Alexandros Georgiou
email: georgiua@ceid.upatras.gr

Andreas Kanavos
email: kanavos@ceid.upatras.gr

Christos Makris
email: makri@ceid.upatras.gr

Finding Influential Users in Twitter Using Cluster-Based Fusion Methods of Result Lists

Keywords: Cluster-based Methods, Influential Users, List Fusion Methods, Social Networks, Temporal Features, Twitter, Web Mining

The topic of the paper is to present a novel methodology in order to characterize influential users, such as members of Twitter, as they arise in social networks. The novelty of our approach lies in the fact that we incorporate a set of features for characterizing social media authors, including both nodal and topical metrics, along with new features concerning temporal aspects of user participation on the topic. We also take advantage of cluster-based fusion techniques for retrieved result lists for the ranking of top influential users.

Introduction

The task of finding the most influential users in an online social networking environment has gained a great amount of attention in recent years. Special focus is given on social networking platforms called microblogging platforms. These platforms allow only short messages to be published (usually ranging in a few hundred characters), a fact that raises a wide range of problems against text-based information retrieval techniques.

A prominent example of such microblogging platforms is the Twitter online social network which only allows messages of 140 characters maximum. Twitter is an internationally famous social networking platform with hundreds of millions of active users. Each user can create an unlimited circle of affiliated users to whom they can publish updates (called tweets). Users are additionally presented with a list of tweets by their affiliated users sorted by the latest, called timeline. User relations in Twitter are not necessarily reciprocal: user a may follow user b, without user b having to authorize it or to follow back. When user b chooses to follow back user a, users a and b can be called friends. The Twitter platform allows users to repost content that they find interesting, an action called retweet which is signified by the characters "RT" following the original content producer's username. A user is able to directly mention another user with the character "@" followed by the mentioned user's username. Topics of discussion can be initiated by any user and organized around user-specified keywords, called hashtags and signified by the character "#" followed by the desired keyword.

Recent studies [START_REF] Huberman | Social networks that matter: Twitter under the microscope[END_REF], [START_REF] Wu | Who says what to whom on twitter[END_REF] have shown that groups of intermediate level users act as propagating nodes for the information flow on such networks, and users rely preferably on other users or special purpose user accounts for their information about certain topics. Taking into account the spread of such online social networks and the impact that they have on many aspects of everyday social, economic and political reality, identifying users with high influence around specified topics is of crucial importance for social media marketing agents, governments, policy makers, celebrities and communities.

The rest of the paper is structured as follows. Section 2 presents background topics while Section 3 presents our methodology followed and the system developed. In Section 4, details of the implementation of the system as well as the evaluation study conducted and the results gathered on both the sentiment analysis topic and the community detection topic are presented. Finally, Section 5 concludes our work and presents directions for future research.

Related Work

Recently, the identification of topical (or influential) authorities in microblogging has gained a lot of attention. In [START_REF] Pal | Identifying topical authorities in microblogs[END_REF], the challenge of finding the most interesting and authoritative authors for any given topic in Twitter is reported. Authors provide a set of features for characterizing any social media author, including both nodal and topical metrics. Their experimental results show that a probabilistic clustering over a feature space, followed by a within-cluster ranking procedure, can yield to a final list of top authors for a given topic. More specifically, their technique uses a Gaussian Mixture Model to cluster users into two clusters over their feature space as the aim is to reduce the size of the target cluster; that is the cluster containing the most authoritative users. In addition in [START_REF] Kafeza | Identifying personality-based communities in social networks[END_REF] and [START_REF] Kafeza | T-pice: Twitter personality based influential communities extraction system[END_REF], the notion of influence from users to networks is extended and in following, personality as a key characteristic for identifying influential networks is considered. The system creates influential communities in a Twitter network graph by considering user personalities where an existing modularity-based community detection algorithm is used. At a later point, the insertion of a pre-processing step that eliminates graph edges based on user personality is utilized. Moreover in [START_REF] Kanavos | Emotional community detection in social networks[END_REF], an efficient and innovative methodology for community detection that will also leverage users' behavior on emotional level is introduced.

Interesting is the work presented in [START_REF] Weng | Twitterrank: Finding topic-sensitive influential twitterers[END_REF], which employs Latent Dirichlet Allocation and a variant of the PageRank algorithm that clusters according to topics and finds the authorities of each topic; the proposed metric is called TwitterRank. The field of analysis in social networks is related to link analysis in the web with cornerstone the analysis of the significance of web pages in Google using the PageRank citation metric [START_REF] Page | The pagerank citation ranking: Bringing order to the web[END_REF], the HITS algorithm proposed by Kleinberg [START_REF] Kleinberg | Authoritative sources in a hyperlinked environment[END_REF] as well as their numerous variants discussed in [START_REF] Langville | Google's PageRank and Beyond: The Science of Search Engine Rankings[END_REF]. PageRank employs a simple metric based on the importance of the incoming links while HITS uses two metrics emphasizing the dual role of a web page as a hub and as an authority for information.

Historically, the above as well as other approaches and techniques have been harnessed throughout microblogging areas. In [START_REF] Guo | Tapping on the potential of q&a community by recommending answer providers[END_REF], an overall generative model for questions and answers in community-based Question Answering (cQA) services is developed, which is then altered to obtain a novel computationally tractable Bayesian network model. Initially, they seek to discover latent topics in the content of questions as well as the associated answers, and latent topic interests of users. Then, they recommend answer providers for new questions according to discovered topics as well as term-level information of queries and users. What is more, in [START_REF] Morris | Tweeting is believing?: Understanding microblog credibility perceptions[END_REF], authors present an investigation dealing with user perceptions about credibility tweets, where they examined key elements of the information interface for their impact on credibility judgements. Their results indicate that users had difficulty determining the truthfulness of content and that their judgement was clouded and often based on heuristics (e.g. if a post has been retweeted) and biased systematically (e.g. topically-related user names seen as more credible).

Furthermore, the similar problem though in other platform (e.g. in in Yahoo! Answers) was addressed in [START_REF] Bouguessa | Identifying authoritative actors in questionanswering forums: The case of yahoo! answers[END_REF]. Their method automatically discriminates between authoritative and non-authoritative users through modeling the authority scores of users as a mixture of gamma distributions. The number of components in the mixture is estimated by the Bayesian Information Criterion (BIC) while the parameters of each component are estimated using the Expectation-Maximization (EM) algorithm. Concerning Yahoo! Answers, authors in [START_REF] Agichtein | Finding high-quality content in social media[END_REF] investigated methods for exploiting specific community feedback so as to automatically identify high quality content. More in detail, a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, is proposed and the experiments show an accurate separation of high-quality items from the rest, non-notable.

Finally, relative study with the current one is [START_REF] Anderson | Effects of user similarity in social media[END_REF] by Anderson et al. in which it is investigated whether similarity in the characteristics of two users can affect the evaluation that one user provides to another. They analyze this problem under a range of natural similarity measures, demonstrating how the interaction between likeness and status can produce strong effects. Among these measures is a resemblance of interests using a distance metric capturing overlap in the types of content that users produce, as well as a similarity of social ties using a measure of the overlap in the sets of people they evaluated.

3 System Description

Modular Architecture

In the social media mining system we developed, the most authoritative users per topic are identified based on a variety of features that combine the quality of content they provide. Text similarity measures, social impact through retweets, ability to spike conversations considering the content provided (through conversational tweets), social graph relations and time-related variables measuring frequency and timezone span consist important characteristics as well.

Our system architecture consists of the following modules:

-A Twitter access module: Twitter database is accessed through Twitter API by this module, using the Twitter4j Java library for Twitter application development. This module receives topic name (#hashtag) as input, and returns user tweets from the specific topic as well as active user data and social graph relations from the total Twitter social graph.

-A Parser module: Output from the Twitter access module is parsed to create appropriate username searchable hashmaps which include all tweets, social graph data and time-related data. This stage is necessary as a preparation for the feature extraction process. -Feature extraction module: Hashmaps containing username -tweet set pairs are given as input from the Parser module. Numbers of original tweets, retweets, conversational tweets are counted, social graph relations are measured, posting frequency for each user is reported and tweets are distributed into four 8-hour time zones (morning, noon, evening, night) based on standard Twitter timestamps. These counts and measures are later combined to create the list of features for every user who participates in the specific topic. Hashmaps are restructured to contain username -feature value pairs. -A Clustering module: the set of username -feature values hashmaps is given as input in a module responsible for the clustering algorithms. Using Fuzzy C-Means, data clusters are created. -A Ranking module: Different types of ranking techniques are compared at the clustered user data. Gaussian ranking used by [START_REF] Pal | Identifying topical authorities in microblogs[END_REF] is tested against a method described in [START_REF] Khudyak Kozorovitsky | Cluster-based fusion of retrieved lists[END_REF].

This system operates nearly on the fly, in the sense that database read-write operations are used only for back-tracking reasons and result storage. Since the data size of specific topics is average and Twitter outputs its content in JSON form, an average computer system is able to execute hashmap counts and feature extraction in memory. There is an open window for parallelization at this point, discussed in Section 5. Direct access to the Twitter dataset queried by topic was used, through the requests documented in the Twitter API. Topic is user-defined at the beginning of the execution, but the Twitter API presents limitations on the maximum data transactions per hour.

