and a graph network is constructed as the center. According to the attributes of the adjacent nodes in the network and the weight of the connection, the model is given to calculate the depression. [START_REF] Tian-Shuang | Study on the Relationship between Depression and Functional Brain Region Based on Restricted FMRI Low Frequency Amplitude[END_REF] The method of using brain-imaging study starting at the resting of the changes of brain function in patients with depression. [START_REF] Pengyu | Microblogging social network in the student depression identification method[END_REF] In terms of time users send microblogging, number of fans and a number of concerns such as depression and to analyze the situation of the user.

Existing studies have focused on the microblogging network and external factors to analyze user behavior characteristics of depressive tendencies situation, ignoring the role of micro-blog text Tech in terms of expression. In this paper, we will study the tendency of depression in the students' microblogging text, which integrate the emotional decay factor and time factor into the change of emotion, and describe the fluctuation of the depressive mood of the students more effectively and effectively. Hospital staff to identify patients with depression and provide aid.

Related work 2.1 Depression

Depression is a common psychological disease and the cause is very complex, which the researchers on the pathogenesis of depression put forward many theoretical hypotheses [START_REF] Shijing | Magnolia treatment of depression and antidepressant mechanism[END_REF] . Psychological and medical researchers also proposed a variety of Depression Diagnostic Scale, which provides an important experimental basis for the relevant practice. Zung [START_REF] Zung | Self-rating Depression Scale in an Outpatient Clinic: Further Validation of the SDS [J][END_REF] proposed depression self-rating scale with a high degree of operability and adaptability; many medical institutions also use this scale to measure the degree of depression in patients. It divides depression into four categories according to the score, [20, 41] indicates normal, [42, 49] indicates mild depression, [50, 57] indicates moderate depression, [58, 80] indicates severe depression. This article produces an online depression self-rating scale (https://sojump.com/jq/9743549.aspx) to obtain a score for each student's depression.

Depression Emotional Tendency Recognition Model

In this paper, the depression and emotional model is shown in Figure1.

Figure 1 Depression Emotional Tendency Identification Model

The model uses the support vector machine algorithm to construct the depressed emotion classifier, and uses the pretreatment training sample training classifier to obtain the reliable depression emotion recognizer. The identification module that the decay factor into emotions and time factor or the discrete continuous depressive emotional state. The emotional chart obtained by Chart efficient user who determines the depression for a period of time.

Preprocessing module

Before the microblogging text is used to identify the depression tendency, the acquired raw data must be converted into a data form that the computer can understand.

In order to solve the high dimensionality of feature space in text categorization, this paper uses CHI for feature selection [START_REF] Aizawa | An Information-theoretic Perspective of tf-idf Measures[END_REF] , and then calculates the weight using the classical TF-IDF method [START_REF] Vapnik | The Nature of Statistics Learning Theory[END_REF] in the field of text processing. In order to solve the sparseness problem of microblogging feature, we use (L T: W) to represent each microblogging, where L is the label of each microblogging, T is the characteristic item, and W is the weight of the characteristic item. Such as: "I really love you, close your eyes, that I can forget, but shed tears, or did not deceive themselves," get five feature words uttered that word feature selection, are "cheat", "Love "," Tears "," close your eyes "," shed ". Therefore, this sentence can be expressed as "1.0 28: 0.4528 39: 0.2295 49: 0.3215 862: 0.5811 1832: 0.54878", where 1.0 is the label, 28 is the characteristic word "cheat" index number and 0.4528 is the weight of the characteristic word.

The Construction of Emotion Classifier

Based on SVM, the depressive emotion classifier is constructed. Firstly, the nonlinear mapping function is constructed to convert from the original microblogging data x to the high dimensional space H. Then, the linear classifier is used to classify the microblogging emotion in the high dimensional space H. The Gaussian kernel function is used to linearize the nonlinear problem, and the Gaussian kernel function is shown in equation (1).

