Qing Xu
email: qing.xu@utbm.fr

Frédéric Lassabe
email: frederic.lassabe@utbm.fr

Hakim Mabed
email: hakim.mabed@pu-pm.univ-fcomte.fr

Alexandre Caminada
email: alexandre.caminada@utbm.fr

Fitness Landscape Analysis for Scalable Multicast RRM Problem in Cellular Network

Keywords: fitness landscape, metaheuristic approach, multimedia multicast, radio resource management

This paper aims to solve the Radio Resource Management (RRM) problem for Multimedia Broadcast Multicast Service (MBMS) system in cellular network. We develop a flexible model to perform dynamic radio resource allocation for MBMS service by using metaheuristic approach. We conduct fitness landscape analysis to study the characteristics of the proposed model, which helps us to select appropriate search strategy. Simulation results show that the proposed algorithm provides better performance than existing algorithms.

Introduction

The MBMS service over UMTS could be carried by PTM or PTP mode in a base station. A cell is defined to be the geographic coverage of a UMTS base station. In PTM mode, service is transmitted by a forward access channel (FACH) covering the whole cell. Each FACH needs one channel code serving large amount of users, but may waste power when users are close to the base station [START_REF] Alexiou | A power control scheme for efficient radio bearer selection in MBMS[END_REF]. The PTP mode uses dedicated channel (DCH) or shared channel (HS-DSCH). Each DCH needs one channel code serving one dedicated user. A HS-DSCH occupies up to 15 channel codes. PTP mode is more efficient than PTM but the served user number is limited. In UMTS where the radio resources (transmit power and channelization codes) are limited, the transmission mode selection is crucial. To improve the efficiency, MBMS Power Counting (MPC) [START_REF]radio resource management strategies[END_REF] proposes to switch from PTP to PTM when pre-estimated power exceeds an operator-defined threshold, and vice versa. MPC is simple to implement but has very limited flexibility since it does not consider multimedia scalability. Dual Transmission Mode (DTM) allows the mixture of PTP and PTM mode for a same service [START_REF] Christophorou | Enhanced radio resource management algorithms for efficient MBMS service provision in UTRAN[END_REF]. However, simulation shows that it is only beneficial for up to 5 users [START_REF] Technologies | 3GPP TSG RAN WG128 R1-02-1240 power usage for mixed FACH and DCH for MBMS[END_REF]. Scalable FACH Transmission (S-FACH) [START_REF] Correia | Multi-resolution broadcast/multicast systems for MBMS[END_REF] transmits scalable encoded service through PTM mode. Services are divided into single layer (SL) and multiple layer (ML) transmission schemes, the latter is divided into several flows. The basic flow is transmitted with lower bit rate while the advanced flows with smaller but fixed geographical coverages. However, S-FACH is still based on the fixed coverage so that the resource allocation can not be optimal.

Consider the quite dynamic property of the RRM problem, all mentioned existing approaches provide only limited flexibility. Therefore, the power and date rate allocation can not be optimal. To address this problem, we propose a Flexible Radio Resource Management Model (F2R2M) by using metaheuristic approach. This model combines two objectives: to satisfy the QoS requirement of multicast service and to minimize the power consumption while avoiding saturation of channel code occupation. To understand the behavior of the proposed model, we conduct fitness landscape analysis on the model under five different scenarios. Based on the analysis results, we evaluate two search operators, and compare the performance with existing approaches.

This paper is structured as follows. The proposed model is formulated in section 2. The fitness landscape analysis and the simulation results are given in section 3. The conclusion is drawn in section 4.

We focus on multicast transmission within one cell c, which is the geographic coverage of a base station. The input variables are defined as follows: T (c) = {t 1 , ...t N t } is a set of users located in c, and their instantaneous geometry coordinates C(c) = {(x 1 , y 1 ), ..., (x N t , y N t )}. A set of services S(c) = {s 1 , ..., s N s } and their the flows

F (s i ) = {f s i ,0 } or {f s i ,1 , f s i ,2 [f s i ,3 ]} are transmitted in c. f s i ,0 indicates s i is SL transmission scheme. f s i ,j (j > 0)
is the sublayers of ML scheme service, f s i ,1 is the basic flow and f s i ,j (j > 1) is the advanced flow which is optional for quality improvement. Each user group subscribing to the same service is called a multicast group: Dist(s i ) = {t n , t m , ...}.

F2R2M supports the co-existing of PTM and PTP modes for each flow. It partitions Dist(s) into four disjointed sets: users served through a FACH UE fach (f s,j ); users transfered through DCHs UE dch (f s,j ); users sharing HS-DSCH UE hs (f s,j ) and non-served users UE noch (f s,j ). The decision of user set follows two principles: 1) to guarantee the service coverage, all users in multicast group should be selected to receive f s i ,0 or f s i ,1 , 2) in ML transmission, the advanced flow is sent only when a lower flow has been received by users. The partition of user depends on channel characteristics: 1) FACH can be listened by all users within its coverage, hence UE fach (f s,j ) includes the nearest group of users in Dist(s), with the distance from the base station under a threshold d thr . d thr is dynamically determined during search procedure. 2) the users in Dist(f s,j ) farther than d thr , are assigned to UE dch (f s,j ) or UE hs (f s,j ). Consequently, according to the user partition and flow bandwidth, available channel code(s) is associated with a nonempty user set. When no channel code is available for a given user, this user is switched to UE noch (f s,j ).

A solution is defined as variable x, which is a matrix with Nt (number of flows) rows and Nf (number of users) columns:

x =      f (s 1 , 1) f (s 1 , 2) ... f (s Ns , 0) t 1 ch 1,1 ch 1,2 ... ch 1,Nf ... ... ... ... ... t i ch i,1 ch i,2 ... ch i,Nf ... ... . . . ... ... t Nt ch Nt,1 ch Nt,2 ... ch Nt,Nf      , ch i,j ∈ {-1, 0, 1, 2, 3}
x(i, j) indicates the channel allocation of user t i for flow f j . Values 0, 1, 2, 3 represent four possible user allocations: UE noch,fach,dch,hs (f s,j ). -1 means the user does not belong to the multicast group. Hamming distance is used to measure the distance of two feasible solutions, which will be used in the calculation of indicators for fitness landscape analysis.

The power consumption is implicitly determined once user and channel code allocation are determined. As simulated in [1], P FACH can be got through a look up table based on the user distribution in UE fach (f s,j ). The DCH transmission power for n users [START_REF] Romero | Radio Resource Management Strategies in UMTS[END_REF] is calculated by : P DCH =

Pp+ n i=1 Lp i • Pn+I i W (E b /No)R b,i +p 1-n i=1 p (E b /No)R b,i +p ,
where P p is common control channel power, P n is the background noise, Lp i is user path loss, W is the bandwidth in UMTS, R b,i is the transmit rate of ith user, p is the orthogonality factor, I i is the intercell interference observed by ith user. The transmit power to guarantee the required HS-DSCH throughput [START_REF] Harri | HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications[END_REF] is:

P HS-DSCH ≥ SINR×[p-G -1 ] Pown
16 , in which P own is the own cell interference experienced by user, G is the geometry factor defined by G = Pown P other +P noise , related with the user position.

A two-dimensional fitness value F (x) = {T h(x), P o(x)} is defined to measure the solution quality. P o(x) is the MBMS transmission power:

P o(x) = s i ∈S(c) f j ∈F (s i ) ch l P f j ,ch l . T h(x) is the throughput loss in cell: T h(x) = s i ∈S(c) f j ∈F (s i ) tu∈Dist(f s i ,j ) max [-∆ j,u , 0]. ∆ j,u
is the difference between allocated channel bit rate (determined by its channel code(s) [START_REF]spreading and modulation (FDD)[END_REF]) and flow bandwidth. The optimization target is to first satisfy the throughput requirement within the maximum power budget, then to minimize the transmission power. Therefore, the comparison of a new solution x ′ and current solution x is conducted in lexicographic order: x ′ is evaluated as a better solution when T h(x ′ ) = T h(x) and P o(x ′ ) ≤ P o(x), or T h(x ′ ) < T h(x).