Feature Extraction

This subsection describes the set of features we inherited from [START_REF] Pal | Identifying topical authorities in microblogs[END_REF] (named "Basic Features") and our contribution to the feature set, which is named "Time-based Features".

Basic Features

User features are extracted by calculating and combining different measures, as proposed in [START_REF] Pal | Identifying topical authorities in microblogs[END_REF]. Thus, we get measures of Original Tweets, meaning new content provided by the user, Conversational Tweets, meaning replies to user (signified by the "@username" string), Repeated Tweets, meaning content that the user provided and is then reproduced by other users (signified by the "RT" string), Mentions, meaning unique references to user's username by other users and Graph Characteristics, meaning measures of total and topic-active friends and followers of the specific user.

According to this method, for the given topic we calculate the following features:

-Topical signal (TS) indicates the percentage of participation in a given topic by a specific author, regardless of the type of tweets. -Signal strength (SS) shows how strong an author's topical signal is based on how many tweets of this author have original content.

-Non-Chat signal (∼CS) tries to capture how many of the author's tweets are not involved in a direct conversation with friends or followers. This is used to discard any conversations that the specific author participated in but were not initiated by them. This feature involves an λ parameter calculated approximately at 0.05 to satisfy the constraint mentioned above. -Retweet impact (RI) demonstrates the impact of content generated by the author under measurement. The number of retweets is considered directly proportional to the impact this content has over the community around the specific topic. The calculations use multiplication by a logarithmic function to rule out the impact that may be generated by overly supportive followers of the specific author. -Mention impact (MI) is counting how much an author is mentioned during the discussion of a certain topic, indicating that they are socially regarded as an authority in the topic. A log function is included here too, to ensure that the author is not mentioned due to their mentioning other authors (in a conversational manner). -Information Diffusion (ID) is a social graph -based feature showing the ratio of number of users activated by the author on log-scale. We consider that an author is "activated" if they start tweeting on a topic after another user from the user's network that has tweeted on the topic before the author. -Network score (NS) is a mere social graph -based feature which counts the number of users active on the topic that are in the social circle of the author.

For further details on the measurements and the calculations involved in the basic feature set, one should refer to [START_REF] Pal | Identifying topical authorities in microblogs[END_REF].

Time-based Features

A central point of motivation for this paper is that the dimension of time is absent from any measure extracted from Twitter topics. This type of topic analysis is based on a static idea about the topic data: it takes topic discussions as solid data, showing indifference for temporal distribution, namely the way that discussion data is spread through time. The reality of social media topic discussion is more dynamic than this. Sparks of "discussion traffic" can be recognized when the topic is "hot" meaning that at some time intervals, due to events of conjuncture, a lot of users get attracted by the specific topic. This can lead some users getting "authoritativeness" points for a short period of activity in the topic's lifecycle. Our claim is that a strongly authoritative user should provide content or be conversationally active throughout the total lifecycle of a topic. In addition, authoritative user tweets should be discoverable throughout the day, so that users active in different time zones could interact with the authoritative user content. This is true especially for topics with a lifecycle that lasts days or months and for topics that have global interest attracted to them, such as an economic or political crisis topic, sports organization topics, etc.

We consider zero time according to the timestamp of the first tweet containing the requested #hashtag and ending time according to the timestamp of the last such tweet by the time of query. We propose new features that put into consideration the above mentioned parameters:

-Frequency is a feature indicating the contribution of a specific author in a topic during the entire lifecycle of the topic. In our approach, high values of tweeting frequency increase the authority of the author. This may seem contrary to the burst of information in short time segments that usually emerge in social networks, but we claim that for a user to be more authoritative, their content generation must follow and span a large percentage of the topic lifecycle. In the example which motivates the research in [START_REF] Pal | Identifying topical authorities in microblogs[END_REF], the Gulf of Mexico Oil Spill, Twitter accounts of environmental agencies considered authoritative for this topic should keep their followers informed as long as the topic is active. High frequency scores can rule out effects of posting burst. To calculate posting frequency, the ratio

f req = tweets i endtime topic -starttime topic (1)
is used for every author active in the topic. -Part-of-day measure captures the notion of users participating in a discussion from different time zones. This is especially interesting for topics with global effect and global audience. Due to the design of a platform such as Twitter, when a user logs in the platform, they see content in a newer-to-older fashion. To discover older content they have to scroll down, even if a search-by-topic approach is utilized. If time zones are taken into account, a user in East Asia should scroll down a lot to read original content from an author posting from the United States (taking into account that most users are not 24/7 online). For a global notion of authority, an author (such as an account registered by an institution or a news agency) should have a posting distribution that covers all day. This is an approximation feature; therefore dividing in four 6-hour parts -of -day measures (morning, noon, evening, night) is enough to demonstrate such distribution. In each part -of -day, simple count of tweets is used and provided as a clustering dimension.

Clustering and Ranking

For the clustering and ranking process, used to derive possible authorative users, two methods were compared: (i) clustering and ranking with the use of Gaussian Mixture Models (GMM) and the Expectation -Maximization (EM) algorithm (the method used in [START_REF] Pal | Identifying topical authorities in microblogs[END_REF], and (ii) our proposal, clustering and ranking with the use of cluster-based fusion of retrieved lists (as presented in [START_REF] Khudyak Kozorovitsky | Cluster-based fusion of retrieved lists[END_REF]. Our proposal also contains the substitution of the simple K-means algorithm for primal clustering by the Fuzzy C-means (as found in [START_REF] Bezdek | Pattern Recognition with Fuzzy Objective Function Algorithms[END_REF], [START_REF] Dunn | A fuzzy relative of the isodata process and its use in detecting compact wellseparated clusters[END_REF]) algorithms because of the notion of similarity it points out which is well suited when one has to deal with user content on a specific topic.

Gaussian Mixture Model

A Gaussian Mixture Model (GMM) is a probability density function calculated as the weighted sum of Gaussian component densities. More specifically, a GMM is a weighted sum of M component Gaussian densities as given by the equation,

p(x|λ) = M ∑ i=1 w i g(x|µ i , ∑ i) (2)
where x is a D-dimensional data vector of features, wi are the mixture weights and g(x|µ i , ∑ i) are the component Gaussian densities. Each is a D-variate Gaussian function with mean vector and covariance matrix.

GMMs are mostly used in continuous-value contexts, i.e. speaker recognition systems and biometric data. This raises a conceptual issue concerning the use of a GMM in the aspect of ranking authors in a microblogging environment. It is not proved that the set of features discussed in the previous section follows the normal (or Gaussian) distribution. Intuition and experiments show that a small cluster of authors around a specific topic achieves great scores, while a long tail of authors achieve low scores. Normal distribution implies that most of the authors should be at a +s distance from the average score (where s is standard deviation), which is not the case especially for popular topics with thousands of followers. Most of the followers participate through a low activity of retweets or commentary tweets, while authorative users should have frequent multi-type contribution on the topic.

Using cluster-based fusion of retrieved lists

The technique of cluster-based fusion is presented and evaluated in [START_REF] Khudyak Kozorovitsky | Cluster-based fusion of retrieved lists[END_REF]. The key concept of this technique is that intersimilarity of documents presented in different query result lists should be rewarded. Given a query q, a document d and a corpus of documents C, one can get L 1 , . . . , L m result lists on m retrievals based on query q. In these lists, d may appear in a low position in a result list. Straightforward list fusion methods, such as the CombSum, CombMNZ and Borda methods use partial list rankings to build a final result list, which can lead to very low total ranking [START_REF] Khudyak Kozorovitsky | Cluster-based fusion of retrieved lists[END_REF], of an important document d. Cluster-based fusion uses the cluster hypothesis to reward low-ranked documents with the condition that they belong in the same cluster with high ranked documents. Therefore, the cluster-based fusion method runs some clustering algorithm on the document set of documents appearing in the partial list and calculates the final ranking list based on partial list score plus cluster score.

In our proposal, we utilize this method using the fuzzy C-means algorithm for clustering documents. More specifically, the results are initially clustered into k lists using the fuzzy C-means algorithm, which permits an author to appear in more than one list. Each list is sorted with the Gaussian ranking method and then the cluster-based fusion method calculates the fusion score of the final ranking list. The cluster-based fusion method in our setting runs for the ClustFuseCombSUM, ClustFuseCombMNZ and ClustFuseBorda [START_REF] Khudyak Kozorovitsky | Cluster-based fusion of retrieved lists[END_REF] best-performing versions of the algorithm.

Experimental Evaluation

In the next three subsections, the experimental setting for our approach is presented (subsection 4.1), followed by the results for the top-10 influential users of different versions of the algorithm (subsection 4.2) and results of anonymous user evaluation (subsection 4.3). The logic behind the experiments is to evaluate the quality of results between the GMM-based approach and the cluster-based fusion approach (with different versions of fusion strategies).