2 2 2 ( ) exp i x x i K x x      (1)
In the data samples may be time-linear high-dimensional space, SVM obtains the optimal hyperplane by calculating the minimization. At the same time, taking into account the number of data points may deviate from the hyperplane, it is slack variables introduced in the existing conditions based on the constraints, constraints as shown in equation (2).

( ) 1 i 1, 2, , T i i i y w x b n      (2)
At this point if you take any value, then any of the super-plane are in line with the requirements. Therefore, it is necessary to add an item after the original minimization function, so that all and the smallest, the new objective function as formula (3).

min {

1 2 ‖𝑤‖ 2 + ∁ ∑ 𝜉 𝑖 𝑛 𝑖=1 } 0≤ 𝛼 𝑖 ≤ ∁ (3)
Where C is a parameter that controls the weight between the two above-mentioned objective function. Further, the constraint is added to the objective function, Lagrange function is configured, the solution was expressed constraints corresponding Lagrange multiplier. Corresponding classification function as shown in equation ( 4).

n i i i i f x y K x x b       ( 4
)
Where χ is the text of the microblog to be classified, 𝜒 𝑖 is the support vector for the text, and 𝑦 𝑖 is the corresponding classification of 𝜒 𝑖 .When 𝑓(𝑥) > 0,there χ has a tendency to depression; 𝑓(𝑥) ≤ 0 is normal.

Mathematical model of depression and emotional decay

Basic assumptions

Depression emotion classifier can identify whether each of these two micro-blog text depressive emotional state, and cannot effectively portray the evolution of these two emotional states. As a thoughtful individual, the person's depression is constantly changing over time, depression decay before giving formula, we first proposed the following basic assumptions. 1. The uncertainty of a person is not a depressed patient; this depression only assumes that individuals within any a period of time tend to be fluctuating mood.

2. An initial state is assumed that any normal individuals are emotional state, represented by state c. In this experiment, the state is the result of emotion classifier, there are two states, where c = 1 for the depression tendency state and c = 0 for the normal emotional state. The state of a microblogging can only explain the individual's emotional state, and cannot describe the degree of individual state, the corresponding state value. 3. The state definition of a value greater than 0 indicates predisposition to depression, state 0 indicates a value less than pleasant state, emotional state value equal to 0 indicates an equilibrium state. Moreover, the larger the state, the higher status individuals tend to some degree bias. The happy state is a subset of the normal state.

Depression Emotional Decay Formula

The structural decay formula is (5).

( ) ( 1) ( 1) nt f t f t e      
(5) Where 𝑓(𝑡) represents time, the micro-blog corresponding depressive emotional state value; 𝑓(𝑡 -1) is the depressive emotion state value corresponding to the microblogging text of the last time. λ is the emotional decay factor, which indicates the decay speed of the emotion, λ = 0.5, that is to say that the depression emotion is in accordance with the half-life law; the value of n is related to the state of microblogging at two adjacent time points, and according to the above basic assumption 3, the value of n is as follows (6).

    2 0,1 1 1 0,1 0 cc n cc               (6)
In view of the limited number of students' microblogging text, the time of microblogging has no regularity. In this paper, the time t which in the above-mentioned emotional decay mathematical model is defined as the time interval of two adjacent microblogs, then the range of t is: T = 0,1,2,3 ⋯, n, and the initial state of any individual 𝑓(𝑡 = 0) = 0.

Basic assumptions above 2 (3) specifically explained in emotion decay formula. When two or more consecutive 0 state occurs, when the state of the next moment 𝑐 = 1, then 𝑡 𝑖 . On the contrary, when the two consecutive or the above state one, if the next time t of the state 𝑐 = 0, then the value of t is not set, but then the next time in turn increase. In the above two kinds of state alternation process, the value of 𝑓(𝑡 -1) remains unchanged, still the state value of the last moment.