We propose two operators to move solution into a neighbor solution.

• Hybrid-moves operator δ H randomly selects user t k from a non-empty UE cho (f s,j ), then t k or a block of users including t k is moved from UE cho (f s,j ) to UE ch i (f s,j ). The moved users depends on the chosen set. For example, if ch i is FACH, the coverage is enlarged to t k , all the users nearer than t k can now hear from FACH, thus, no matter what user sets they are currently allocated at, they should stay or be inserted in UE fach (f s,j ).

• Insert operator δ I moves only one user for each operator application.

Once it chooses FACH as ch i or ch o , then t k is determined: the nearest user in UE hs (f s,j ) ∪ UE dch (f s,j ) ∪ UE noch (f s,j ) , or the farthest user within UE fach (f s,j ).

Fitness Landscape of F2R2M

In Table 1, five test scenarios with different service configurations and user numbers are designed for simulations. For example, in the first scenario 2s-50-sn, two services s 1 and s 2 are transmitted to 30 and 20 users respectively. s 1 consists of three flows: f 1 ,f 2 at 32 kbps, and f 3 at 64 kbps. s 2 consists of only one flow f 0 at 128 kbps. The users are assumed to be uniformly distributed around the base station. In our model, two fitness landscapes L hy and L in are defined by δ H and δ I . We study their characteristics in three aspects: 1) the distribution of feasible solutions within search space; 2) the distribution of fitness space; and 3) the links between distance and fitnesses of solutions. Two populations of solution S ini and S lo are generated. S ini is composed of 600 solutions randomly chosen from the initial solution space. S lo is the population of local optima found by applying hill climbing (HC) [START_REF] Glover | Handbook of Metaheuristics[END_REF] to S ini . Starting from a random solution in S ini , HC evaluates all its feasible neighbors and replaces it by the neighbor that has the best fitness. HC stops at S lo when no neighbor is better than current solution.

Analysis of Search Space

To study the distribution of feasible solutions in search space, two types of distance are calculated: d ini and d lo are the distances among any two solutions in S ini and S lo . Table 2 presents the minimum, the maximum and the quartiles (median, Q1 and Q3) of these distances. Firstly, for both operators, the distance statistics in the initial solution space (d ini in S ini,in and S ini,hy ) are homogeneous, that is simply because they are randomly selected in the initial solution space. Secondly, when looking at the the distance statistics in the local optima space (d lo in S lo,in and S lo,hy ), δ H generates much converged space. better than that of S lo,in (T h hy (lo) < T h in (lo)). The same situation for the other scenarios is confirmed in Table 3, in which we can see δ H finds solutions with smaller T h(x), hence better solutions than δ I . This can be explained by the fact that, unlike δ I , δ H moves a block of elements which explores larger search space, so that it can easily escape from the local optimal. 2). Therefore, L in is "shallower" than L hy .

Analysis of Fitness Space

Results Comparison with Conventional Algorithms

The solutions of competing approaches and the best found solutions based on F2R2M are shown in

Figure 1 Fig. 1 .

 11 Figure 1 illustrates the fitness value distribution of S ini and S lo for the fifth scenario 3s-100u-ssn. The fitness values of S ini are well diversified for both operators. Comparing the first and third subfigures, the quality of S lo,hy is

Table 2

 2 Analysis of Search Space: distance between solutions of landscapesL in : d ini in S ini,in L in : d lo in S lo,in L hy : d ini in S ini,hy L hy : d lo in S lo,hy

		Min	Quartiles	Max	Min	Quartiles	Max	Min	Quartiles	Max	Min	Quartiles	Max
	scen 1	15	62 54,72	109	17	62 54,71	108	19	62 53,71	108	0	34 23,45	109
	scen 2	20	64 55,74	114	20	65 55,75	117	23	64 54,73	121	0	33 22,46	127
	scen 3	20	64 55,73	116	20	64 55,74	116	21	65 54,73	115	0	43 30,57	138
	scen 4	26	66 56,76	148	20	66 56,76	148	20	64 55,73	116	0	35 24,49	156
	scen 5	21	64 55,73	130	19	65 55,76	180	21	64 55,73	129	0	38 23,56	194

Table 3

 3 Fitness space analysis: fitness values of populations of local optima Analysis of Links between Distance and Fitness In F2R2M, step length is the number of implemented operator applications by using hill climbing method. The average step length in L hy for five scenarios are: 26, 27, 43, 68, 100. While in L in , they are: 10, 10, 9, 11, 13. In general, δ I moves shorter distance than δ H , which makes the search procedure walks nearby the initial solution. This explains that the search space of S lo,in does not concentrate the S ini,in (Table

	Scenarios	Fitness		L hy : S lo,hy				L in : S lo,in
			Min	Med Q1,Q3	Max	Mean	Min	Med Q1,Q3	Max	Mean
	2s-50u-sn	Th(x) %	0	0 0,0	58	1.7	4.5	57.5 52.25,62	69	56.21
		Po(x) w	7.08	14.89 12.95,18.18	19.0	15.28	1.5	6.53 5.03,8.33	18.54	6.44
	2s-50u-ss	Th(x) %	0	0 0,0	62.5	0.94	0	58.75 54,62	69.5	10.74
		Po(x) w	5.81	14.63 14.36,15.02	16.58	14.57	1.69	6.24 4.69,7.93	16.3	6.44
	3s-80u-snn	Th(x) %	0	4 0,15	91.5	9.43	37	87.5 82,92	99	86.61
		Po(x) w	5.543	17.29 15.92,18.79	18.99	17.2	2.10	6.56 4.96,8.21	18.96	6.74
	3s-80u-ssn	Th(x) %	0	1 0,2	93.5	3.8	20.5	88 83.5,91.5	99	86.34
		Po(x) w	6.38	17.57 17.22,17.63	19.0	17.2	2.1	6.56 4.96,8.21	18.96	6.75

Table 4 .

 4 Solutions are represented in two-dimensional: T h(x) in percentage and P o(x) in watts. The maximum power budget is set as 19w hence solutions with T h(x) < 19 are feasible and emphasized in boldface. Both operators in F2R2M can always find good enough solutions: less throughput loss with less power consumption than existing approaches. The mean fitness values of found solution with F2R2M-in for scenarios 2 to 5

Table 4

 4 Results comparison with existing approaches (7.13, 2.55). While for F2R2M-hy, the mean fitness values are from (1.7%, 15.3 w) to (7.29%, 18.12 w) with standard deviation from (8.2,2.56) to (11.93, 1.4). δ H performs better than δ I , which proves the discussion in section 3 that δ H has capacity of "jump" from bad solutions, while δ I can only stay in basins. Besides, the average consuming time of one HC trial is calculated, δ I and δ H needs 0.06s and 0.16s for 2s-50u-sn, 0.77s and 0.31s for 3s-100u-ssn. Both operators can find good enough solution with acceptable time. δ H can move more steps hence farther than δ I , therefore the hybridmoves operator costs double time than insert operator.In order to solve the RRM problem in MBMS system, we propose a novel model named Flexible Radio Resource Management Model (F2R2M) using metaheuristic approach. In our model, two objectives are took into account to satisfy the QoS request and minimize the power consumption. Based on fitness landscape analysis, a study is investigated on the performance of the two proposed neighborhood functions. Simulation results show that our approaches can achieve significant gains comparing with the other existing approaches. In the future work, we are interested in applying the other local search algorithms such as Simulated Annealing and Tabu Search to improve the search efficiency.

