Simple models of network access,with applications to the design of joint rate and admission control

At the access to networks, in contrast to the core, distances and feedback delays, as well as link capacities are small, which has network engineering implications that are investigated in this paper. We consider a single point in the access network which multiplexes several bursty users. The users adapt their sending rates based on feedback from the access multiplexer. Important parameters are the user’s peak transmission rate p, which is the access line speed, the user’s guaranteed minimum rate r, and the bound ϵ on the fraction of lost data.Two feedback schemes are proposed. In both schemes the users are allowed to send at rate p if the system is relatively lightly loaded, at rate r during periods of congestion, and at a rate between r and p, in an intermediate region. For both feedback schemes we present an exact analysis, under the assumption that the users’ job sizes and think times have exponential distributions. We use our techniques to design the schemes jointly with admission control, i.e., the selection of the number of admissible users, to maximize throughput for given p, r, and ϵ. Next we consider the case in which the number of users is large. Under a specific scaling, we derive explicit large deviations asymptotics for both models. We discuss the extension to general distributions of user data and think times.     

Incorporating behavioral trust theory into system development for ubiquitous applications

Trust has been shown to be a key factor for technology adoption by users, that is, users prefer to use applications they trust. While existing literature on trust originating in computer science mostly revolves around aspects of information security, authentication, etc., research on trust in automation—originating from behavioral sciences—almost exclusively focuses on the sociotechnical context in which applications are embedded. The behavioral theory of trust in automation aims at explaining the formation of trust, helping to identify countermeasures for users’ uncertainties that lead to lessened trust in an application. We hence propose an approach to augment the system development process of ubiquitous systems with insights into behavioral trust theory. Our approach enables developers to derive design elements that help foster trust in their application by performing four key activities: identifying users’ uncertainties, linking them to trust antecedents from theory, deducting functional requirements and finally designing trust-supporting design elements (TSDEs). Evaluating user feedback on two recommender system prototypes, gathered in a study with over 160 participants, we show that by following our process, we were able to derive four TSDEs that helped to significantly increase the users’ trust in the system.     

Incremental critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user's feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature's value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a user's critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.     

A task-driven intelligent workspace system to provide guidance feedback

We introduce a vision-based human–computer interaction system which collaborates with a user by providing feedback during user activities. We design an intelligent workspace system that analyzes the context of a user’s tasks and generates appropriate feedback guiding the user to complete the tasks correctly. While a user is performing the high-level activity, our system analyzes what sub-events the user has already completed and what sub-events are needed next in order for the user to finish the activity. Based on this analysis, the system generates hierarchical feedback messages to assist the user. We test our system on three different types of tasks: tightening nuts on a wheel of a car, assembling a toy spaceship with LEGO blocks, and assembling a laptop computer with its parts. Experimental results demonstrate the accuracy of the vision-based activity analysis, and a comparative user study shows that our system’s feedback enables users to complete assembly tasks with significantly improved efficiency.     

Interacting meaningfully with machine learning systems: Three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple “right/wrong” judgments. If the users themselves could work hand-in-hand with machine learning systems, the users’ understanding and trust of the system could improve and the accuracy of learning systems could be improved as well. We conducted three experiments to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study that investigated users’ willingness to interact with machine learning reasoning, and what kinds of feedback users might give to machine learning systems. We then investigated the viability of introducing such feedback into machine learning systems, specifically, how to incorporate some of these types of user feedback into machine learning systems, and what their impact was on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human–computer collaboration via on-the-spot interactions as a promising direction for machine learning systems and users to collaboratively share intelligence.     

Designing motivation using persuasive ambient mirrors

In this article, we describe four case studies of ubiquitous persuasive technologies that support behavior change through personalized feedback reflecting a user’s current behavior or attitude. The first case study is Persuasive Art, reflecting the current status of a user’s physical exercise in artistic images. The second system, Virtual Aquarium, reflects a user’s toothbrushing behavior in a Virtual Aquarium. The third system, Mona Lisa Bookshelf, reflects the situation of a shared bookshelf on a Mona Lisa painting. The last case study is EcoIsland, reflecting cooperative efforts toward reducing CO₂ emissions as a set of virtual islands shared by a neighborhood. Drawing from the experience of designing and evaluating these systems, we present guidelines for the design of persuasive ambient mirrors: systems that use visual feedback to effect changes in users’ everyday living patterns. In particular, we feature findings in choosing incentive systems, designing emotionally engaging feedback, timing feedback, and persuasive interaction design. Implications for current design efforts as well as for future research directions are discussed.     