To quantitatively characterize the condition of the individual depression tendency over time, individuals taking herein mean value of the emotional state of a period of time for the metric, as shown in equation (7). Where 𝑡 = 𝑖 represents the depressive state value of the i-th microblog from the i-th microblogging, and 𝑓(𝑡 = 𝑖) represents the mean value of depression from the i-th microblog to the n-th microblog.

( )

1 n ti f t i Avg n i      (7)
Above mathematical model of emotional decay, this paper presents a mathematical model that identifies whether the individual is depressed over a period, as in equation (8).

Ε(Α𝜈𝑔) = { 𝑛𝑜𝑚𝑎𝑙, Α𝜈𝑔 ∈ [-1.6,0.2] 𝑑𝑒𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛, Α𝜈𝑔 ∈ [0.2,2] (8)
Equation ( 8) shows that the average state of individual Yu state in the [-1.6,0.2] interval, the emotional state is normal; depressive state of the average in the [0.2,2] interval, there is a tendency to depression.

Case Analysis

Twitter test set is selected from the nickname "Fuji child package" student embodiment, with the emotional classifier on its pre-processed microblogging text classification, in view of the limited space, only select the first five microblogging content and classifier output. The emotional state presented as shown in Table 1 below. The state sequence of the output is {1,0,1,0,0,1,1,0,0,0,1,0,0,1,0}.According to Hypothesis 3, the state value 𝑐 = 0 at time 𝑡 = 0, that is. After the initial state, the 𝑓(𝑡 = 0) = 0 sequence is {0,1,0,1,0,0,1,1,0,0,0,1,0,0,1,0}, according to the emotional depression Decay the mathematical model that get the emotional state values ,as shown in Table 2 below. According to the different emotional status values at all times, you can draw depressive mood charts.

For this case, from 𝑡 = 0 to 𝑡 = 15, calculate the mean value of depression

Ανg = ∑ 𝑓(𝑡) 16 ⁄ = 0.7631 15 𝑡=0
. According to the formula ( 8), it can be judged that the student is in a depressed state during that time. 3 below. After microblog corpus manually labeled, received a total of depression microblogging 1512 and 3786 normal microblogging. There is 1154 test set microblogging. This experiment is carried out in win10 32-bit system, using python language.

Table 1 microblogging content and emotional classifier output status Table 2 emotional state value corresponding to the emotional state sequence

( 0) 0 ft   2 0.5 ( 1) ( 0) ( 1) 0.6065 f t f t e        1 0.5 2 ( 2) ( 1) ( 1) 0.2387 f t f t e         2 0.5 3 ( 3) ( 2) ( 1) 0.4618 f t f t e         1 0.5 4 ( 4) ( 3) ( 1) 0.3264 f t f t e         1 0.5 5 ( 5) ( 4) ( 1 0. 44 ) 24 f t f t e         2 0.5 ( 6) ( 5) ( 1) 0.8509 f t f t e        2 0.5 2 ( 7) ( 6) ( 1) 1.2188 f t f t e         1 0.5 3 ( 8) ( 7) ( 1) 0.9957 f t f t e         1 0.5 4 ( 9) ( 8) ( 1) 0
.8604 f t f t e         1 0.5 5 ( 10) ( 9) ( 1) 0
.7783 f t f t e         2 0.5 ( 11) ( 10) ( 1) 1
.3848 f t f t e        1 0.5 2 ( 12) ( 11) ( 1) 1.0169 f t f t e         1 0.5 3 ( 13) ( 12) ( 1) 0.7938 f t f t e         2 0.5 ( 14) ( 13) ( 1) 1.4003 f t f t e        1 0.5 2 ( 15) ( 14) ( 1) 1.0324 f t f t e         4
Table 3 training set and test set number and microblogging number

Result analysis

Experiment 1 test concentration Twitter 1154, using the depression and emotional depression without using dictionaries and dictionary emotion experiment, the results shown in Table 4 below. As can be seen from Table 4, the artificial depiction of emotional depression that a single microblogging recognition accuracy significantly improved.