	Scenarios	MPC	DTM	S-FACH	F2R2M-in	F2R2M-hy
		{T h(x), P o(x)}	{T h(x), P o(x)}	{T h(x), P o(x)}	{T h(x), P o(x)}	{T h(x), P o(x)}
	2s-50u-sn	0%,27.2	0%,30.5	65% 10.2	4.5%, 18.5	0% 10.2
	2s-50u-ss	0%, 27.2	0%, 30.5	47% 15.4	0%,15.58	0% 13.1
	3s-80u-snn	0%, 32.5	0%,37.7	25.4% 27.0	25.4%, 16.9	0% 15.0
	3s-80u-ssn	0%, 32.5	0%,37.7	36.2% 22.6	15.4%, 16.5	0% 14.4
	3s-100u-ssn	0%, 37.73	0%,37.69	51.18%,15.9	15.9%,18.12	1.76%,17.5
	are from (56.2%, 6.4 w) to (74.4%, 6.65 w) with standard deviation from (8.12,
	2.45) to


Carole Rodon

Isabelle Ragot-Court

Jian Zhuo

How to measure risky driving behaviours of electric and motorized two-wheelers in China? A critical review of current existing tools used towards Chinese drivers

Keywords: Driving behaviours, risk, electric-two-wheelers riders, motorizedtwo-wheelers riders, China, social-cognitive psychologist approach

 sole definition of risky driving behaviours in order to be operationalized into items dedicated to E2W and M2W riders.

Introduction

From Europe, the mode of transport that is so stereotypical China remains the human powered two-wheelers i.e. the bicycle. However, the road landscape diversifies and Electric twowheelers (E2W or electric bike) have emerged as a popular mode of transportation in many large cities during recent years [START_REF] Weinert | The Transition to Electric Bikes in China: Effect on Travel Behavior, Mode Shift, and User Safety Perceptions in a Medium-Sized City[END_REF]. Actually they compose the main part of the two-wheeled traffic beside regular bicycles. It is not easy to have clear statistic data about the distribution of vehicles types as well as about road safety in China and in many other developing countries (Luoma &Sivak, 2007 in Zhang, Tsimhoni, Sivak and[START_REF] Zhang | Road safety in China: analysis of current challenges[END_REF]. One official source even though it is not that quite recent, 2009, is the Asian Development Bank with its report titled ''Electric bikes in the People's Republic of China: impact on the environment and prospects for growth''. There, statistics features report E2W ownership as estimated in 2007 to be between 33 million and 45 million (Feng, Jiang, et al., 2007;Zhejiang Bike Web 2007). More recently the estimated total number of E2W was over 120 million in 2010 (Xinhua news, 2010[START_REF] Wu | The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study[END_REF]. Motorcycles ownership in the PRC reached 80 million by 2005 (National Bureau of Statistics 2007) and there are around 460 million bicycles and 13 million cars. In this Asian bank development bank report, it is underlined then that there is no category for E2W in the National Statistics Bureau data set. Therefore, it is difficult to determine whether E2W are counted as bicycles or small utilitarian motorcycles (which could include mopeds) or not counted at all since registration requirements differ from city to city. Most importantly, the growth development in China of E2W is expected to keep going notably. For instance in Shanghai, the LGP two-wheelers (Liquefied Petroleum Gas) which were still 288 000 in 2009, will in 2013 be banned. Since 2003, this has been already the case for the motor gasoline two-wheelers. This displacement mode is closely related to considerations of urban planning. With the expansion of cities, the decline of peri-urban areas and the increasing congestion of roads, the appeal for E2W is that it allows travelling long distances at low cost. In this context, [START_REF] Wu | The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study[END_REF] report that Chinese government agencies are showing an increasing interest in promoting twowheeled traffic. But this growing popularity of two-wheeled traffic is not without safety concerns. In 2004, 13,655 regular bicycle riders died and 54,286 were seriously injured in road accidents. These statistic figures represent 12.8% of all traffic fatalities and 11.3% of injuries (CRTASR, 2004[START_REF] Wu | The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study[END_REF]. Traffic safety for E2W riders is considered as an even more severe problem since the number of fatalities and injuries has increased dramatically over the past few years. In 2004, 589 E2W riders died and 5295 were seriously injured. The corresponding figures increased to 2469and 16,468, respectively in 2007(CRTASR, 2007[START_REF] Wu | The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study[END_REF]. To sum up local views about E2W are quite ambivalent [START_REF] Cherry | Electric Two-Wheelers in China: Analysis of Environmental, Safety, and Mobility Impacts[END_REF] as the general context is of a growing crisis of unsafe roads with a mortality rate four to five times higher than in other nations. It is in this general context that the Road Traffic Safety Law of the People's Republic of China 1 has been stated in 2003; the Chinese first-ever law on road traffic safety. It was intended to address this alarmingly high traffic fatality rate. Opponents of E2W often cite the number of deaths as proof of dangerousness while others consider that this reflects their vulnerability [START_REF] Cherry | Electric Two-Wheelers in China: Analysis of Environmental, Safety, and Mobility Impacts[END_REF]. Either dangerous and/or vulnerable, a deeper knowledge on risk factors of both E2W and 1 http://english.gov.cn/laws/2005-09/07/content_29966.htm

The Asian Conference on the Social Sciences 2012 Official Conference Proceedings Osaka, Japan M2W riders and their accident involvement is required and more specifically in the Chinese context.

Adopting this objective, we launch a research program which aims to improve specific knowledge about the risk of E2W riders in China under a social-cognitive psychologist approach and in comparison with M2W. In this paper, and as a first stage of this program, we refer to general statements and Chinese specificities regarding traffic road context and vehicles' interactions. Then we classically refer to the literature in order to answer the question ''what is called a risky driving behaviors as a two-wheelers?'' and ''how are they actually measured in the Chinese traffic road context?''. We reviewed both how a definition is made and how it is operationalized into items. That lead us to critically examine currently exploited tools in China about both cars and two-wheelers related to dangerous driving behaviours: the Chinese Driving Questionnaire (CDQ, [START_REF] Xie | A social psychological approach to driving violations in two Chinese cities[END_REF], the aberrant driving behaviors questionnaire (Shi, Bai, Ying & Atchley (2010), and the Chinese Motorcycle Rider Driving Violation (CMRDV, [START_REF] Cheng | Development of a Chinese motorcycle rider driving violation questionnaire[END_REF].

General statements and Chinese specificities regarding traffic road context and vehicles' interactions

Generally speaking, we can state from the Western literature that the traffic road involves various users with different designs of vehicles (E2W, M2W, cars…) as various driving styles that result from the physical and dynamic characteristics of these vehicles. All these elements lead to difficulties in terms of interactions management between these users, notably between power two-wheelers riders and car drivers [START_REF] Hole | Some factors affecting motorcyclists' conspicuity[END_REF][START_REF] Horswill | Motorcycle accident risk could be inflated by a time to arrival illusion[END_REF][START_REF] Crundall | Car drivers' attitudes towards motorcyclists: A survey[END_REF][START_REF] Hancock | Driver workload during differing driving maneuvers[END_REF][START_REF] Obenski | Motorcycle Accident Reconstruction: Understanding Motorcycles[END_REF][START_REF] Van Elslande | Specificity of error-generating scenarios involving motorized twowheel riders[END_REF]Jaffard and Van Elslande, in press). Two explanations are the potential mutual misunderstanding or ignorance of the driving situation's determinants and difficulties in taking others into account. These include various practices among users which can be related to the physical and dynamic characteristics of their vehicle and their level of familiarity with other users [START_REF] Mundutéguy | A Contribution to Situation Awareness Analysis: Understanding how mismatched expectations affect road safety[END_REF][START_REF] Mundutéguy | A Contribution to Situation Awareness Analysis: Understanding how mismatched expectations affect road safety[END_REF]Crundall et al., in press;Shahar et al., in press;[START_REF] Comelli | Brightly coloured motorcycles and brightly coloured motorcycle helmets reduce the odds of a specifific category of road accidents: a case-control study[END_REF][START_REF] Magazzù | Are car drivers holding a motorcycle licence less responsible for motorcycle--Car crash occurrence?: A non-parametric approach[END_REF][START_REF] Weber | Unfallauslösende Faktoren bei motorisierten Fahrrädern [Factors causing accidents with motorized bicycles[END_REF][START_REF] Brooks | Driver awareness and motorcycle accidents[END_REF][START_REF] Crundall | Car drivers' attitudes towards motorcyclists: A survey[END_REF]. Over this general statement that might be quite common to different countries we have to highlight the specificities of the Chinese road context as at least different from the western countries. First in Europe, EW2 are almost absent from the road landscape and M2W still account for less than 2% of all road traffic in term of kilometers driven! Other strong differences with EU countries exist about the vehicles fleet: heavily-loaded trucks, many non-licensed motorcycles, and a large number of pedestrians [START_REF] Zhang | Road safety in China: analysis of current challenges[END_REF]. Shi, Bai, Ying and Atchley (2010) explain that since 2000 due to the increase of urbanization and motorization in China, the Chinese road traffic receives a massive additional numbers of new drivers with varied driving experience on road in China. They point out the weakness of driver training and the unfamiliarity with road safety and the rules for economic and historical causes. They claim that after years of cycling (traditional mode), the abrupt transition to motorized mode is able to generate safety problems. Indeed, there are some distinctions between the two modes regarding restrictions of traffic lanes, different priority rules etc. The authors assume that some drivers would reproduce the same behavior as those practiced by bike such as stopping anywhere and anyway. To sum up, the road regulations, the infrastructures and the fleet of vehicles are in China not yet stable. It's also the case about the driving trainings conditions. Finally, another Chinese characteristic of the road is that according to traffic laws in China, and though having great differences in physical performance, both E2W and regular bicycles are classified as non-motor vehicles. Moreover, both E2W and regular bicycles are operated in the same lanes and are subjected to the same traffic rules. However, most E2W on the road exceed the performance limits of the national standard and travel much faster than regular bicycles [START_REF] Weinert | The Transition to Electric Bikes in China: Effect on Travel Behavior, Mode Shift, and User Safety Perceptions in a Medium-Sized City[END_REF][START_REF] Wu | The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study[END_REF]. All in all, these statements argue for expected differences between users in China and Western countries in term of driving behaviours and consequently of risky driving behaviors as well.