Query expansion using pseudo relevance feedback on wikipedia

One of the major challenges in Web search pertains to the correct interpretation of users’ intent. Query Expansion is one of the well-known approaches for determining the intent of the user by addressing the vocabulary mismatch problem. A limitation of the current query expansion approaches is that the relations between the query terms and the expanded terms is limited. In this paper, we capture users’ intent through query expansion. We build on earlier work in the area by adopting a pseudo-relevance feedback approach; however, we advance the state of the art by proposing an approach for feature learning within the process of query expansion. In our work, we specifically consider the Wikipedia corpus as the feedback collection space and identify the best features within this context for term selection in two supervised and unsupervised models. We compare our work with state of the art query expansion techniques, the results of which show promising robustness and improved precision.     

Feedback types and users’ behavior in online innovation contests: Evidence of two underlying mechanisms

Online innovation contests (OIC) provide companies, via a dedicated community, an important means to access remote knowledge and ideas of users and thereby a creative playground for fueling innovation. Our literature review shows that our understanding of the impact of diverse types of feedback on user participation, especially continued participation, and success in OIC is at a nascent stage. The present study therefore seeks to examine why and how different types of feedback influence users’ behavior in OIC, and the detailed mechanisms underlying such influences. While our results do not show a significant relationship between receiving peer dynamic feedback and user success, we find that receiving sponsor static feedback in users’ first submission is positively associated with their continued participation in OIC. Also, when compared to peer dynamic feedback, sponsor static feedback has a stronger effect on users’ continued participation. Our goal is to confer a holistic picture of how the timing, source, and form of feedback shape user continuous participation and success in OIC.     

Characterizing users’ check-in activities using their scores in a location-based social network

Analysis of users’ check-ins in location-based social networks (LBSNs, also called GeoSocial Networks), such as Foursquare and Yelp, is essential to understand users’ mobility patterns and behaviors. However, most empirical results of users’ mobility patterns reported in the current literature are based on users’ sampled and nonconsecutive public check-ins. Additionally, such analyses take no account of the noise or false information in the dataset, such as dishonest check-ins created by users. These empirical results may be biased and hence may bring side effects to LBSN services, such as friend and venue recommendations. Foursquare, one of the most popular LBSNs, provides a feature called a user’s score. A user’s score is an aggregate measure computed by the system based on more accurate and complete check-ins of the user. It reflects a snapshot of the user’s temporal and spatial patterns from his/her check-ins. For example, a high user score indicates that the user checked in at many venues regularly or s/he visited a number of new venues. In this paper, we show how a user’s score can be used as an alternative way to investigate the user’s mobility patterns. We first characterize a set of properties from the time series of a user’s consecutive weekly scores. Based on these properties, we identify different types of users by clustering users’ common check-in patterns using non-negative matrix factorization (NMF). We then analyze the correlations between the social features of user clusters and users’ check-in patterns. We present several interesting findings. For example, users with high scores (more mobile) tend to have more friends (more social). Our empirical results demonstrate how to uncover interesting spatio-temporal patterns by utilizing the aggregate measures released by a LBSN service.     

Adaptive user modelling in car racing games using behavioural and physiological data

Personalised content adaptation has great potential to increase user engagement in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adaptive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to identify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user’s physiological data and game-related actions, and cluster them into low level primitives. These primitives are transformed and evaluated into higher level abstractions such as experience, exploration and attention. These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feedback from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions personalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation.     

