topauthor: Tang_L* : Human-Computer Interaction Bibliography Search

[1] Daily & Hourly Adherence: Towards Understanding Activity Tracker Accuracy Late-Breaking Works: Usable, Useful, and Desirable / Tang, Lie Ming / Day, Margot / Engelen, Lina / Poronnik, Philip / Bauman, Adrian / Kay, Judy Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.3211-3218

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Summary: We tackle the important problem of understanding the accuracy of activity tracker data. To do this, we introduce the notions of daily and hourly adherence, key aspects of how consistently people wear trackers. We hypothesise that these measures provide a valuable means to address accuracy problems in population level activity tracking data. To test this, we conducted a semester-long study of 237 University students: 88 Information Technology, 149 Medical Science. We illustrate how our adherence measures provide new ways to interpret data and valuable insights that take account of tracker data accuracy. Finally, we discuss broader roles for daily and hourly adherence measures in activity tracker data.

[2] Personalized Recommendation via Parameter-Free Contextual Bandits Session 4B: Recommending / Tang, Liang / Jiang, Yexi / Li, Lei / Zeng, Chunqiu / Li, Tao Proceedings of the 2015 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2015-08-09 p.323-332

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Summary: Personalized recommendation services have gained increasing popularity and attention in recent years as most useful information can be accessed online in real-time. Most online recommender systems try to address the information needs of users by virtue of both user and content information. Despite extensive recent advances, the problem of personalized recommendation remains challenging for at least two reasons. First, the user and item repositories undergo frequent changes, which makes traditional recommendation algorithms ineffective. Second, the so-called cold-start problem is difficult to address, as the information for learning a recommendation model is limited for new items or new users. Both challenges are formed by the dilemma of exploration and exploitation. In this paper, we formulate personalized recommendation as a contextual bandit problem to solve the exploration/exploitation dilemma. Specifically in our work, we propose a parameter-free bandit strategy, which employs a principled resampling approach called online bootstrap, to derive the distribution of estimated models in an online manner. Under the paradigm of probability matching, the proposed algorithm randomly samples a model from the derived distribution for every recommendation. Extensive empirical experiments on two real-world collections of web data (including online advertising and news recommendation) demonstrate the effectiveness of the proposed algorithm in terms of the click-through rate. The experimental results also show that this proposed algorithm is robust in the cold-start situation, in which there is no sufficient data or knowledge to tune the hyper-parameters.

[3] Deal or deceit: detecting cheating in distribution channels DB Session 5: Systems and Applications / Shu, Kai / Luo, Ping / Li, Wan / Yin, Peifeng / Tang, Linpeng Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.1419-1428

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Summary: Distribution channel is a system that partners move products from manufacturer to end users. To increase sales, it is quite common for manufacturers to adjust the product prices to partners according to the product volume per deal. However, the price adjustment is like a double-edged sword. It also spurs some partners to form a cheating alliance, where a cheating seller applies for a falsified big deal with a low price and then re-sells the products to the cheating buyers. Since these cheating behaviors are harmful to a healthy ecosystem of distribution channel, we need the automatic method to guide the tedious audit process.
Thus, in this study we propose the method to rank all partners by the degree of cheating, either as seller or buyer. It is mainly motivated by the observation that the sales volumes of a cheating seller and its corresponding cheating buyer are often negatively correlated with each other. Specifically, the proposed framework consists of three parts: 1) an asymmetric correlation measure which is needed to distinguish cheating sellers from cheating buyers; 2) a systematic approach which is needed to remove false positive pairs, i.e., two partners whose sale correlation is purely coincident; 3) finally, a probabilistic model to measure the degree of cheating behaviors for each partner.
Based on the 4-year channel data of an IT company we empirically show how the proposed method outperforms the other baseline ones. It is worth mentioning that with the proposed unsupervised method more than half of the partners in the resultant top-30 ranking list are true cheating partners.