Critical review of literature: which definitions and which measures are made of risky driving behaviours on the Chinese road context? Now that we described the Chinese road context and stated its peculiarities, we keep it as a general background to assess the literature. We critically review recent research studies implemented in China aiming to find out ''what is called a risky driving behaviors as a twowheelers?'' We review both how a definition is made and how it is turned into items for implementations in China. First a simple research on Sciencedirect gives us a glance on the still needed research publications 1/at an international level 2/on the field of safety road in China 3/under the social and psychological sciences approaches. Indeed, a search using ''China'' and ''safety road'' as keywords to look for in abstract, keywords, and title brings 19 publications as a results. The former one is dated from 2006! Then a search within results targeting ''two-wheelers'' brings only one paper2 ! Targeting ''cycle'' instead, only brings 9 additional papers. It informs that concerning dangerous driving behaviors including the ones of two-wheelers riders, research studies focus on three specific driving behaviors: helmet use, red-light running and speed behaviors. Then, to fulfil our goal for giving a definition to risky driving behaviors as two-wheelers, we opened up our search with no limitations to twowheelers but including also cars.

First, we refer to [START_REF] Xie | A social psychological approach to driving violations in two Chinese cities[END_REF] who relevantly point out that some Chinese cultural factors, personality factors and attitudinal factors as well might play a role on accident risk distinctively from other cultures. Additionally, they mention for example that some classical demographic factors have been shown to have a different weight or none compared to Western populations. In this paper the authors aim to deal with driving behaviors in two Chinese cities and their involvement with traffic accidents. These driving behaviors they focus on are first mentioned as "aberrant driving behaviors", then as "aggressive violations", as "driving violations", and finally "intentional driving violations". As no definition is provided these designations can be understood as interchangeable. Here considered traffic road violations are understood both as intentional and aggressive. For the purpose of their study, [START_REF] Xie | A social psychological approach to driving violations in two Chinese cities[END_REF] make use of the Manchester Driver Behaviour Questionnaire (DBQ) initially developed by [START_REF] Parker | Driving errors, driving violations and accident involvement[END_REF] for the Western drivers. Then one shall be stated that according to the items of this tool the risk as linked to traffic accident is here strictly operationalized as violations, lapses and errors. Then, references to the road's regulation are obvious. Based on the DBQ, the authors develop the Chinese Driving Questionnaire (CDQ). This covers culture-specific topics that emerged from interviews and literature review. Respondents express their agreement with 40 statements related to the importance of social hierarchy, the road safety measures, and attempts to escape the sanctions, the questioning of the lawful authority by some drivers who think first to their own interests rather than safety. Additionally to the development of the CDQ, the authors add to the DBQ an extended set of driving violations relevant in China. Then this extended version of the DBQ refines categories as errors and lapses, aggressive violations, lane-use, inattention errors, maintaining progression violations, and signalling of impatience. Having a closer look on these factor labels one can question why this idea of (intentional) aggression is only enlighten in this second violation factorial items group and not in another one. It appears as quite debatable for main of these items: ''Chase a driver who angered you'', ''Get involved in unofficial races'', ''Race away from traffic lights'', ''Overtake on the inside'', ''Break speed limit'', ''Drink and drive'', ''Show an aversion to other road users''. When even the DBQ itself is noticed as the ''DBQ aggressive violation scale'', we defend that those driving behaviors are over-interpreted like intentional aggression. Since contextualization of the driving situation often lacks in these items, it makes them imprecise. As an example ''Race away from traffic lights'' besides an intentional aggression can otherwise correspond to a judgmental/perception errors. Another example ''Break speed limit'' instead of a willingness to show one-self aggressive to another could respond to one sensation seeking need. In this regard, Xie and Parker themselves mention in discussion about results variability according to age of the sample, a study of sensation seeking in a Chinese sample by [START_REF] Wang | Test of sensation seeking in a Chinese sample[END_REF].

Otherwise it could be an overuse of the concept of aggressiveness as Shi et al. (2010) referring to Xie and Parker describe it as emotional instead. Finally, this above critical analysis brings us to defend a methodological point of view. Either intentional aggressiveness, ignorance, sensation seeking, or whatever personality, cognitive, social or cultural factors, any explanatory factors of traffic rules violations should be independently measure from the (risky) driving behaviors themselves. Confounding violations items to intentional aggression motives is a restrictive or bias approach that does not allow investigating on further explanatory factors.

More recently, Shi, Bai, Ying and Atchley (2010) that we mentioned before, drove a study among Chinese drivers as well (Beijing city). The authors aim to understand the nature of aberrant driving behaviors. Despite a lack of definition, as the authors refer to Reason et al. (1990) it is clear that these behaviors must be understood as violations (intentional) and errors (non intentional behaviors). They aim to compare among the new set of car drivers, selfreported driving behaviors of Chinese people with those that we know in the West. Rather than make use of currently used tools in Western countries, the authors highlight the need to develop a ''localized Chinese and standardized version of the DBQ'' to be applied everywhere in China. They acknowledge as an excellent innovation [START_REF] Xie | A social psychological approach to driving violations in two Chinese cities[END_REF] CDQ development as its combination with the DBQ for discovering new predictors. Nevertheless, they argue that these predictors may have changed in the last decade. Their point of view is that ''interpersonal network'' and ''social hierarchy'', which are the two most important factors in the CDQ are no longer valid for the new generation of Chinese drivers.

The authors also assume that the increased number of drivers who switch from bicycle to car should lead to the finding of more "self-willed" behaviour from these new drivers. Therefore, Shi and al. take the opportunity to develop an updated tool. All in all this is about 25 items of violations and errors. Six items from the extended DBQ [START_REF] Xie | A social psychological approach to driving violations in two Chinese cities[END_REF] and nine from the original DBQ (Reason et al. 1990) having been shown to be related to accidents are included. Ten new items are designed from prior interviews. Factor analysis reveals five factors: "Emotional Violation", "Risky Violation", "Distracted Error", "Self-willed violation" and "Inexperience Violation/Error". Interestingly for our purpose, except "inexperience Violation/Error", all these violation factors are assumed as deliberate, intentional behaviors. It appears to be debatable when reading some items lacking of contextualization to determine

The Asian Conference on the Social Sciences 2012 Official Conference Proceedings Osaka, Japan whether the behavior is intentional or not. The disparity of items for each factor blurs a tentative of categorization by the authors. Furthermore, errors are presupposed to be due to the distraction or from inexperience. But errors while driving are not that restricted. Fail to notice a pedestrian crossing can sometimes be due to the low cognitive conspicuity of pedestrians.