Towards accurate cursorless pointing: the effects of ocular dominance and handedness

Pointing gestures are our natural way of referencing distant objects and thus widely used in HCI for controlling devices. Due to current pointing models’ inherent inaccuracies, most of the systems using pointing gestures so far rely on visual feedback showing users where they point at. However, in many environments, e.g., smart homes, it is rarely possible to display cursors since most devices do not contain a display. Therefore, we raise the question of how to facilitate accurate pointing-based interaction in a cursorless context. In this paper we present two user studies showing that previous cursorless techniques are rather inaccurate as they lack important considerations about users’ characteristics that would help in minimizing inaccuracy. We show that pointing accuracy could be significantly improved by acknowledging users’ handedness and ocular dominance. In a first user study (n=?33), we reveal the large effect of ocular dominance and handedness on human pointing behavior. Current ray-casting techniques neglect both ocular dominance and handedness as effects onto pointing behavior, precluding them from accurate cursorless selection. With a second user study (n=?25), we show that accounting for ocular dominance and handedness yields to significantly more accurate selections compared to two previously published ray-casting techniques. This speaks for the importance of considering users’ characteristics further to develop better selection techniques to foster more robust accurate selections.     

Dynamic personalization in conversational recommender systems

Conversational recommender systems are E-Commerce applications which interactively assist online users to acquire their interaction goals during their sessions. In our previous work, we have proposed and validated a methodology for conversational systems which autonomously learns the particular web page to display to the user, at each step of the session. We employed reinforcement learning to learn an optimal strategy, i.e., one that is personalized for a real user population. In this paper, we extend our methodology by allowing it to autonomously learn and update the optimal strategy dynamically (at run-time), and individually for each user. This learning occurs perpetually after every session, as long as the user continues her interaction with the system. We evaluate our approach in an off-line simulation with four simulated users, as well as in an online evaluation with thirteen real users. The results show that an optimal strategy is learnt and updated for each real and simulated user. For each simulated user, the optimal behavior is reasonably adapted to this user’s characteristics, but converges after several hundred sessions. For each real user, the optimal behavior converges only in several sessions. It provides assistance only in certain situations, allowing many users to buy several products together in shorter time and with more page-views and lesser number of query executions. We prove that our approach is novel and show how its current limitations can catered.     

An intelligent recommendation system using gaze and emotion detection

Recently, recommendation system has become popular in many e-commerce websites. It helps users by suggesting products which they could buy. Existing work till now uses past feedback of user, similarity of other users’ buying pattern, or a hybrid approach in which both type of information is used. But the pitfall of these approaches is that there is a need to collect and process huge amount of data for good recommendation. This paper is aimed at developing an efficient recommendation system by incorporating user’s emotion and interest to provide good recommendations. The proposed system does not require any of aforementioned data and works without the continuous and interminable attention of the user. In this framework, we capture user’s eye-gaze and facial expression while exploring websites through inexpensive, visible light “webcam”. The eye-gaze detection method uses pupil-center extraction of both eyes and calculates the reference point through a joint probability. The facial expression uses landmark points of face and analyzes the emotion of the user. Both methods work in approximate real time and the proposed framework thus provides intelligent recommendations on-the-fly without requirement of feedback and buying patterns of users.

     

一种新的基于重排序的相关反馈图像检索方法

提出了一种新的相关反馈方法,该方法引入了R_norm重排序机制。通过计算用户反馈的按个人兴趣排列的期望输出顺序与系统输出图像顺序之间的R_norm值,来调整各个特征的权重,从而指导下一轮的检索。新方法不需标注,减轻了用户的负担,从而避免了用户是否愿意配合的问题,而且实验表明较Rui方法在性能上有很大提高。     

Instance based personalized multi-form image browsing and retrieval

It is important to adapt and personalize image browsing and retrieval systems based on users’ preferences for improved user experience and satisfaction. In this paper, we present a novel instance based personalized multi-form image representation with implicit relevance feedback and adaptive weighting approach for image browsing and retrieval systems. In the proposed system, images are grouped into forms, which represent different information on images such as location, content etc. We conducted user interviews on image browsing, sharing and retrieval systems for understanding image browsing and searching behaviors of users. Based on the insights gained from the user interview study we propose an adaptive weighting method and implicit relevance feedback for multi-form structures that aim to improve the efficiency and accuracy of the system. Statistics of the past actions are considered for modeling the target of the users. Thus, on each iteration weights of the forms are updated adaptively. Moreover, retrieval results are modified according to the users’ preferences on iterations in order to improve personalized user experience. The proposed method has been evaluated and results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with proposed approaches in the multi-form scheme.     