Dataset

For the construction of our test data set, we had to respect the current limitations of the Twitter API, together with the need to build a data set of topics that have differences in their temporal development. The Twitter database was queried for the hashtags: #blacklivesmatter, #bigdata and #germanwings.

The first hashtag, #blacklivesmatter, responds to a discussion topic about a social situation with duration in time and very different activity levels from time to time. The second hashtag, #bigdata, is reflecting a discussion topic with mostly scientific and business interest and quite sparse but also quite linear activity in time. The third hashtag, #germanwings had to do with an emerging tragic event and organized discussion topic that demonstrated a burst of activity for the first few days but then faded to very low activity levels.

The construction of the data set was completed with a two-step repetitive process where firstly a tweet was returned as answer to the hashtag query and then a second query was performed to get the friends and followers list of the user that posted the tweet. That process resulted in 2.000 tweets and 49 user accounts (with a total of 50.622 followers) for the topic #blacklivesmatter, 2.000 tweets and 45 user accounts (with a total of 98349 followers) for the topic #bigdata and 1.860 tweets and 40 user accounts (with a total of 86.002 followers) for the topic #germanwings.

Top-k Users

For each topic and each tweet on the data set, two sets of experiments were conducted. The first set of experiments produced top-k ranked user lists by the execution of the GMM-based version of the algorithm as presented in [START_REF] Page | The pagerank citation ranking: Bringing order to the web[END_REF] and three versions of clusterbased fusion algorithms using the ClustFuseCombMNZ, ClustFuseBorda and Clust-FuseCombSUM strategies for list fusion, as presented in [START_REF] Khudyak Kozorovitsky | Cluster-based fusion of retrieved lists[END_REF], without the addition of the proposed temporal features. In the four columns of Table 1, one can see the top-5 ranked user lists for the three different topics. The second set of experiments produced top-10 ranked user lists like the first set, but this time including the temporal features we proposed in section 3.2.2. The four columns of Table 2 present the results of the four different algorithms for the top-5 ranked user accounts. It is important to note here that there are differences in the ranking produced by the algorithms after the addition of the temporal features, mostly affecting the methods based on the cluster hypothesis (e.g. ClustFuseCombSUM).

On the other hand, as previously mentioned, the average number of Followers per Community is slightly lower when the emotional methodology is followed. This is mainly a result of the way that Influential Metric is defined as it deals with an overall estimation of the impact of each user in the produced community.

User Evaluation

For the purposes of user evaluation of the different result sets, we organized an online survey and asked social media users to anonymously complete some web forms. A special occasion web application was developed linked to a database where answers where concentrated for later process. The evaluation scenario complied with the following assumptions: (1) evaluating users were anonymous (age and gender data where recorded for statistical reasons), (2) evaluating users were not presented with the results of the algorithms and are asked to rank usernames without guidance.

Users were presented with the whole data set and enabled to browse through the tweets, filter them by topic and query them by keyword or by username. After browsing through the data set, users were asked to choose the most influential username per topic, according to what they believe. That username was awarded by 10 extra points. After choosing the top username, users were presented with three forms, one for each topic, where they were asked to rank each of the usernames participating in the topics with a rank between 1 to 10 according to whether they are authoritative or not. The final rank for a username is the sum of ranks it has gained. A total number of 296 social media users from Facebook and Twitter took part in the evaluation survey with average age of 28.3 years and 37% of them were women. To understand the effectiveness of each method under evaluation, and also the effectiveness of the new time-based features we proposed, we used precision and Pearson -correlation metrics to measure the correctness of the algorithmic results and whether there is an agreement between method and user evaluation for the ranking order of users.

Precision and Pearson -correlation metrics are presented in Tables 3 and4 for the two sets of experiments described in Subsection 4.2. As we can see in both situations, the cluster-based methods score better than the GMM-based algorithm. The GMMbased algorithm seems to outrun the cluster-based fusion method only when Clust-FuseCombMNZ strategy is used for fusion. Please notice that abbreviations have been In the case of adding temporal features, one can see a significant improvement in the precision of every method, and an average improvement in the Pearson -correlation. The algorithms based on the ClustFuseBorda and ClustFuseCombSUM strategy seem to perform better in terms of recommendation quality. 5 Conclusions and Future Work

In this paper, a novel approach to the problem of the discovery of topical influential users in a microblogging environment was presented and evaluated. The important advances of this research are the suggestion of fuzzy clustering and cluster-based fusion of user lists, together with the addition of time-based features that improve the overall precision and correlation scores. The list fusion approach circumvents possible drawbacks that the GMM-based methods have in cases that user features do not follow a normal distribution, a situation most common in social network environments.

There is an open question of parallelization of the methods presented in this paper for the creation of a nearly real time authority discovery system. The aspects of time in web and social network mining tasks are rather newly introduced but can gain potential due to the dynamic nature of these networks. Recent work on personalized user profile recommendation [START_REF] Abel | Analyzing temporal dynamics in twitter profiles for personalized recommendations in the social web[END_REF] and on event discovery in Twitter [START_REF] Stilo | Time makes sense: Event discovery in twitter using temporal similarity[END_REF], expand the aspect of temporal dynamics in such environments.

For the discovery of influential users to be more accurate, one must comprehend the properties of the microblogging network and the behavior of the users, such as understanding collaborative behavior [START_REF] Honeycutt | Beyond microblogging: Conversation and collaboration via twitter[END_REF], analyzing why a tweet is likely to be retweeted [START_REF] Suh | Want to be retweeted? large scale analytics on factors impacting retweet in twitter network[END_REF] and decoding the social mechanism that explains why users with many followers are not necessarily the most influential [START_REF] Cha | Measuring user influence in twitter: The million follower fallacy[END_REF].

Table 1 .

 1 Top-5 ranked users with temporal features

	GMM	ClustFuseCombMNZ ClustFuseBorda ClustFuseCombSUM
		#blacklivesmatter
	Shgamha	PoeticRebel	Me MrCool	Shelby ville
	newBREED	pces	foodbruh	chilllaxx
	ArtisMentis	I Cant Breathe	Shelby ville	dmwwalker343
	PoeticRebel	Shgamha	chilllaxx	AshhhG
	I Cant Breathe newBREED	dmwwalker343	newBREED
			#bigdata
	AnRcloudSoft	PyramidAnalytic	eberman007	revistadircom
	revistadircom	bobehayes	GammaAnalytics phatpenguin
	danablouin	ThugMetricsNews	ThugMetricsNews byod news
	METAMORF US aleson es	KobbyDon1	BusinessNWSRM
	phatpenguin	ymtreb	mallys	BDUGUK
		#germanwings
	GAABY	GAABY	DobleYouu	DobleYouu
	WSJIndonesia	WSJIndonesia	FresaaChampagne FresaaChampagne
	KeystoneIDEAS die politik	EkoPardiyanto	EkoPardiyanto
	mycomfor	mycomfor	adrianaeloca	adrianaeloca
	EkoPardiyanto	lesatorr	nonotina	nonotina

Table 2 .

 2 Top-5 ranked users without temporal features

	GMM	ClustFuseCombMNZ ClustFuseBorda ClustFuseCombSUM
		#blacklivesmatter
	Shgamha	pces	Me MrCool	Shelby ville
	newBREED	I Cant Breathe	foodbruh	PoeticRebel
	ArtisMentis	PoeticRebel	Shelby ville	chilllaxx
	PoeticRebel	Shgamha	PoeticRebel	dmwwalker343
	I Cant Breathe newBREED	chilllaxx	AshhhG
			#bigdata
	AnRcloudSoft	NoSQLDigest	byod news	NoSQLDigest
	revistadircom	SocialNewsCorp	BusinessNWSRM revistadircom
	danablouin	KobbyDon1	BDUGUK	ThugMetricsNews
	METAMORF US PyramidAnalytic	AnRcloudSoft	phatpenguin
	phatpenguin	Paxata	eberman007	GammaAnalytics
		#germanwings
	GAABY	flores crespo	FresaaChampagne FresaaChampagne
	WSJIndonesia	tedmohs	lesatorr	lesatorr
	KeystoneIDEAS PhilDeCarolis	adrianaeloca	adrianaeloca
	mycomfor	HInstMH	Peterotul97	Peterotul97
	EkoPardiyanto	die politik	HInstMH	HInstMH

used space wisely, i.e. #blac for #blacklivesmatter, #bigd for #bigdata and #germ for #germanwings.

Table 3 .

 3 Precision and Pearson -correlation with temporal features

		ClustFuse	ClustFuse	ClustFuse
	GMM	CombMNZ	Borda	CombSUM
		Precision
	#blac 0,7	0,6	0,85	0,8
	#bigd 0,6	0,6	0,8	0,8
	#germ 0,6	0,5	0,75	0,75
		Pearson -correlation
	#blac 0,45	0,47	0,57	0,55
	#bigd 0,49	0,47	0,62	0,64
	#germ 0,51	0,48	0,55	0,59

Table 4 .