Finally, [START_REF] Cheng | Development of a Chinese motorcycle rider driving violation questionnaire[END_REF] seem to be the only one to focus on Motorcycle Chinese riders. They could develop for example an adaptation of the Motorcyclists' Riding Behavior Questionnaire (MRBQ, [START_REF] Özkan | Motorcycle accidents, rider behaviour, and psychological models[END_REF][START_REF] Elliott | Errors and violations in relation to motorcyclists' crash risk[END_REF]. Distinctively, the MRBQ is only dedicated to motorized two-wheelers when the DBQ, the CDQ and recently Shi et al. (2010) The intent is to assess the driving violations of Chinese motorcycle riders and evaluate its screening accuracy between accident-involved and accident-free motorcycle riders. Nevertheless, one could counter-argue that authors' goal is not yet fully reached here. Indeed, in China the idea of ''locally developed'' can suffer some significant regional differences, even cultural ones. In Cheng and Ng's paper, Chinese riders are clearly quoted as from mainland China, Taiwan, and Hong Kong. However the methodologies of the scale development and the test phases have been exclusively driven in Hong-Kong. First, a working group including a principal motorcycle driving instructor from the Hong Kong School of Motoring, a commercial motorcycle accident avoidance instructor, a high experienced licensed motorcycle rider, and two expert instructors have been involved for the CMRDV items generation. Then, a final sample of 680 Hong Kong riders of motorcycles with an engine capacity between 50 and 250 cc composed the population for the test of the full survey questionnaire. Therefore, the items of the CMRDV are designed in traditional characters and some of them are so typically Cantonese way of oral talking. That appears relevant despite the fact that official languages are English and Chinese (Mandarin or Putong hua), however Cantonese (or Guangdong hua) is acknowledged as the de facto official spoken variety of Chinese language in Hong Kong. To make it clear, 97% of the population in Hong Kong speaks Cantonese and it is the main variety of Chinese language used in education, broadcasting, government administration, legislature and judiciary as well as in daily social communication. However, in Mainland China Mandarin dominates. That makes non-negligible differences features in the choice of a tool for the measurement of driving behaviors. Additionally, it shall be mentioned some differences between Mainland China and its Special Administrative Regions regarding the level of traffic regulations, the advancement of the road infrastructure and regarding traffic directions. People drive on the left side in Hong-Kong and Macau while on the right side in Taiwan and Mainland China. Therefore it seems to us quite premature to reach the conclusion as claimed by the authors that the CMRDV can be applied to any Chinese Motorcycles riders driving behaviors. Besides, as usual in most of studies, Cheng and Ng here do not take into account the very wide diversity of motorcyclists in their data analysis and comments [START_REF] Mundutéguy | A Contribution to Situation Awareness Analysis: Understanding how mismatched expectations affect road safety[END_REF]. Indeed, their sample gathers riders of vehicles with engines between 50cc and 250cc which covers a range of differences regarding physical and

The Asian Conference on the Social Sciences 2012 Official Conference Proceedings Osaka, Japan dynamics properties. Electric two-wheelers themselves can be scooter or bike styles. But no data analyses are introduced under this distinctive feature. Finally, the authors do not include in their sample riders with less than three years of experience with two-wheelers and narrow to expert riders for the generation of items. Then one could put in question the representativeness of the results while two-wheelers new drivers have distinctive driving behaviors and a high accident record rate according to the Western literature. Furthermore it has been shown a higher risk for young motorcyclists for getting involved in accident and moreover those of high severity [START_REF] Yannis | Driver age and vehicle size effects on fault and severity in young motorcyclists accidents[END_REF][START_REF] Nakahara | Temporal distribution of motorcyclist injuries and risk of fatalities in relation to age, helmet use, and riding while intoxicated in Khon Kaen, Thailand[END_REF][START_REF] Kraus | Trends in deaths due to motorcycle crashes and risk factors in injury collisions[END_REF][START_REF] Huang | A literature review of motorcycle collisions[END_REF][START_REF] Mullin | Increasing age and experience: are both protective against motorcycle injury? A case-control study[END_REF]Zambon, & Hasselberg, 2006a). Even if driving experience is also a significant factor to explain accident [START_REF] Sexton | The accident risk of motorcyclists[END_REF] young age comes first [START_REF] Taylor | Factors affecting the accident liability of motorcyclists -A multivariate analysis of survey data (RR270)[END_REF]. [START_REF] Rutter | Age and experience in motorcycling safety[END_REF] give as an example one willingness among young people to break the law and violate rules of the road traffic.

Towards a more comprehensive definition of risky driving behaviour

Our main statement is that there is currently no available definition about what is a risky or dangerous driving behaviour as two-wheelers, both for Chinese and Western studies.

Generally, literature about driving behaviors as linked to road accidents does not provide a formal definition. Even more, there are no standard designations between different research papers or within one research paper itself. ''Aberrant driving behavior'', ''intentional driving violation'', ''interpersonal aggressive violation'' are alternatively and even confoundedly mentioned to designate driving behaviours as linked to accidents. Our observation is consistent with [START_REF] Dula | Risky, aggressive, or emotional driving: Addressing the need for consistent communication in research[END_REF] systematic content analysis. Following the objective to clarify imprecise lines definitions of what are dangerous driving behaviours, the authors suggest that dangerous driving behaviors embrace those that put in danger or potentially in danger the others. They identify in the literature three main categories of dangerous driving behaviors: (a) intentional acts of bodily and/or psychological aggression toward other drivers, passengers, and/or pedestrians (acts may be physical, gestural, and/or verbal in nature); (b) negative emotions felt while driving (including frustration, anger and rage, but which might also include sadness, frustration, dejection, jealousy, etc.); and (c) risk-taking behaviors defined as dangerous behaviours performed without intent to harm self or others. This third class includes such behaviours as speeding, general tailgating, running red lights, weaving through traffic, manoeuvring without signalling, and frequent lane changing. Following our goal to measure risky driving behaviors of both electric-two wheelers and motorized-two wheelers in the Chinese road context, we suggest to be based on Dula and Geller third level definition of dangerous behaviors. One driving behavior is risky if it (potentially) puts in danger oneself or endangers others in the traffic system, out of self/hetero aggressiveness and out of negative emotions felt while driving.We think that adopting this definition will allow to go beyond the violations/aggressive intention and errors' usual reading approaches of aberrant driving behaviours related to accidents, moreover when it is debatable that these last ones can be seized through self-reported method. The interest is to explore further explanatory factors of these dangerous driving behaviours including aggression intention as it can be measured separately, as other explanatory factors. Already some individual, personality trait factors have been explored as the well-known sensation seeking trait, but social psychological and cultural factors still need to be investigated. We demonstrated there is currently no satisfying available measure that gathers the features of 1/ measuring risky driving behaviors of twowheelers without neglecting the specificities of the vehicle driven (type and categories of twowheelers for example), 2/ being ''locally'' adapted to the Mainland China traffic road context and 3/ therefore in Chinese language (meaning Mandarin). A continuation then should be the development of this still missing self-reported questionnaire according to the definition of

The Asian Conference on the Social Sciences 2012 Official Conference Proceedings Osaka, Japan risky driving behaviors we introduced. Plus, according to the peculiarities of the Chinese road context (as roads shared between drivers with and without driving license and, recent modal shift from one type -often non-motorized-of vehicles to another motorized ones for drivers of various ages), we think such a tool should avoid any driving regulations references in the design of risk-taking behaviours items.

Conclusion

Generally speaking aspects related to human factors in road safety issues are still lacking in China. The approach we suggest is valuable as it allows going beyond a technical and mechanical vision on the general question of urban mobility. More specifically, one aim is to contribute improving knowledge about specific risk and vulnerability factors of E2W riders and M2W riders respectively in interactions with car users. Our critical review introduced in this paper provides a first step. We defend the need to take into account the specificities of the local context and the evolution of transport mobility in the investigation of driving behaviors. Finally, the development of an ad hoc tool to measure risky driving behaviors dedicated to the two-wheelers in China shall be later quickly adapted with few modifications to others developing countries road context in Asia and then in South America. Eventually, new knowledge that would be developed under this approach should benefit to European countries.