A novel method for personalized music recommendation

With the development of digital music technologies, it is an interesting and useful issue to recommend the ‘favored music’ from large amounts of digital music. Some Web-based music stores can recommend popular music which has been rated by many people. However, three problems that need to be resolved in the current methods are: (a) how to recommend the ‘favored music’ which has not been rated by anyone, (b) how to avoid repeatedly recommending the ‘disfavored music’ for users, and (c) how to recommend more interesting music for users besides the ones users have been used to listen. To achieve these goals, we proposed a novel method called personalized hybrid music recommendation, which combines the content-based, collaboration-based and emotion-based methods by computing the weights of the methods according to users’ interests. Furthermore, to evaluate the recommendation accuracy, we constructed a system that can recommend the music to users after mining users’ logs on music listening records. By the feedback of the user’s options, the proposed methods accommodate the variations of the users’ musical interests and then promptly recommend the favored and more interesting music via consecutive recommendations. Experimental results show that the recommendation accuracy achieved by our method is as good as 90%. Hence, it is helpful for recommending the ‘favored music’ to users, provided that each music object is annotated with the related music emotions. The framework in this paper could serve as a useful basis for studies on music recommendation.     

Trust-inspiring explanation interfaces for recommender systems

A recommender system’s ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model for recommender agents with a focus on the agent’s trustworthiness as derived from the user’s perception of its competence and especially its ability to explain the recommended results. We present in this article new results of our work in developing design principles and algorithms for constructing explanation interfaces. We show the effectiveness of these principles via a significant-scale user study in which we compared an interface developed based on these principles with a traditional one. The new interface, called the organization interface where results are grouped according to their tradeoff properties, is shown to be significantly more effective in building user trust than the traditional approach. Users perceive it more capable and efficient in assisting them to make decisions, and they are more likely to return to the interface. We therefore recommend designers to build trust-inspiring interfaces due to their high likelihood to increase users’ intention to save cognitive effort and the intention to return to the recommender system.     

A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet

Selecting tourist attractions to visit at a destination is a main stage in planning a trip. Although various online travel recommendation systems have been developed to support users in the task of travel planning during the last decade, few systems focus on recommending specific tourist attractions. In this paper, an intelligent system to provide personalized recommendations of tourist attractions in an unfamiliar city is presented. Through a tourism ontology, the system allows integration of heterogeneous online travel information. Based on Bayesian network technique and the analytic hierarchy process (AHP) method, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Spatial web services technology is embedded in the system to provide GIS functions. In addition, the system provides an interactive geographic interface for displaying the recommendation results as well as obtaining users’ feedback. The experiments show that the system can provide personalized recommendations on tourist attractions that satisfy the user.     

Protecting query privacy in location-based services

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts—user profiles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users’ query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users’ query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users’ privacy requirements expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage.     

Efficient trace and revoke schemes

Our goal is to design encryption schemes for mass distribution of data , which enable to (1) deter users from leaking their personal keys, (2) trace the identities of users whose keys were used to construct illegal decryption devices, and (3) revoke these keys as to render the devices dysfunctional. We start by designing an efficient revocation scheme, based on secret sharing. It can remove up to t parties, is secure against coalitions of up to t users, and is more efficient than previous schemes with the same properties. We then show how to enhance the revocation scheme with traitor tracing and self-enforcement properties. More precisely, how to construct schemes such that (1) each user’s personal key contains some sensitive information of that user (e.g., the user’s credit card number), in order to make users reluctant to disclose their keys. (2) An illegal decryption device discloses the identity of users that contributed keys to construct the device. And, (3) it is possible to revoke the keys of corrupt users. For the last point, it is important to be able to do so without publicly disclosing the sensitive information.