[4] iMiner: Mining Inventory Data for Intelligent Management Demo Session 2 / Li, Lei / Shen, Chao / Wang, Long / Zheng, Li / Jiang, Yexi / Tang, Liang / Li, Hongtai / Zhang, Longhui / Zeng, Chunqiu / Li, Tao / Tang, Jun / Liu, Dong Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.2057-2059

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Summary: Inventory management refers to tracing inventory levels, orders and sales of a retailing business. In the current retailing market, a tremendous amount of data regarding stocked goods (items) in an inventory will be generated everyday. Due to the increasing volume of transaction data and the correlated relations of items, it is often a non-trivial task to efficiently and effectively manage stocked goods. In this demo, we present an intelligent system, called iMiner, to ease the management of enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data, and incorporate the latest advances of data mining technologies into the system to perform the tasks of inventory management, e.g., forecasting inventory, detecting abnormal items, and analyzing inventory aging. Since 2014, iMiner has been deployed as the major inventory management platform of ChangHong Electric Co., Ltd, one of the world's largest TV selling companies in China.

[5] Ensemble contextual bandits for personalized recommendation Cold start and hybrid recommenders / Tang, Liang / Jiang, Yexi / Li, Lei / Li, Tao Proceedings of the 2014 ACM Conference on Recommender Systems 2014-10-06 p.73-80

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Summary: The cold-start problem has attracted extensive attention among various online services that provide personalized recommendation. Many online vendors employ contextual bandit strategies to tackle the so-called exploration/exploitation dilemma rooted from the cold-start problem. However, due to high-dimensional user/item features and the underlying characteristics of bandit policies, it is often difficult for service providers to obtain and deploy an appropriate algorithm to achieve acceptable and robust economic profit.
In this paper, we explore ensemble strategies of contextual bandit algorithms to obtain robust predicted click-through rate (CTR) of web objects. The ensemble is acquired by aggregating different pulling policies of bandit algorithms, rather than forcing the agreement of prediction results or learning a unified predictive model. To this end, we employ a meta-bandit paradigm that places a hyper bandit over the base bandits, to explicitly explore/exploit the relative importance of base bandits based on user feedbacks. Extensive empirical experiments on two real-world data sets (news recommendation and online advertising) demonstrate the effectiveness of our proposed approach in terms of CTR.

[6] Bundle recommendation in ecommerce Session 7b: more like those / Zhu, Tao / Harrington, Patrick / Li, Junjun / Tang, Lei Proceedings of the 2014 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2014-07-06 p.657-666

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Summary: Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually exposed to a set of items and they may buy multiple items in one single order. Thus, the relevance or profitability of one item may actually depend on the other items in the set. In other words, the set of recommendations is a bundle with items interacting with each other. In this paper, we introduce a novel problem called the Bundle Recommendation Problem (BRP). By solving the BRP, we are able to find the optimal bundle of items to recommend with respect to preferred business objective. However, BRP is a large-scale NP-hard problem. We then show that it may be sufficient to solve a significantly smaller version of BRP depending on properties of input data. This allows us to solve BRP in real-world applications with millions of users and items. Both offline and online experimental results on a Walmart.com demonstrate the incremental value of solving BRP across multiple baseline models.

[7] Searching similar segments over textual event sequences DB track: data streams and probabilistic queries / Tang, Liang / Li, Tao / Chen, Shu-Ching / Zhu, Shunzhi Proceedings of the 2013 ACM Conference on Information and Knowledge Management 2013-10-27 p.329-338

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Summary: Sequential data is prevalent in many scientific and commercial applications such as bioinformatics, system security and networking. Similarity search has been widely studied for symbolic and time series data in which each data object is a symbol or numeric value. Textual event sequences are sequences of events, where each object is a message describing an event. For example, system logs are typical textual event sequences and each event is a textual message recording internal system operations, statuses, configuration modifications or execution errors. Similar segments of an event sequence reveals similar system behaviors in the past which are helpful for system administrators to diagnose system problems. Existing search indexing for textual data only focus on unordered data. Substring matching methods are able to efficiently find matched segments over a sequence, however, their sequences are single values rather than texts. In this paper, we propose a method, suffix matrix, for efficiently searching similar segments over textual event sequences. It provides an integration of two disparate techniques: locality-sensitive hashing and suffix arrays. This method also supports the k-dissimilar segment search. A k-dissimilar segment is a segment that has at most k dissimilar events to the query sequence. By using random sequence mask proposed in this paper, this method can have a high probability to reach all k-dissimilar segments without increasing much search cost. We conduct experiments on real system log data and the experimental results show that our proposed method outperforms alternative methods using existing techniques.