 4 Precision and Pearson -correlation without temporal features

		ClustFuse	ClustFuse	ClustFuse
	GMM	CombMNZ	Borda	CombSUM
	#blac 0,7	0,5	0,8	0,8
	#bigd 0,6	0,6	0,8	0,85
	#germ 0,7	0,5	0,7	0,7
		Pearson -correlation
	#blac 0,43	0,44	0,58	0,52
	#bigd 0,46	0,42	0,57	0,66
	#germ 0,51	0,48	0,55	0,59


Dr Marta Perez
email: marta.perezgarcia@telefonica.comsarita.saffonlopez@telefonica.com

Sarita Saffon Lopez

Building Trust Between Users and Telecommunications Data Driven Virtual Assistants

Keywords: Artificial Intelligence, Data Driven Virtual Assistants, Trust, Human Computer Interaction, Telecommunications

When we think about traditional technology services, such as the Internet and popular smartphone apps, interaction or experience related words tend to come to our mind. However, when we shift to more disruptive and breakthrough technologies, such as big data, IoT or Artificial Intelligence (AI), trust and security become a key challenge as the environments in which the personal information is stored and used to extract value changes in ways that are difficult to understand for normal users. This study explores the current challenges to trust and rely on a Virtual Assistant and establishes a series of constructs to build trust in a Telecoms Data Driven Virtual Assistant by considering aspects from an embryonic idea of the AI to its final articulation as a digital service. It examines a series of multi-methodology research interactions to help shape and refine the service, not only to build trust in the AI but also in the organisation behind it.

Introduction

Since ancient times, humans have had the curiosity to explore their own cognitive capacities, also referred to as intelligence, and how to replicate them in their own inanimate creations [START_REF] Villena | Inteligencia en Redes de Comunicaciones -01 Historia de la Inteligencia Artificial[END_REF]. Literature, myths and history itself prove this; from Von Kempelen's "Turk" automaton playing chess with Napoleon, to Mary Shelly's Frankenstein, to Alan Turing's code deciphering machine, until the development of Data Driven Virtual Assistants (DDVA) [START_REF] Mccorduck | History of Artificial Intelligence. IJCAI[END_REF] [START_REF] Bolter | Turing´s Man: Western culture in the Computer Age[END_REF]. The relationship between the human mind and machines has grown so strong that the language used to refer to both is almost analogous, with terms such as memory, hard drive, sensors, neurons, and so on. This discipline then, of studying the human cognitive competences and trying to replicate them, has become a science called Artificial Intelligence (AI). From competing in games such as chess [START_REF] Decoste | The Future of Chess-Playing Technologies and the Significance of Kasparov Versus deep Blue[END_REF]; to being part of strategic war weapons [START_REF] Teuscher | Alan Turing: Life and Legacy of a Great Thinker. 1 st edn[END_REF], to being able to recognize speech [START_REF] Kuligowska | Virtual assistants support customer relations and business processes[END_REF], and now to current uses in industries such as logistics, retail, education and financial services [START_REF] Bahrammirzaee | A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems[END_REF]. Furthermore, the future uses of AI are expected to apply to diverse areas, such as medicine, where surgeries might be done by automatons [START_REF] Grace | When Will AI Exceed Human Performance? Evidence from AI Experts[END_REF]. After briefly reviewing AI´s past, present and future, it is clear that it has become part of our lives, even though humans continue to doubt, and even fear the power that AI may reach. This assertion is supported by Pega Systems global 6.000 consumer study [START_REF] Systems | What consumers really think about AI: A Global Study[END_REF] where 70% of the participants reported that they feared AI and 24% are worried about robots taking over the world.

2

What does trust mean and why is it important?

Trust is defined as someone's willingness to become susceptible regarding a certain issue, context or information [START_REF] Corritore | On-line trust: concepts, evolving themes, a model[END_REF]. This is the reason why trust is of special importance when it comes to human-computer interaction, where users need to trust and rely on the digital agents or virtual assistants because they have to act on their behalf [START_REF] Castelfranchi | The role of trust and deception in virtual societies[END_REF]. In the context of online versus offline, Corritore et al [START_REF] Corritore | On-line trust: concepts, evolving themes, a model[END_REF] identified three core aspects that need to be built in order to generate trust: ease of use, risk involved and technology credibility. Sas and Khairuddin [START_REF] Sas | Design for Trust: An exploration of the challenges and opportunities of bitcoin users[END_REF] go beyond this, establishing there are two different types of trust when it comes to people interacting with technology: (1) trust between users and technology itself; and (2) trust between users who interact with technology. In the case of Virtual Assistants, trust is more focused on the first one, trust between users and technology, as it is a one-to-one relationship and there is no communication or information sharing with other party. In fact, when it comes to this information sharing between the user and the AI technology, it becomes obvious that not all of the information is of the same quality and usefulness, so allowing a system to dissect it is the first step towards trust [START_REF] Marsh | Trust and Reliance in Multi-Agent Systems: A Preliminary Report[END_REF]. Ultimately, the objective would be to enable these artificial intelligence virtual assistants to take care of issues with no intervention or supervision of the user [START_REF] Castelfranchi | Social trust: A cognitive approach[END_REF]. Many articles have been written arising this trust issue between users and artificial intelligence but there is however little work done on the investigation of their key constructs needed to generate trust between these parties.

Key challenges in trusting an AI

Northstar Research Partners [START_REF]AI today, AI tomorrow: Awareness, acceptance and anticipation of AI: A global consumer perspective[END_REF] carried out a survey study that explored the awareness and acceptance of AI from a consumer perspective. It highlights three concerns that participants reported around the subject of AI, which constitute the focus of building and maintaining trust.

A lack of knowledge of AI does not help build reliance

By definition, there are two types of AI: weak and strong [START_REF] Kerns | What's the Difference Between Weak and Strong AI[END_REF]. On one hand, only weak AI exists currently, which is based on programmed response-powered algorithms. Therefore, this is a supervised process that does not comprehend the commands it receives, but classifies them according to a pre-set data base of responses, and only simulates a human-like experience. On the other hand, strong AI, not yet created but in the process, has a mind of its own, hence it creates clusters and associations in order to provide a response. However, do users know this? Are they aware of how artificial intelligence works? Pega Systems study [START_REF] Systems | What consumers really think about AI: A Global Study[END_REF] reports that more than 50% of its 6,000-customer base are not aware of ever interacting with AI technology. This fact highlights the massive gap between people's perceptions, awareness and conscience around AI. Most information about AI users have is what they have seen in futuristic science fiction movies where machines take over the world, so it becomes crucial to create support, coordination, explanation groups and initiatives [START_REF] Castelfranchi | The role of trust and deception in virtual societies[END_REF] to make people knowledgeable of the benefits and potential of AI to enrich their lives. As MIT professors Erik Brynjolfsson and Andrew McAfee [START_REF] Erik Brynjolfsson | The second machine age: Work, progress, and prosperity in a time of brilliant technologies[END_REF] explain, AI is the base of what they refer to as the second machine age, resembling a new industrial revolution, where "computers and other digital advances are doing for mental power…what the steam engine and its descendants did for muscle power." (pp. [START_REF] Bahrammirzaee | A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems[END_REF][START_REF] Grace | When Will AI Exceed Human Performance? Evidence from AI Experts[END_REF]. The process towards trusting this technology will require exposure and experience, such as it did in the 18 th century with the Industrial Revolution. In summary, people mistrusted machines at the beginning of the Industrial Revolution like people mistrust AI today because, as the Pega Study [START_REF] Systems | What consumers really think about AI: A Global Study[END_REF] shows, they do not even know what AI actually is.

Mistrusting AI to solve complex problems

Every customer is different and as such, their problems have very diverse contingencies. How can a DDVA be smart enough to understand all the details, specifics of thousands and millions of customers? Research shows that existing AI relies on human monitoring, a best practice referred to as Human-in-the-Loop [START_REF] Dautenhahn | The art of designing socially intelligent agents: Science, fiction, and the human in the loop[END_REF], which is based on generating machine learning models that, in case of uncertainty or in contexts with a low level of confidence, yields the decision making to a human mind. This feedback is used to learn and improve algorithms over time. AI is not currently taking over the complex functions but the easy ones, while real people still work on the hard functions. This case is illustrated in current automatic pilot commercial planes like the Boing 777 [START_REF] Sha | G: Primary Flight Control of Boeing-777[END_REF] and self-driving cars as the Tesla autopilot mode in their models X, S and 3 [START_REF] Tesla | Full Self-Driving Hardware on All Cars[END_REF], where a human is always in the driving seat to take control if needed. When the AI systems need autonomy to perform tasks and make decisions on behalf of users, it becomes essential to develop trust on the AI actions in order to use them and adopt them as assistants in people's daily lives [START_REF] Glass | Toward establishing trust in adaptive agents[END_REF]. Currently, a human is always there controlling cases of doubt or ambiguity, for no machine is perfect, as no human is [START_REF] Negnevitsky | Artificial Intelligence: A Guide to Intelligent Systems[END_REF], so there is no need to separate AI from humans, since various trials such as with Centaur Chess [START_REF] Scharre | Centaur Warfighting: The False Choice of Humans vs. Automation[END_REF] and IBM Watson [START_REF] Rachlin | Making IBM's computer, Watson, human[END_REF] proved that human minds and machines work better together than separately [START_REF] Bunch | Principles for humancentered interaction design, part 2: Can humans and machines think together?[END_REF].