As the environmental protection motives are increasing, so are the use of M2W and E2W.

  questionnaire focus on car drivers. The MRBQ reveals that motorcycle riders behaviours respond to a five-factor structured model: traffic errors, control errors, speed violations, performance of stunts and use of safety equipment. But the MRBQ as among other current tools has been developed in Western countries and mainly in English language. Cheng and Ng share[START_REF] Xie | A social psychological approach to driving violations in two Chinese cities[END_REF] asShi et al. (2010) 's point of view (2002) that contextual factors in terms of social, cultural, and traffic environments could also influence the driving behavior of Chinese drivers, especially their tendency to violate. Hence, they argued the need for an ad hoc measure to evaluate driving violations of motorcycles in China. They aim to ''locally develop'' a questionnaire. Finally, they design the Chinese Motorcycle Rider Driving Violation (CMRDV) scale. It consists of 19 items.

Changxu Wu, Lin Yao, Kan Zhang, The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study, Accident Analysis & Prevention, Available online 2 July 2011, ISSN 0001-4575, 10.1016/j.aap.2011.06.001. (http://www.sciencedirect.com/science/article/pii/S0001457511001679)The Asian Conference on the Social Sciences 2012 Official Conference Proceedings Osaka, Japan

This research work is supported by the Development Research Center of the Shanghai Municipal People's Government. The authors thank Sandi Reis for her help in revising English in this document.


Edgar L Schiebel

Marianne Hörlesberger

Dominique Besagni

Ivana Roche

Claire François

Interrelationships of technologies -the concept of home technology terms

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Interrelationships of technologies -the concept of home technology terms

Edgar Schiebel (1) , Marianne Hörlesberger (1) Dominique Besagni (2) , Ivana Roche (2) , Claire François (2) edgar.schiebel@ait.ac.at, marianne.hoerlesberger@ait.ac.at dominique.besagni@inist.fr, ivana.roche@inist.fr, claire.francois@inist.fr (1) AIT Austrian Institute of Technology GmbH, Tech Gate Vienna, Donau-City-Strasse 1, 1220 Vienna, Austria (2) INIST -CNRS, 2 allée du Parc de Brabois, CS 10310, 54519 Vandoeuvre-les-Nancy cedex, France

Background

Nowadays technological progress and industrial innovation very often are induced by interdisciplinary research. As an example, applications of nanotechnology in biomedicine [START_REF] Rojas-Chapana | Multi-walled carbon nanotubes and metallic nanoparticles and their application in biomedicine[END_REF] combine research in many disciplines like physics, chemistry, microbiology, cell biology, and material science. In the cited case, all disciplines contribute to the design, synthesis and fabrication of biocompatible devices at the nanometer scale. Only little research has been performed to analyze interdisciplinary interrelation of technologies on the basis of research topics. The work of Morillo F., Bordons M., Gomez I. [START_REF] Morillo | Interdisciplinarity in science: A tentative typology of disciplines and research areas[END_REF] aimed to establish a tentative typology of disciplines by their degree of interdisciplinary research. They measured interdisciplinarity with indicators based on the multi-assignation of journals in subject categories, but not on the level of research issues like nanotubes, thin layers, etc.

In this contribution, we introduce a methodology to analyze the interrelationship of technologies on the basis of keywords present in bibliographic records. We consider these terms are a representation of the research topics diffused by means of their publication in the scientific literature. Key questions are: How can we demonstrate the interrelations between technologies on the basis of research items? Do keywords that occur most often in articles assigned to a technology reflect research issues and do they show interrelationships?

Methodology

Scientific literature consists of very well structured documents and most of them are represented under the form of bibliographic records and stored in databases like Web of Science, Scopus, PASCAL or others. For our analysis we used the PASCAL database. Technologies were defined on the basis of categories the database producer uses for the classification of the bibliographic records. In the project PromTech (PROMTECH, 2007), we defined 45 promising emerging technological fields. Later we published a method to assign the so-called home technology status to keywords: for each keyword, the technological field with the highest relative number of papers where a keyword occurred was addressed as its home technology. Then it was very easy to extract all keywords of the bibliographic records related to each technological field and to tag the corresponding home technology [START_REF] Besagni | Les émergences technologiques dans le domaine des dispositifs optoélectroniques : identification et caractérisation[END_REF]. In this presentation, we used the common keywords across the different technological fields to show interrelations on the basis of their home technology terms.

IAMOT 2011 -SCHETAL 1

Results

As a first result we could see that words occur with different probabilities in articles assigned to different technological field. For example "nanostructured materials" occurred with a probability of 0.61 in the field of molecular electronics and 0.31 in biotechnology respectively and with smaller values in 19 other fields. In consequence, "nanostructured materials" where tagged with molecular electronics as home technology, because of the highest probability of its occurrence in this technological field.

Most important technological fields and their home technology terms with a high relative occurrence in bibliographic records were: surface treatment of metals -surface treatments, coatings, non metal coating, metal coating, hot spraying; ceramics: experimental study, composite material, manufacturing, oxide ceramics, scanning electron microscopy; optoelectronics: optoelectronic device, light emitting diodes, light emitting diode, photodetector, organic light emitting diodes.

The interrelationship between technological fields on the basis of keywords was represented by a square matrix: rows and columns are the considered technological fields and the cell contains the number of home technology terms that occur in the bibliographic records of the corresponding technologies. For each technology the row represents the number of home technology terms from other technologies in its records. The sum of a row is called the import of terms and the sum of the column is called the export of terms. We found the highest number of exported terms in the following fields: mechanical working of metals; mechanical engineering, general; electrical energy and ceramics, and we observed the highest number of imported terms in mechanical engineering, general; mechanical transmissions; optoelectronics; surface treatment of metals and molecular electronics. The highest trade balance (export/import) could be seen at cristallisation, lixivation, separation; electrical engineering, general; ecology (animals, plants, cells); waste treatment; physical impact on biological material; electrical machines and the lowest at the following technology fields: mechanical engineering, general; molecular electronics; biotechnology; mechanical transmissions and optoelectronics.

We took the very innovative field of optoelectronics to examine the trade balance in more detail. The profile of this technology showed that magnetics, electrical energy, electronic circuits, molecular electronics and transistors are the technologies with the highest interrelation with optoelectronics: its most important trade partners. Bibliographic record in the field of molecular electronics show the highest number of terms with optoelectronics as a home technology. It is remarkable that there is no technology field with a positive trade balance for optoelectronics. In order to produce other interpretation of the content of the term flows coming in or going out the optoelectronic field, we employ a clustering approach to study, on one hand, the set of bibliographic records not related to the considered field and where we find at least one home technology term coming from optoelectronics and, on the other hand, the set of records related to the considered field and containing at least one home technology term coming from the other technological fields.

Conclusions

In our analysis we found that a database with a well-defined domain classification scheme and carefully assigned keywords is a very important source for the analysis of interrelations between IAMOT 2011 -SCHETAL IAMOT 2011 -SCHETAL 3 technological fields on the basis of terms. The concept of home technology terms gives us the possibility to assign terms to technologies in a pragmatic way. The findings show that the assigned terms make a lot of sense, with the exception of terms that occur very often like experimental study or terms that are not standardized. Taking sums of rows and columns the interrelation matrix reveals general views insight of technological fields concerning the source and drains distance or the so-called trade balance of technological fields. We could not reveal that basic oriented technologies have higher positive trade balance than applied research on a first glance. The trade balance profile gives a good view insight of single technologies and the most interrelated partner technologies. Driving technological progress in industry it is very important to know about important knowledge from related technologies to use one technology intensively in product or process innovation. On the other hand the export characteristics give an idea about the usability of knowledge in other areas an probably opens the dissemination of knowledge in other areas.