Experiment 2 uses the Depression Emotional Classifier to classify the microblogging text of each student in the test set, and obtain the intelligence status and classification accuracy of each microblogging emotion of 68 test cases. Some of the data are shown in Table 5 below. Using the mathematical model of depressive emotion decay to draw out the emotional trend graphs of 68 test cases, limited to the limited length of the articles, the trend graphs of some test cases were randomly selected from two types of test sets, as shown in Fig. 2 and Fig. 3.

As can be seen from Fig. 2 and Fig. 3, a normal emotional state of the individual curves are generally fluctuations in the χ axis, the majority of the individuals are in a state of pleasure. On the contrary, the emotional curves of individuals with depression tend to be above the χ axis; two types of individuals over time curve mood swings are likely to exceed the critical emotional point 𝑓(𝑡) = 0 , which is also a composite of the real situation of human emotional changes.

Figure 2 shows the emotional trend of students with depression

Figure 3 Normal student's emotional chart

Experiment 3 randomly selected two periods for each test case in 68 test cases, with 15 microblogs as a period. Among them, from 20 depressed tendencies of the students which in the depression trend chart selected 40 time periods, from 48 emotional normal students of which the depression trend chart selected 96 time periods, respectively, using the formula (7) (8)to determine. Results are shown in table 6. Recognition accuracy than the normal emotions of depressive tendencies rate is low, probably due to the students in the normal state that issued microblog contains words related to depression. Based on the microblogging text, this paper constructs a model of depression and emotion tendency recognition. The experiment shows that the model can identify the tendency of the individual in a period of time. This paper only considers the interaction of depression at adjacent time points, but does not consider the broader time interval factor. At the same time, the value of emotional decay factor is related to various factors such as environment and personality. It is only assumed that the emotional decay meets the half-life. Regularly, follow-up will be the above aspects of research, with a view to a better experimental result.

Experimental results and analysis 4 . 1 Data acquisition and annotation label content 1

 411 Did not expect stomach pain into the hospital 0 Meet the Tianzi Square 1 To TM computer, brush more than 200 sheets of A4 and close to the collapse of the end of the period. 0 Headphones placed in the draft of Stefanie and always can cause a lot of resonance and back 0 How the end of the holiday began a little homesick From December 2016 to March 2017, data collected from 271 students' microblogging, microblogging that get 7321 text. From the scale score results, 80 people tend to depression, normal 191 people. Ratio of 3: 1, were randomly selected from two types of training with examples and test students, the number of training set and test concentration shown in Table

Table 4 uses the experimental accuracy of depression and emotional dictionary

 4

	TRANING SAMPLE	NO ADOPTED	ADOPTED
		DICTIONARY	DICTIONARY
	2000	70.62%	80.58%
	3000	73.83%	85.68%
	4000	75.85%	87.36%
	5298	78.45%	90.04%

Table 5 different students' single microblogging classification accuracy

 5

		Training	Test
	Depression	60	20
	normal	143	48
	total	5298	1154

Table 6 Test results for the test set

 6

			小桑 meng			6	43	0.9767
			2					噼里啪啦啦啦(45分)
								不二小孩包(44分)
		1.5					我叫麥幾幾(43分)
	value		1					Arioster(42分)
	Status	0.5
			0
	-0.5	0	2		4	6	8	10 12 14 16 18 20 22 time
	value Status	-1.4 -1.2 -0.8 -0.6 0.2 0 -0.2 -0.4 -1 -1.6 -1.8	0	2	4	6	time 10 12 14 16 18 20 22 加油加盐再加米(25分) 8 鲜奶妹(37分) Stephanie_T(40分) 大胡糊糊呼呼(36分)
							Total	Correctly identify	Accuracy
	Depression tendency 40	34	85.00%
	Normal mood			96	80	83.75%
	total						136	114	83.82%