Managing and handling personal and private data

Other concern that increases distrust on AI is the uncomfortable necessity of sharing our personal data with an AI system, and not being sure of the ethical guidelines that it follows [START_REF] Abascal | Moving towards inclusive design guidelines for socially and ethically aware HCI[END_REF]. For instance, to assure ethical guidelines, it is important AI developers address the three AI factors suggested in The Cambridge Handbook of Artificial Intelligence [START_REF]The Cambridge handbook of artificial intelligence[END_REF]: (1) being transparent to inspection, (2) being predictable and (3) being non-operable, with the objective of becoming trust-generating features. This could be done by explaining the algorithms behind the system to support the transparency and predictability of the system [START_REF] Gibb | T-Group theory and laboratory method: Innovation in re-education Climate for trust formation[END_REF]. Banavar [START_REF] Banavar | Learning to trust artificial intelligence systems[END_REF] also proposes various tasks in order to achieve trust and reassurance. For example, before launching an AI system, he suggests to run field testing for ethics-related design issues, and continue to elicit user feedback throughout the deployment to monitor other potential ethical problems. When considering a product or service for a DDVA, ethics programs should be embedded in the AI system so they can reduce the risks and guide the system into values and principles in which it has to base its own behaviour [START_REF] Banavar | Learning to trust artificial intelligence systems[END_REF]. These guidelines require research and interdisciplinary teams of professionals in ethics and in AI development, to find a common language that will ensure the proper and practical implementation of ethical program in the machines [START_REF] Anderson | Machine ethics[END_REF]. For ensuring that these best practices are respected and enforced, current players in the AI field (Amazon, Google, Facebook, IBM, etc.) have already formed an alliance, in which they pursue various ethical topics, and develop new ones to safeguard the transparency and safety of data used by AIs [START_REF]Industry Leaders Establish Partnership on AI Best Practices[END_REF].

Best practices in AI: What to do and what to avoid

Trust building in any part of people's lives is affected by first impressions [START_REF] Yu | Developing trust: First impressions and experience[END_REF]. With DDVA, it is no different. According to psychologist Albert Mehrabian [START_REF] Mehrabian | Silent messages: Implicit communication of emotions and attitudes. 2 nd edn[END_REF], 38% of first impressions depend on the tone of voice, and only 7% on the words used. This includes using silences between sentences so that the person listening has time to process the information given, and having fillers just like "mmm" or "well", all to create the feeling of talking to a human being and therefore developing trust [START_REF] Kedem | Is Siri lying to you? Knowing when a bot sounds trustworthy is the next step in digital security[END_REF]. A second aspect that has to be considered is the gender of the DDVA, some key players in this area, such as Apple, Google or Amazon, have ensured that the device not only has a human-like tone of voice but a female one, as they are perceived as warmer [START_REF] Mitchell | Does social desirability bias favour humans? Explicit-implicit evaluations of synthesized speech support a new HCI model of impression management[END_REF], more trustworthy [START_REF] Siegel | Persuasive robotics: The influence of robot gender on human behaviour[END_REF], easier to understand [START_REF] Sokhi | Male and female voices activate distinct regions in the male brain[END_REF], in summary, easier to like. However, the human like aspect of the DDVAs cannot go to an extreme. Dr. Masahiro Mori [START_REF] Mori | The Uncanny Valley[END_REF] developed in 1970 the theory of the Uncanny Valley, which states that as the AI becomes more human-like there is an increase in the levels of acceptability and affinity with the device, but if it resembles too much human, there is a dramatic decrease of approval reaching even negative likeness and unsettling feelings [START_REF] Mori | The Uncanny Valley[END_REF]. This is the reason why many organisations are now going into the opposite direction, trying to avoid this Uncanny Valley. For instance, newly competitors have shifted from previous DDVA trend of naming their devices with human-like names as Alexa or Cortana, to more neutral and unbiased names like Bixby by Samsung and Tmall by Alibaba. Even though these best practices are being published, implemented and protected by the DeepMind Alliance, a well-established AI research organisation, there exist various cases of AI failures from where other learnings can rise. An example is Microsoft´s Tay, an AI chatbot which used people's Twitter conversations to generate her own, and was tricked by users to learn and produce racist and other harmful tweets. Additionally, Tay was modelled as a teenager girl, pushing the feminisation of the AI, worsening the situation [START_REF] Neff | Automation, Algorithms, and Politics| Talking to Bots: Symbiotic Agency and the Case of Tay[END_REF]. Another example of a not so successful outcome is Facebook's AI robots, Alice and Bob, which were disconnected because, while performing a negotiation between them, they generated a language that was incomprehensible for human beings [START_REF] Bradley | Facebook AI Creates Its Own Language In Creepy Preview Of Our Potential Future[END_REF].

These cases represent examples that feed fears inherent in the human mind, and inhibits them from trusting AI systems.

5

The Context

This research study takes place within a multinational telecoms organisation because it reflects rather clearly two challenges the literature review has highlighted around trusting AI: (1) AI taking over complex points of interaction between users and humans where problem solving is essential; and (2) the aversion towards an AI handling personal data (consumption of services, behaviours, habits, etc.). A Telecoms DDVA could work as an artefact to save time and effort, helping users make the most of their hired services and improve overall customer experience satisfaction [START_REF] Perez | The potential of Data-Driven Virtual Assistants to enhance Customer Experience in the Telecommunications Industry[END_REF]. The principles that appear in the findings section of this paper are being applied to Telefonica (trading as Movistar, Vivo and O2 in more than twenty countries) and its cognitive intelligence Aura. However, the study has been carried out as an exploratory study and has been conducted in general terms, evaluating the results as a category (not associating it to a specific brand) and recruiting participants from all the major Telecoms providers in Spain.

Methodology and Sample

To explore and tackle the issues highlighted in the literature, two ad-hoc user-centred research activities in Spain were conducted:

1. Sixteen in-depth interviews to find out perceptions around the concept, the value proposal and use cases. This was tested with a sample of six current DDVA users, six ex users and four aware but not current users. Two people of three age ranges (25-35, 36-45, 46-55) formed each of the groups.

2. Five hundred online questionnaires to explore the personality of current DDVAs and future interests, of which two hundred and nine participants were current users of DDVAs, a hundred and one ex users, and hundred and one aware but not current users. In each of these groups there was a mix of ages: 16-24, 25-34, 35-44 and 45-54 years old.

All participants were of medium-high socio economic level, living in capital cities and in proportion of 50% men and 50% women. The reason behind all these recruitment parameters was to understand habits, expectations, fears and perceptions of different segments with different approaches and usage of technology.

Findings

These different research activities that took place during this exploratory study helped identify a series of key aspects in the development of trust with a telecoms DDVA:

7.1

How should a Telecoms Data Driven Virtual Assistant be?

After identifying a suitable name for the DDVA, this study sought to understand the personal qualities it should have in order to emotionally connect with users. In the quantitative phase of this study, the 500 questionnaires, users were presented with a series of characters, skills and personality aspects to determine current and future perception of VAs. This helped understand the relationship between functional and emotional benefits users experiment with this type of Artificial Intelligence. In terms of characters, users were given a wide range of options to articulate the way they would perceive a Telco DDVA. Among these options, 60% of current 299 VA users agreed that they would interact with a robot-like character, contrasted by the 0% and 11% that chose mother or friend, respectively. This remote expectation of a personified human-like DDVA and instead an expectation of a pure technological, digital and artificial machines, confirms once more the Uncanny Valley theory of not humanising AI in excess [START_REF] Mori | The Uncanny Valley[END_REF]. In terms of personality traits, users expect DDVAs to be precise, especially when serving them information and answers. A suggested future improvement of DDVA personality is creativity, which is something they currently lack. Users also desire a Telecoms DDVA to be proactive, able to make intelligent suggestions and teach them how to make the most out of their services. However, users also expressed that they still lack the trust in AI to handover decision making, so we believe the proactivity of DDVAs must have a clearly defined scope. In summary, the personality traits (precision, creativity, proactivity) users expect and desire from a Telecoms DDVA have the potential of fostering positive connotations that could influence the perception of the Telecommunication Organization itself.

Transferring human roles to AI

The in-depth individual interviews explored the different aspects regarding the concept and value proposal of the DDVA. Within this context, users highlighted the fact that one of the DDVA objectives should be to improve customer experience and customer care with a Telecoms provider. This led to two major findings: (i) Users considered this would represent a massive change for customer care experience and many of them felt, a priori, reluctant to change. However, after a first moment of doubt, most users started balancing pros and cons of current situation and the proposed one and considered the optimisation of time and resources that a DDVA would have for them resulted on a more positive perceived customer care relationship; (ii) Secondly, users also would like to have a backup person to talk to when the DDVA is not able to solve their problem, following the Human In-The-Loop practice [START_REF] Dautenhahn | The art of designing socially intelligent agents: Science, fiction, and the human in the loop[END_REF]. These findings point out to the fact that people do not consider the DDVA will satisfy all their requests, and consider that they would not remain unsolved when combining human and AI forces [START_REF] Bunch | Principles for humancentered interaction design, part 2: Can humans and machines think together?[END_REF]. This led to a higher level of satisfaction of the DDVA concept proposed, while establishing a potential scenario to merge both worlds in order to enhance customer service and strengthen the value proposition.