[8] Automatic ad format selection via contextual bandits Industry session / Tang, Liang / Rosales, Romer / Singh, Ajit / Agarwal, Deepak Proceedings of the 2013 ACM Conference on Information and Knowledge Management 2013-10-27 p.1587-1594

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Summary: Visual design plays an important role in online display advertising: changing the layout of an online ad can increase or decrease its effectiveness, measured in terms of click-through rate (CTR) or total revenue. The decision of which layout to use for an ad involves a trade-off: using a layout provides feedback about its effectiveness (exploration), but collecting that feedback requires sacrificing the immediate reward of using a layout we already know is effective (exploitation). To balance exploration with exploitation, we pose automatic layout selection as a contextual bandit problem. There are many bandit algorithms, each generating a policy which must be evaluated. It is impractical to test each policy on live traffic. However, we have found that offline replay (a.k.a. exploration scavenging) can be adapted to provide an accurate estimator for the performance of ad layout policies at LinkedIn, using only historical data about the effectiveness of layouts. We describe the development of our offline replayer, and benchmark a number of common bandit algorithms.

[9] Scaling matrix factorization for recommendation with randomness Posters: behavioral analysis and personalization / Tang, Lei / Harrington, Patrick Companion Proceedings of the 2013 International Conference on the World Wide Web 2013-05-13 v.2 p.39-40

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Summary: Recommendation is one of the core problems in eCommerce. In our application, different from conventional collaborative filtering, one user can engage in various types of activities in a sequence. Meanwhile, the number of users and items involved are quite huge, entailing scalable approaches. In this paper, we propose one simple approach to integrate multiple types of user actions for recommendation. A two-stage randomized matrix factorization is presented to handle large-scale collaborative filtering where alternating least squares or stochastic gradient descent is not viable. Empirical results show that the method is quite scalable, and is able to effectively capture correlations between different actions, thus making more relevant recommendations.

[10] An English-translated parallel corpus for the CJK Wikipedia collections / Tang, Ling-Xiang / Geva, Shlomo / Trotman, Andrew Proceedings of ADCS'12, Australasian Document Computing Symposium 2012-12-05 p.104-110

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Summary: In this paper, we describe a machine-translated parallel English corpus for the NTCIR Chinese, Japanese and Korean (CJK) Wikipedia collections. This document collection is named CJK2E Wikipedia XML corpus. The corpus could be used by the information retrieval research community and knowledge sharing in Wikipedia in many ways; for example, this corpus could be used for experimentations in cross-lingual information retrieval, cross-lingual link discovery, or omni-lingual information retrieval research. Furthermore, the translated CJK articles could be used to further expand the current coverage of the English Wikipedia.

[11] Incorporating occupancy into frequent pattern mining for high quality pattern recommendation KM track: pattern mining / Tang, Linpeng / Zhang, Lei / Luo, Ping / Wang, Min Proceedings of the 2012 ACM Conference on Information and Knowledge Management 2012-10-29 p.75-84

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Summary: Mining interesting patterns from transaction databases has attracted a lot of research interest for more than a decade. Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. In this paper, we introduce a new measure of pattern interestingness, occupancy. The measure of occupancy is motivated by some real-world pattern recommendation applications which require that any interesting pattern X should occupy a large portion of the transactions it appears in. Namely, for any supporting transaction t of pattern X, the number of items in X should be close to the total number of items in t. In these pattern recommendation applications, patterns with higher occupancy may lead to higher recall while patterns with higher frequency lead to higher precision. With the definition of occupancy we call a pattern dominant if its occupancy is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both frequent and dominant. Additionally, we also formulate the problem of mining top-k qualified patterns: finding the qualified patterns with the top-k values of any function (e.g. weighted sum of both occupancy and support).
The challenge to these tasks is that the monotone or anti-monotone property does not hold on occupancy. In other words, the value of occupancy does not increase or decrease monotonically when we add more items to a given itemset. Thus, we propose an algorithm called DOFIA (DOminant and Frequent Itemset mining Algorithm), which explores the upper bound properties on occupancy to reduce the search process. The tradeoff between bound tightness and computational complexity is also systematically addressed. Finally, we show the effectiveness of DOFIA in a real-world application on print-area recommendation for Web pages, and also demonstrate the efficiency of DOFIA on several large synthetic data sets.