Pascal Cuxac
email: pascal.cuxac@inist.fr

Jean-Charles Lamirel
email: jean-charles.lamirel@loria.fr

Valerie Bonvallot
email: valerie.bonvallot@inist.fr

Efficient supervised and semi-supervised approaches for affiliations disambiguation

The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... So the search of names of persons or of organization is difficult, a single name can appear in different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way, considers that we have a training corpus, and uses a Naive Bayesian model. The second way assumes that we have not resource learning, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and are already partially applied in a scientific survey department. However, we aware that our approach may have limitations: we can't process efficiently highly unbalanced data but solutions are possible for future developments.

Introduction

In bibliographic databases, affiliations of authors are of paramount importance. Hence, they permit to the laboratories or institutes to get national and even international visibility, as well as they consequently provide authors with scientific caution. We cannot discuss the issue of affiliations without talking of "Shanghai ranking" which aims at evaluating universities. Our purpose here is not to feed up the controversy (Van Raan 2005) [START_REF] Liu | Academic ranking of world universities using scientometrics:A comment to the Fatal Attraction[END_REF] , but to point out that the treatment of affiliation plays an important role in the calculation of universities "performance". Moed [START_REF] Moed | Citation Analysis in Research Evaluation[END_REF]) reports some problems with author's names and also institutions: "Authors from the same institution, or even from the same department, may not indicate their institutional affiliations in the same way". Depending on the country, it is not always clear how to name a laboratory with respect to its supervisory authorities. The affiliation is also important information to disambiguate author names in bibliographic databases. In this regard Wang notes: as the amount of available information increases, problem caused by misspelling, spelling difference, and name or affiliation change also become worse." [START_REF] Wang | A boosted-trees method for name disambiguation[END_REF]. A standardization of data in bibliographic databases is necessary to carry out informetrics studies, but it is not a trivial task: the practice, intentional or otherwise, of omitting institutional affiliations, or giving incomplete or wrong information is not uncommon [START_REF] Hood | Informetric studies using databases: Opportunities and challenges[END_REF]. This paper proposes an approach based on Nave Bayes learning method and overlapping clustering. It is structured as follows: section 2 summarizes related works and identifies problems. Section 3 describes our approach firstly with supervised learning method and then with semi-supervised method. Next section 4 reports experiments and results. Section 5 conclude and discusses future work.

State of the art and discussion:

As part of bibliometric analyzes, the authors affiliations can produce statistics by laboratories as well as by institutes or universities. However, such analyses often face with problems of high variability and heterogeneity of naming: a single laboratory name may thus appear in several different ways if the authors use different abbreviations, incomplete or misspelled words (typing mistakes, spelling...). In addition, some universities may have several names (for example University Pierre and Marie Curie = University Paris VI). This problem is known for long but still persist nowadays. In the 1990s, De Bruin et al. [START_REF] De Bruin | The unification of addresses in scientific publications[END_REF], point out the problem of variability of the author addresses in databases such as SCI (Science Citation Index). They highlight the case of countries like Germany or France where the heterogeneity of data is particularly important. Zitt [START_REF] Zitt | Challenges for scientometric indicators: data demining, knowledge-flow measurements and diversity issues[END_REF] emphasizes the importance of data standardization (author names, affiliations) with special consideration to countries like France where affiliations overlap is important (for example one laboratory may have a University affiliation and CNRS affiliation). For many bibliometrics analysis, the unification of institutional addresses is an essential task, often boring, to be carried out prior to any study ( [START_REF] Bourke | Standards issues in a national bibliometric database: The Australian case[END_REF], [START_REF] Osareh | A comparison of Iranian scientific publications in the SCI: 1985-1989 and 1990-1994[END_REF]).

For solving the problem, De Bruin [START_REF] De Bruin | The unification of addresses in scientific publications[END_REF] propose to deal separately with all the words belonging to affiliations and to use in a second step a classification strategy to unify all possible variations of the different words. In a later work [START_REF] De Bruin | Delimitation of scientific subfields using cog nitive words from corporate addresses in scientific publications[END_REF], the same authors use a "single-link clustering" approach to delineate different areas of science on the basis of affiliations. French et al. [START_REF] French | Using Clustering Strategies for Creating Authority Files[END_REF] supply an authority file after a cleaning step (name of country, zip codes, states, expansion of abbreviations, acronyms ...) and then use a clustering based on an "edit distance". Recent approaches also address the problem by the single use of NLP methods, like in [START_REF] Galvez | The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)[END_REF]. The terms data cleaning, data scrubbing, data standardization, data disambiguation, data homogenization and also entity resolution are used to refer to the tasks of transforming source data into clean or normalized data for loading in databases or linking with other data sets or computing statistical indexes (bibliometric analysis for example). If as we have seen, these problems are essential in bibliometrics, they are also recurrent in many other areas where the heterogeneity of data is an important problem. This can be within a file or a database, but also when combining information from heterogeneous sources (e.g. record linkage). Erhard Rahm [START_REF] Rahm | Data Cleaning: Problems and Current Approaches[END_REF] classifies data quality problems encountered in data cleaning tasks (fig. 1). In our case, we can assimilate the multi-source to a bibliographic database reporting papers from journals of different publishers. The approach presented by Fellegi and Sunter [START_REF] Fellegi | A Theory for Record Linkage[END_REF] is the reference widely used in record linkage to identify the same entities in different datafiles. It is based on calculating similarity scores between two records. Generalizations of this method for more than two data files have been recently proposed [START_REF] Sadinle | Approaches to Multiple Record Linkage[END_REF] (Sadinle and Frienberg 2012). Ventura [START_REF] Ventura | Methods Matter: Revamping Inventor Disambiguation Algorithms with Classification Models and Labeled Inventor Records[END_REF] mixes disambiguation and record linkage algorithms, using Random Forest, and applies this methodology to a case study of inventors of USPTO patents in the optoelectronics field. In this context of record linkage, Churches [START_REF] Churches | Preparation of name and address data for record linkage using hidden Markov models[END_REF] shows that probabilistic Hidden Markov models for pre-processing data (names and address), give accurate results with complex data such as residential addresses.

When training data is available many of this studies use metrics to measure similarities between data such that Jaccard, soft-TF-IDF and mainly edit distance [START_REF] Huang | Efficient name disambiguation for large-scale databases[END_REF]. In his review paper, Bilenko compares the performance of various matching methods and concludes than learned affine edit distance can outperform others with EM techniques [START_REF] Bilenko | Adaptive name matching in information integration[END_REF]). However, probabilistic approaches have been proposed by several authors like e.g. Carayol [START_REF] Carayol | Whos Who in Patents. A Bayesian approach[END_REF], which proposes a bayesian approach to treat the who's who problem in European patents. He arises the transitivity issue that we'll discuss in the conclusion. Conversely, some authors propose approaches based only on unsupervised algorithms. This is the case of Niu (Niu & Wu and Shi 2006) which presents a new method for entity disambiguation using textual information and interobject relationship to evaluate similarity. Entities are author names, and interobject relationship is related to coauthorship network. The novel methodology developed by Ashwani for unification of authors names use web mining to get full names and find publications pages [START_REF] Aswani | Mining Information for Instance Unification[END_REF]). As we see, applications are numerous, be it in bibliographic data bases, in data ware-houses, in multiple record linkage, in semantic web, or also in semantic digital libraries as shown in [START_REF] Jiang | Affiliation disambiguation for constructing semantic digital libraries[END_REF]. To conclude lets quote the standardization actions conducted through the International Standard Name Identifier (ISNI) and the Virtual International Authority File (VIAF). The aim of ISNI (http://www.isni.org) is to identify international public identities of individuals or communities and to provide tools for disambiguation. VIAF (http://viaf.org/) is a research project of OCLC (Online Computer Library Center) which aims to align lists of authorities (including proper names) to form an international reference database. Do not forget to mention the Oyster software: is an open-source software developed by Talburt in the ERIQ Research Center (Entity Resolution and Information Quality at the University of Arkansas). OYSTER (Open sYSTem Entity Resolution) is an entities resolution system using XML scripts [START_REF] Zhou | OYSTER: A Tool for Entity Resolution in Health Information Exchange[END_REF].