7.3

The importance of the decision-making process

In this case, users were given a series of stimulus data in the form of different use cases the DDVA could carry out in order to improve their relationship with the Telecoms provider. This led to a discussion around the articulation of these scenarios, exploring the role the DDVA would play. The results of this study showed users are very interested in a DDVA that is proactive, makes suggestions and has a didactic function for users in order to learn the possibilities it offers. They showed an interest in receiving alerts about anomalies in their hired telco services. However, a key insight that was identified was the importance of the decision-making process in this type of scenarios. Users implied that even when a DDVA could potentially solve a problem with their services, they expect the DDVA to alert them and ask for permission to start sorting out the incidence. This suggests users are at a very early stage within the trust path towards independent DDVAs and still want to ensure they have the power and authority to control them. They are not interested yet in a DDVA that is able to make independent decisions on their behalf, even if this is for their own good. This takes the results back to perception of the DDVA as a robot or servant exposed previously.

Gathering and handling personal data

The literature has highlighted how personal data handling is one of the main challenges when trusting AI [START_REF]AI today, AI tomorrow: Awareness, acceptance and anticipation of AI: A global consumer perspective[END_REF]. This was a core aspect in this study as the Virtual Assistant is driven by the data gathered in the Telecoms organisation and the data that the user keeps generating when making use of its telecoms services. The interviews with users have helped identify two moments of truth around this issue: (1) The first one happens when a person realises the quantity of data the Telecoms has about its customers, which generates a sense of surprise and agitation, however, after this brief moment, users tend to have (2) the second moment of truth, by which they understand that information is already within their service provider, it is acceptable they have it. This thought calms them down as they realise nothing bad has ever happened to them around data handling with their telecoms provider.

Conclusions

Building trust is a complex task, as it rests on a combination of several characteristics or features. The different research activities have helped identify a series of aspects that are essential to the start building trust between a user and a Telecoms DDVA, so the learnings from the different experiments are currently being applied to Aura, Telefonica's Data Driven Virtual Assistant. Firstly, when building a DDVA, a series of characteristics to shape its personality are required. This study has pointed out the importance of being precise in the information offered and the level of quality in the results the DDVA shows. In the future, a more creative personality could be built on top of efficiency. These personality qualities, are currently being applied to Aura, so the team is working on the efficiency and precision with which Aura can answer a series of given questions to users. Furthermore, deepening the knowledge around these attributes is key to build the desired personality and that works with the mother brand. A second issue that has emerged is the articulation of the decision-making process, which the qualitative interviews have shown is placed on the user. Even when dealing with a contingency or problem, users are not yet prepared for the DDVA to make decisions on its own. This study highlighted the importance that having control has for the user so Aura will notify users of any abnormality so that the user is the one with the power, the control over his own telecoms services and it is the one who makes the decisions. This is something that could evolve in the future, as these type of products and services become increasingly integrated in everyday life, but not yet at this early stage. Consequently, when this type of DDVA adoption evolves, Aura's team would like to test if users trust her enough to enable her to make decisions on her own (benefiting the user). In terms of the implications of the DDVA taking over certain interactions in the customer experience, specially its potential implementation in customer care, users have shown an interest toward the time and effort saving the DDVA could bring them in this type of situation, improving their level of satisfaction with the telecoms organisation. The trade-off they require is to be redirected to a person when the DDVA is not able to solve the issue they are trying to sort out. As for privacy and security of personal data, this study has suggested that the brand equity of the organisation providing the DDVA plays a key role in addressing this challenge. An organisation that is not able to build trust and confidence in its customers will probably not be able to generate trust in a DDVA, no matter the value proposition.


Keywords: Output isolation, power divider, tunable device, tuner, spurious free, varactors

We propose a new topology based on a semilumped complex impedance transformer for the design of a frequency tunable three-port power divider. A prototype has been realized in a hybrid technology with commercially available varactor diodes. Our design is based on an impedance transformer in the input branch (with electrical length of 20.4°), and a loop in the output branches (with electrical length of 2x24°). This leads to a miniaturized device, more than two times shorter than a classical Wilkinson power divider. This divider can be tuned over ±30% around 1.35 GHz (from 0.95 GHz to 1.75 GHz) with insertion loss lower than 0.3 dB, return loss better than 20 dB and isolation between the two output ports better than 20 dB. Moreover, the low-pass behavior of the input impedance transformer leads to a good rejection of the second harmonic.

I. INTRODUCTION

Tunable miniaturized devices constitute a great challenge in the field of modern RF/microwave telecommunications. In the near future, more and more applications will use multiple operating frequencies, with hard miniaturization constraints for MMIC applications. In this context, some researches are carried out to show the feasibility of various tunable devices that can be embedded in RF/microwave telecommunication systems like phase shifters, impedance transformers, filters, couplers, and power dividers. Several technologies can be used to achieve such tunable devices: ferroelectric materials, ferromagnetic materials, MEMS, piezoelectric devices, optical control and semiconductors.

The technical maturity of the power dividers dates from the sixties when Wilkinson published an article on a N-port divider (with N even or odd) [START_REF] Wilkinson | An N-way hybrid power divider[END_REF]. One of the properties of this kind of divider is to distribute uniformly the input power P into N output ports so as to have a P/N power in phase on each output. Another property is related to a very good isolation between the output ports. This divider is designed with quarter-wave transmission lines.

In general, power dividers can be classified in two groups: three-port and four-port dividers. Since 1960 many researches on the three-port dividers were carried out, (i) to increase the bandwidth by increasing the number of sections [START_REF] Cohn | A class of broadband three-port TEM-mode hybrids[END_REF], (ii) to distribute a no uniform power to each output [START_REF] Parad | Split-tee power divider[END_REF], (iii) to decrease the size of the devices using lumped elements [START_REF] Kopp | Asymmetric lumped element power splitters[END_REF], [START_REF] Scardelleti | Miniaturized Wilkinson power divider utilizing capacitve loading[END_REF], [START_REF] Ahn | General design equations, small-sized impedance transformers, and their application to small-sized three-port 3-dB power dividers[END_REF] (iv) to eliminate the higher harmonics [START_REF] Yi | Modified Wilkinson power divider for nth harmonic suppression[END_REF]. All these researches are based on quarter-wave transmission lines, used as impedance transformers to transform a pure resistive load into another resistive load. In parallel, researches were carried out on complex impedance transformers, not based on quarterwave transmission lines. One principle was shown in 1967 [START_REF] Hamid | On the design of stepped transmissionline transformers[END_REF].

In this paper we show a new compact topology of power dividers having the same properties as Wilkinson power dividers. They are based on a complex impedance transformation. The output branches make it possible to transform a resistive load into a complex load using a smalllength transmission line. This complex load is then transformed to a resistive load thanks to a small-length complex impedance transformer. This new topology leads to miniaturized, spurious free power dividers. The complex impedance transformer is realized by a transmission line loaded by a capacitor, leading to a slow-wave and low-pass behaviour. First, we will describe the principle of the topology, compared to a classical Wilkinson power divider. Then the design, simulation and measurement results obtained on a tunable power divider will be presented.

II. PRINCIPLE

A classical three-port Wilkinson power divider is constituted by a T junction and two output impedance transformers. A resistor between output ports is used to achieve isolation (see Fig. 1).

R = 2Z0 Port 1 (Z0) λ/4 ; 0 2 Z Port 2

Input transformer

Output loop

J D1 θ 1, Z1 θ 3, Z3 Port 3 Port 1 R D2 θ 2, Z2
paper. This topology can be divided in two blocs: an input transformer, the "tuning bloc", and an "output loop" necessary to achieve isolation between the two output ports (ports 2 and 3). Compared to the Wilkinson power divider, the impedance transformer is placed in the input port, so the input impedance at the junction point (J), looking to ports 2 and 3, can be adjusted, and is not necessary equal to the matching input impedance, ie 50 Ω in general.

The frequency tunability is obtained with the varactor diode D 1 . The equivalent capacitance of D 1 can be adjusted by the bias voltage applied to this varactor. So the equivalent electrical length and characteristic impedance of the impedance transformer change. The isolation between the two output ports, for each working frequency, is achieved with the varactor diode D 2 capacitance tuning.

We first detail the tuning bloc, and secondly, we give the principle of the output loop.

The design is carried for a 1.2 GHz working frequency.

A. Tuning bloc : Fig. 3 shows the electrical equivalent circuit of the tuning bloc. It is realized with a small-size T semi-lumped transformer. We call Z out the impedance seen from the output of the tuning bloc. Z out has been represented on the Smith chart in Fig. 4, for a typical case with

Z 1 =Z 2 =120 Ω, θ 1 =14.4°, θ 2 =6°
and a varactor reactance X=1/Cω varying from X max =88 Ω to X min =30 Ω at the center frequency f = 1.2 GHz, ie a C max /C min ratio equal to 3.

Fig. 4 shows that for small length transmission lines (θ 1 =14.4° and θ 2 =6°), Z out is always capacitive, and the Z out point can be changed with the varactor tuning. Finally, this structure could be considered as a low-pass filter [START_REF] Kaddour | Compact and selective low-pass filter with spurious suppression[END_REF], leading to a good rejection of the spurious frequencies, especially at twice the working frequency as will be shown in measurement results.

B. Output loop :

The output loop equivalent electrical circuit is given in Fig. 5. For small-length output loop transmission lines, i.e. smaller than a quarter-wave length, Z in is inductive.

C. Blocs matching :

Considering small-length transmission lines for the input tuning and output loop blocs, the points for Z out and Z in are in the lower part (capacitive part) and in the upper part (inductive part) of the Smith Chart, respectively. Then the matching condition

* out in Z Z =
can be realized for a large bandwidth.

III. DESIGN AND MEASUREMENT RESULTS

To illustrate the new concept developed for the design of miniaturized tunable power dividers, a prototype has been realized in a hybrid technology by using surface mounted varactor diodes. For simplicity, the design was done for a 1.2 GHz working center frequency. The characteristic impedance of the input tuning bloc and output loop transmission lines was fixed to 120 Ω. A greater value would lead to much smaller transmission lines, but also increased (a) for bias voltages from 0 V to 12 V. The equivalent electrical circuit of the varactor is given in Fig. 9. The parasitic series resistance R s is 1.6 Ω, the series inductance L s is 1.2 nH, and the case capacitance C c is 0.11 pF. Simulations have shown that the capacitance of the varactor D 2 should vary from 0.65 to 4.3 pF. So two different varactors D 1 and D 2 were necessary. In order to use the same varactors, we just had one inductor in parallel with D 2 . In this case, the equivalent capacitor is:

2 2 0 / 1 // ω ω ω L C L C eq                 - = = (1)
with:

LC / 1 0 = ω .

Measurements were carried out with a WILTRON™ 360

Vector Network Analyzer and a standard SOLT calibration procedure. Simulations were carried out with ADS. Here again, the agreement between simulations and measurements is good. Thanks to the varactor D 2 capacitance tuning, the isolation is better than 20 dB for the whole bandwidth. Finally, Fig. 14 shows the low-pass behavior of the structure for a 1.68 GHz working frequency, corresponding to |S 11 | < -20 dB (C 1 =1.46 pF). |S 21 | lies between -3 and -4 dB from DC to about 2 GHz, and rapidly decreases above 2 GHz. The capacitively loaded transmission lines of the input tuning bloc acts as a low pass filter, with a cut-off frequency near the Bragg frequency, as explained in [START_REF] Kaddour | Compact and selective low-pass filter with spurious suppression[END_REF]. Moreover the varactor's series inductance leads to a transmission zero at the frequency:

( )

1 0 2 / 1 C L f s π =
, ie f 0 = 3.7 GHz in our case (C 1 =1.56 pF and L s = 1.2 nH).

IV. CONCLUSION

A new topology for equal three-port power dividers has been proposed. This topology leads to a miniaturized power divider, at mean 50 % smaller than a classical Wilkinson power divider. Due to the use of complex impedance transformer exhibiting a low-pass behavior, spurious are greatly attenuated. Also, the power divider can be completely tuned. A prototype realized with commercially available varactors has shown a good matching and isolation (better than 20 dB) over a tunable bandwidth of 60 % for a C max /C min ratio equal to 3. First harmonic of the working frequency is attenuated by more than 15 dB and spurious are attenuated by more than 9 dB to five times the working frequency. Insertion losses are limited to 0.3 dB. Fixed power dividers can be realized with the proposed approach, leading to spurious free, small-size, small-bandwidth power dividers.

Fig. 1 .

 1 Fig. 1. Wilkinson power divider.

Fig. 2 .

 2 Fig. 2. The proposed power divider topology.

Fig. 2

 2 Fig.2shows the power divider topology proposed in this

Fig. 3 .

 3 Fig. 3. Input tuning bloc equivalent electrical circuit.

Fig. 4 .

 4 Fig. 4. Impedance seen from the output of the tuning bloc Z out , versus frequency, for different reactance values at the 1.2 GHz central working frequency: minimum (a), mean (b) and maximum (c).

Fig. 5 .

 5 Fig. 5. Output loop equivalent electrical circuit.We call Z in the impedance seen from the junction input. Z in has been represented on the Smith chart in Fig.6, for a typical case with Ω = 120 c Z and 3 24 θ = ° when ports 2 and 3 are

Fig. 6 .

 6 Fig.6. Impedance seen from the input of the output loop Z in , versus frequency.

  manufacturing sensitivity. The power divider was designed in microstrip on the Rogers™ RO4003 substrate (relative permittivity ε r = 3.38, dielectric loss tan(δ) = 0.0035, height h = 0.813 mm, and copper thickness t = 35 µm). The transmission lines width has been set to 250 µm in order to realize the 120 Ω characteristic impedance.MA4ST-1240 Macom™ varactor diodes were used. The variable capacitance can be changed between pF

Fig. 9 .

 9 Fig. 9. Macom MA4ST-1240 Varactor equivalent electrical circuit. Using the complete varactor model, the length of the transmission lines L 1 to L 3 , corresponding to θ 1 to θ 3 , were optimized with ADS [10] to achieve the largest bandwidth with the matching condition |S 11 | < -20 dB. We obtained 1 14.4 θ =

Fig. 10 .

 10 Fig. 10. Photography of the power divider.

Fig. 11 .

 11 Fig. 11. |S 11 | and |S 21 |. (a) Simulations. (b) Measurements.

Fig. 12 .

 12 Fig. 12. |S 32 |. (a) Simulations. (b) Measurements.

Fig. 11

 11 Fig. 11 (a) and (b) show the simulations and the measurements, respectively, of |S 11 | and |S 21 |. The value of the varactor's capacitance and the bias voltage has been indicated in the graphs, for simulations and measurements, respectively. Measurements and simulations show a good agreement, even if some ripples of maximum ± 0.05 dB are present in the measurement results for |S 21 |. If the matching condition |S 11 | < -20 dB is considered, the relative bandwidth reaches ± 30 %, from 0.95 GHz to 1.75 GHz around 1.35 GHz. The insertion losses are limited to 0.3 dB, ie -3.3 < |S 21 | dB < -3.2. These insertion losses are

  Fig. 13 (a) and (b) show the simulations and measurements, respectively, of |S 22 | and |S 12 |.

Fig. 13 .

 13 Fig. 13. |S 22 | and |S 12 |. (a) Simulations. (b) Measurements. The output ports matching (|S 22 |) is better than 20 dB in the whole bandwidth. The device allows combiner utilization.

Fig. 14 .

 14 Fig. 14. Rejection of the spurious harmonics. (a) Simulations. (b) Measurments.


Thomas Pietrzak
email: thomas.pietrzak@univ-metz.fr

Benoit Martin
email: benoit.martin@univ-metz.fr

Isabelle Pecci
email: isabelle.pecci@univ-metz.fr

Information display by dragged haptic bumps

The utility of haptic interfaces is widely established now. Although many interfaces has been designed, few tests about immediate usability have been made. The purpose of this paper is a study of direction and amplitude discrimination when a user feels a bump while moving the hand along an axis with an haptic pointing device. The results show that users can discriminate two and four directions with few errors while using one amplitude. But in the case of two or three amplitudes, some users have difficulties. The amplitude discrimination is more problematic. With only two amplitudes some users don't manage to discriminate the bumps, and with three amplitudes most of them make a lot of errors.

Introduction

Haptic can be used to give the user some information. For example, Wall and Brewster [START_REF] Wall | Providing external memory aids in haptic visualisations for blind computer users[END_REF] describe a system which allows the user to put bookmarks in the environment called beacons. Thanks to these bookmarks they can navigate from each recorded location to another: with a simple keystroke, the user is dragged to the bookmark's position. Their study exposes a navigation of bar charts. The user had to give the highest value between four given bars among twelve bars. The proportion of correct answers was 76% and the average time taken to answer a question was 50.92s. It would be interesting to add some codes or icons on the beacons to give them a semantic. Some user tests could be done to let us know if adding informationd during the guidance can decrease the answer time and the error rate.

We can find two kinds of haptic icons in the literature. The first one has been created by MacLean and Enriquez [START_REF] Maclean | Perceptual design of haptic icons[END_REF]. They use a DC motor which delivers forces on a rotation axis. The signal parameters are magnitude (or force), shape (sinusoid, square, etc.) and frequency. They did some tests where users had to classify icons in categories. The results show that the only criteria commonly used by all the users is frequency. It should be interesting to create such kind of icons with a widely used device.