Our approach:

We present two different methods for affiliation disambiguation: first, a supervised learning approach relying on manually analyzed reference dataset, and in a further step, a semi-supervised approach whose goal is to get rid of a training corpus for cases where no validation data are available.

Supervised learning method:

Supervised learning methods allow to produce rules from a learning corpus, generalizing what they could learn to the unknown inputs. In the literature there are many methods such as SVM, Rocchio, K-NN, Naive Bayes, HMM, Decision Trees...Our supervised approach is based on a Naive Bayes (NB) algorithm. Let C a set of affiliations classes C = {c 1 , c 2 , ..., c k }, the problem is to assign to an affiliation, one of these categories. Using a set of N labeled affiliations {(a i , c i ), 1 ≤ i ≤ N }, we construct a classification function F : A → C with A = set of all affiliations.

Bayes' formula for a given affiliation a allows to calculate its probability of belonging to a particular class c : Despite of the two main known defects of such method, that are, its ignorance of the order of the words and its ground hypothesis that words are independent conditionally to their class membership, its represents a good alternative for solving our problem. The results obtained by this implementation of Bayes theorem are valid and demonstrated by [START_REF] Hand | Idiots BayesNot So Stupid After All[END_REF]. Hence, Domingos et al [START_REF] Domingos | Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier[END_REF] formerly showed that the misclassification error of NB is minimized as compared to other methods.

Method for semi-supervised classification:

Whenever no a priori knowledge is available, we applied a semi-supervised methodology. In this case, we firstly process by the use of an overlapping clustering method. The exploited clustering technique is the axial k-means algorithm (a variant of the k-means method proposed by Lelu, [START_REF] Lelu | Modles neuronaux pour l'analyse de donnes documentaires et textuelles[END_REF], which allows to produce clusters presenting particular characteristics:

• they can overlap because the clustering method allows an object or a variable to belong to more than one cluster;

• the constituting elements of a cluster, objects and variables, are ranked by decreasing similarity with the cluster ideal type.

Figure 2: Semi-supervised classification schema.

In the second step, we only retain the major representatives of the classes, which are the documents that have the highest values of projection on the axes representing the classes.

These documents will then be used as the training corpus. Then, we calculate the most representative words of each class and we use each of these groups to query the web (via Google). The site that represents the most relevant answer of the search engine for a given class is used to label the class. If necessary, we proceed to a further step of classes merging.

In the last phase, we train the NB method with the corpus defined in the clustering phase and labeled by the class names extracted from the web. The testing process is achieved on the complementary corpus of documents eliminated after the clustering phase.

Figure 2 summarizes in a schema, our semi-supervised approach with the three steps: softclustering, clusters labelling and clusters fusion, and finally the Naive Bayes classification.

Experiments

In this section we present our results obtained with three corpora and the two methods presented previously.

Datasets:

We used three different datasets: a first dataset of 10 057 French affiliations (noted hereafter A1), a second small dataset of 150 Lorraine affiliations (region of France) (noted A2), and a last dataset of 2266 French affiliations extracted from WOS and SCI (noted A3). All those datasets have been preprocessed by splitting the affiliations into words using space as separator (any punctuation, including dash, is firstly removed). Given the difficulty we had to cut affiliations into words in the datasets A1 and A2 (dash, sometimes missing space ...), we have then used a second splitting technique based on n-grams after converting affiliations in string without spaces or punctuation. Supervised learning is applied on dataset A1, and semi-supervised one is applied on the two other datasets.

Measures of performance:

The results are evaluated in terms of recall, precision, F-measure, because we know a priori classes of all affiliations.

Recall : R = T P (T P + F N ) P recision : P = T P (T P + F P ) Fmeasure :

F = 2 * P * R (P + R)
where TP, FP, FN, mean number of true-positives, number of false-positives, and number of false-negatives, respectively.

Supervised learning:

The dataset A1 was split into training dataset and test dataset successively represented by the words of affiliations and by the n-grams. The figure 4 shows the distribution of the affiliations in the 53 resulting classes (test + train) and thus highlights the fact that the resulting classification is highly unbalanced. The classification results on the dataset A1 are presented in Table 1. Because of the problem of individualization of words affiliations in dataset A1, the results obtained with n-grams appear slightly better, with an optimum of Recall of 0.92. It should also be noted that a systematic lookup of the analysis results of NB which got a very high probability, whilst being in contradiction with the expected (i.e. human labeled) results, permits us to prove that the manual labeling of the test corpus was sometimes wrong (the model was giving the right answer in all that cases!).

Semi-supervised classification:

The datasets A2 and A3 were used for this experiment. The said datasets are split into train and test with the methodology described in the former section detailing our approach. The figure 5 reports the distribution of affiliations in the resulting classes (A2: 19 classes; A3: 10 classes). It highlights that the smaller dataset (A2) is highly unbalanced, whilst the bigger one (A3) is homogeneous. This distribution is obviously intentional, in order to be aware the impact of data distribution on our results. As detailed in table 3, the results are very good for the dataset A3, but significantly lower for the corpus A2, where recall is average. This is due to the large number of classes as compared to the small size of the corpus and to the strong imbalance of these classes. Indeed, in this case the exploited clustering method is becoming blind to small classes.

Conclusions and discussions

The results we obtained with our approach for affiliations disambiguation are very encouraging, both in the supervised learning context and in the semi-supervised one. Our experiments also permit us to show that our method provide a significant assistance for correcting the results of human labeled affiliations. However, it is clear that we must practice more experiments to conclude on an overall relevance of the methodology. Hence, there still remain some weaknesses in our methodology, mainly related to the exploited clustering method in the case of very unbalanced classes. We thus plan to conduct tests with other clustering methods and implement data balancing techniques. Another important point would be to exploit a learning method that should be able to learn with character strings of variable length. It would be necessary to develop an automatic method that could highlight the conflicting cases. Future works should also take into account the xml structure to consider separately the cities, street names, the laboratories names... Another way is to consider the scientific content of documents, such as titles and abstracts of articles published. Once these documents indexed each address would be represented by a vector of words (describing research activities) allowing probably a more relevant classification.

The study of transitivity can perhaps permit to detect false positive or false negative results and thereby isolate the results to be verified. We also propose to compare our results with those obtained using the Oyster software.

Figure 1 :

 1 Figure 1: Data quality in data cleaning tasks figure from (Rahm and Do 2000)

P

  (c | a) = P (a | c) * P (c) P (a) with P (c | a) = probability of c given a, P (a | c) = probability of a given c, P (a) P (c) = respectively probability of a and probability of c If we simplify by assuming that labels are randomly distributed (are not dependent on the length of the affiliation or the position within the affiliation), then the probability of affiliation a given a class c, is P (a | c) = P (w i | c) with w i = the i-th word of a. then, by applying the Bayes rule we can classify an affiliation in a class c: c = arg max P (a | c)P (c) = arg max P (c | a)

Figure 3 :

 3 Figure 3: Data sample with various forms of addresses

Figure 4 :

 4 Figure 4: Distribution of affiliations in resulting classes (dataset A1)

Figure 5 :

 5 Figure 5: Distribution of affiliation in resulting classes (datasets A2 and A3)

Table 1 :

 1 R, P and F values for corpus A1.

		Recall Precision F-measure
	N-gram 0.92	0.94	0.93
	Words	0.81	0.88	0.85

Table 2 :

 2 : Results of K-means R, P and F values for corpora A2 and A3.With this kind of data, the results of K-means are not performing (table2). It is probably due to the bad representation of data. As we discuss in the last section of this paper, a vector space representation taking in account the scientific content linked to each addresses should improve the clustering result.

	Kma only Recall Precision F-measure
	Corpus A2 0.44	0.81	0.55
	Corpus A3 0.40	0.95	0.54

Table 3 :

 3 : R, P and F values for corpora A2 and A3.

	Recall Precision F-measure
	Corpus A2 0.79	0.76	0.73
	Corpus A3 0.98	0.97	0.97