The other haptic icons are the tactons created by Brewster and Brown [START_REF] Brewster | Nonvisual information display using tactons[END_REF]. These icons are a tactile transcription of the earcons [START_REF] Brewster | An evaluation of earcons for use in auditory human-computer interfaces[END_REF]. The idea is simple: the codes are created in a hierarchical way. One criteria is used per level, one value of a criteria maps to a code of the level associated. Tactons use three parameters: rhythm, frequency and duration. For example, you can create a rhythm for file and another for directory, then a frequency for "open" and another for "close". And finally you create a duration for "read only" and another for "read and write". So you will be able to code "open file in read only" and to compare easily with "open directory". The first tests are not conclusive enough so other tests with other parameters are currently being processed.

In a previous paper, we studied haptic bumps where the user's hand was completely dragged [START_REF] Pietrzak | Affichage d'informations par des impulsions haptiques[END_REF]. It combines the idea of force feedback icons of the haptic icons, and the possibility to create hierarchical codes like the tactons/earcons. The idea was to drag the user on some millimetres in six directions (upward, downward, foreward, backward, leftward and rightward). We used two and three amplitudes in the same serie. The results have shown that we can discriminate the six directions and two amplitudes with very few errors. When using a third amplitude, there is an ambiguity with the medium amplitude which is often confused with the two others. We decided to experiment a similar system where the user is more active and should move along an axis to feel the bump. This is the purpose of this paper.

Haptic bumps

Haptic is a good alternative channel to provide the user some information when visual and auditory channels are not suitable. Interfaces for visually and hearing

Proceedings of ENACTIVE05

2nd International Conference on Enactive Interfaces Genoa, Italy, November 17th-18th, 2005 impaired people are good examples for that purpose. A widely used technique to prevent the user from being lost in the haptic space is to constraint him in the area he is supposed to explore. For instance, we often stick the user on a line: this could help the user to recognise a shape or a path. We designed some bumps we could put on a line to add informations on it (bold line on the figure 1). So when the user moves along the axis, he will be dragged in a direction, according to a code. He will feel like if the line was bent.

Experiments protocol

In these experiments the user will use a PHANToM Desktop haptic device (figure 2). The force is exerted on a stylus the users hold in the hand. This version of the device has six degrees of freedom (three in translation and three in rotation), and renders forces on the three translation ones. The purpose of these experiments is to test the discrimination of several direction and amplitude bumps. A pilot evaluation was done to design the procedure and to choose the parameters. Bumps were designed as shown on the figure 3: the bump was located at the right of the starting point. If the user moved to the left he was just constraint on an horizontal line. The bump was a sinusoid, and started 5mm after the starting point and ended 5mm after that, then there was a 2mm pause before stopping the bump. The parameters tested in these experiments are direction and amplitude. The goal is to experiment immediate discrimination and not learning.

The users have done several series, they were given 150 bumps in each. When the experimenter starts the bump, the system stores the cursor's position and sets the starting position at that location. Then the user has to move and feel the bump up to the stop position. After that, the user is not constraint anymore and says what he felt: the direction (upward, downward, foreward or backward) and the amplitude (from 1 to 3, 1 being the smallest and 3 the biggest). The experimenter stores the answers, and the next bump starts. After each serie, the user's impressions were collected. The users were between 23 and 37, one female and five male. All are right-handed and sighted; they were blindfolded to prevent from giving visual aids.

Direction discrimination experiment

Experimental procedure

The first step of our study is to test the direction discrimination. Four series had been made. In the first two ones there were two direction bumps (upward and downward). In the first serie the amplitude was 0.4cm and 1.6cm in the second. In the last two series, four directions were used : upward, downward, foreward and backward. The two series used 0.4cm and 1.6cm again.

Results and discussions

The errors of the series with 2 directions are reported in the table 1 and those of the series with 4 directions in the As we can see the users make very few errors (table 1). Three users made as much errors in the first serie as in the second. The three others made almost all their errors in one serie and none in the other. Only the first user rather preferred the second amplitude. So if we take the user's favourite amplitude we get only 0.77% of erroneous answers. All the users thought the second amplitude was very violent, and they had to hold the stylus stronger to prevent from making errors. Two users were very disturbed by that and made more errors with the bigger amplitude than with the smallest.

Amplitude

User Errors (cm) 1 2 3 4 5 6 (%) 0.4 0 1 3 3 1 2 1.11% 1.6 0 4 2 2 0 0 0.89% total 0 5 5 5 1 2 1.00% The amount of errors is less important than in the previous series whereas the task is supposed to be more difficult (table 2). You can notice that this time the users make more errors with the smallest amplitude than with the bigger amplitude. However, the users still prefer the smallest amplitude. We could explain the decrease of errors by a little learning, or by a better concentration because the task was more difficult. Some users had the feeling that some bumps of a serie hadn't the same amplitude as others in the same serie. Especially upward/downward and foreward/backward didn't seems to have the same amplitude. We could explain that in two ways. The first hypothesis is that the PHANToM uses one motor for each axis, so there could be a difference of calibration. The second one is that foreward and backwards are direction in the same axis than the user's arm, and upward and downwards are perpendicular to this axis. So the users have more natural resistance force against a foreward/backward bump.

Direction and amplitude discrimination experiment

Experimental procedure

The second step aims to combine all the previous parameters by proposing bumps of several directions and amplitudes in the same serie. Two series has been made: in the first one there were only two amplitudes (0.4cm and 1.2cm) and in the second one three amplitudes (0.4cm, 0.8cm and 1.6cm) were proposed. In both series the four previous directions are used. The user had to recognise the direction and the amplitude at the same time.

Results and discussions

Direction errors are reported in the tables 3 and 4. This time each table represent one serie and each row shows the number of erroneous answers when the corresponding direction was given. The amplitude errors are in the tables 5 and 6, and the rows show the number of errors when the corresponding amplitude was given.

Direction User Errors (cm) 1 2 3 4 5 6 (%) upwards 0 1 3 1 0 0 2.98% downwards 2 2 4 0 0 1 3.26% backwards 3 0 1 2 0 0 2.33% forewards 2 1 5 2 0 1 5.56% total 7 3 13 5 0 2 3.33% We get three times more errors than in the previous experiment (two last series). So using several amplitudes in the same serie obviously disturbs the direction discrimination.

Direction

User Errors (cm) 1 2 3 4 5 6 (%) upwards 2 2 0 4 1 1 4.07% downwards 2 0 1 2 0 1 3.57% backwards 1 0 0 1 0 1 1.61% forewards 2 0 1 6 0 0 3.00% total 7 2 2 13 1 3 3.11% The error rate is quite the same than in the last serie (table 4), so the number of different amplitudes doesn't matter with the direction discrimination. Thus we can conjecture the errors are caused by the fact of having other parameters to recognise at the same time.

As you can see the amplitude errors are higher than the direction errors. It is clear that the amplitude discrimination is a difficult task. An interesting point is One bump among five is misunderstood by the users. It is clear that users have difficulties to discriminate three amplitudes. Many users make a lot of mistakes because they think relative. So they don't feel the same amplitude when the previous bump was weak, medium or strong. It could be explained by the fact that people don't hold the stylus with the same strength whether the bump was weak or strong.

Two users made an interesting remark. They said that the direction was easier to understand when moving slowly, and the amplitude was easier to understand while moving quickly. This could explain the difficulty of trying to recognise both simultaneously. One of those two users was the user 5 and is one of the users who made the less errors. Due to this remark he deduced that he has to read all the bumps with the same speed. The experimenter noted that the users that made the more errors was often changing their reading speed. This fact is important and should be experimented.

Conclusion

The interaction technique described in this paper is a simple idea to give the user some informations using an haptic pointing device. The users can recognise the direction quite efficiently, and have some difficulties while trying to recognise the amplitude at the same time. The users made fewer errors in a previous tests with completely dragged bumps [START_REF] Pietrzak | Affichage d'informations par des impulsions haptiques[END_REF], so we think that we can obtain similar results if we put the bumps in a context, as suggested by some users. Sometime they had some hesitations and it caused quite many errors, so the possibility to read several times the code could help them to understand better.

This point should be fixed when we will do tests of bumps sequences on a large axis. This experiment was only a preliminary test of immediate utilisation. We will do tests with visually impaired children, and we will tests a learning on these icons. They are used in a software developed for the use of the European project MI-COLE.

Figure 1 :

 1 Figure 1: Bump felt by the user

Figure 2 :

 2 Figure 2: PHANToM haptic device

Figure 3 :

 3 Figure 3: Bump design

Table 1 :

 1 Experiment 1 with 2 directions: errors in direction

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table 2 .

 2 Each row corresponds to a serie, the last line is the total.

Table 2 :

 2 Experiment 1 with 4 directions: errors in direction

Table 3 :

 3 Experiment 2 with 2 amplitudes: errors in direction

Table 4 :

 4 Experiment 2 with 3 amplitudes: errors in direction

Table 6 :

 6 Experiment 2 with 3 amplitudes: errors in amplitude

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Acknowledgements

This work is funded by the European project MI-COLE (IST-2003-511592) and by the CA2M (Communauté d'Agglomération de Metz Métropole) in the project PICOB.