[12] LogSig: generating system events from raw textual logs Topics and events / Tang, Liang / Li, Tao / Perng, Chang-Shing Proceedings of the 2011 ACM Conference on Information and Knowledge Management 2011-10-24 p.785-794

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Summary: Modern computing systems generate large amounts of log data. System administrators or domain experts utilize the log data to understand and optimize system behaviors. Most system logs are raw textual and unstructured. One main fundamental challenge in automated log analysis is the generation of system events from raw textual logs. Log messages are relatively short text messages but may have a large vocabulary, which often result in poor performance when applying traditional text clustering techniques to the log data. Other related methods have various limitations and only work well for some particular system logs. In this paper, we propose a message signature based algorithm logSig to generate system events from textual log messages. By searching the most representative message signatures, logSig categorizes log messages into a set of event types. logSig can handle various types of log data, and is able to incorporate human's domain knowledge to achieve a high performance. We conduct experiments on five real system log data. Experiments show that logSig outperforms other alternative algorithms in terms of the overall performance.

[13] Large-scale behavioral targeting with a social twist Social, search, and other behaviour / Liu, Kun / Tang, Lei Proceedings of the 2011 ACM Conference on Information and Knowledge Management 2011-10-24 p.1815-1824

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Summary: Behavioral targeting (BT) is a widely used technique for online advertising. It leverages information collected on an individual's web-browsing behavior, such as page views, search queries and ad clicks, to select the ads most relevant to user to display. With the proliferation of social networks, it is possible to relate the behavior of individuals and their social connections. Although the similarity among connected individuals are well established (i.e., homophily), it is still not clear whether and how we can leverage the activities of one's friends for behavioral targeting; whether forecasts derived from such social information are more accurate than standard behavioral targeting models. In this paper, we strive to answer these questions by evaluating the predictive power of social data across 60 consumer domains on a large online network of over 180 million users in a period of two and a half months. To our best knowledge, this is the most comprehensive study of social data in the context of behavioral targeting on such an unprecedented scale. Our analysis offers interesting insights into the value of social data for developing the next generation of targeting services.

[14] Enhancing accessibility of microblogging messages using semantic knowledge Poster session: databases / Hu, Xia / Tang, Lei / Liu, Huan Proceedings of the 2011 ACM Conference on Information and Knowledge Management 2011-10-24 p.2465-2468

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Summary: The volume of microblogging messages is increasing exponentially with the popularity of microblogging services. With a large number of messages appearing in user interfaces, it hinders user accessibility to useful information buried in disorganized, incomplete, and unstructured text messages. In order to enhance user accessibility, we propose to aggregate related microblogging messages into clusters and automatically assign them semantically meaningful labels. However, a distinctive feature of microblogging messages is that they are much shorter than conventional text documents. These messages provide inadequate term co occurrence information for capturing semantic associations. To address this problem, we propose a novel framework for organizing unstructured microblogging messages by transforming them to a semantically structured representation. The proposed framework first captures informative tree fragments by analyzing a parse tree of the message, and then exploits external knowledge bases (Wikipedia and WordNet) to enhance their semantic information. Empirical evaluation on a Twitter dataset shows that our framework significantly outperforms existing state-of-the-art methods.

[15] Communicating Image Content Computer Systems: CS1 - Information Display and Control / Tang, Lisa / Carter, Jim A. Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting 2011-09-19 p.495-499

doi: 10.1177/1071181311551102

Link to HFES Digital Content

Summary: While a picture is worth a thousand words, do you know what those words are communicating? The Internet is filled with visual graphics to present information, complement textual content, and/or add visual appeal. What happens if you or the user cannot see the images or visual content? How will you get the same information? Although containers for providing textual information (known as alternative text) for images already exist, most web pages do not utilize them. Even when alternative text is available, the descriptions are often vague and uninformative. This paper reports on activities to provide guidance, a procedure for describing images, and a tool to support the creation of informative alternative text for all types of images. Evaluations of the procedure and tool confirm the promise of this approach, and have identified several improvements to increase usability.

[16] Verbalizing Images Part V / Inclusive Design and Accessibility / Tang, Lisa / Carter, Jim A. HCI International 2011: 14th International Conference on HCI - Posters' Extended Abstracts, Part I 2011-07-09 v.5 p.394-398

Keywords: alternative text; images; description; captions

Link to Digital Content at Springer

Summary: Although a picture is worth a thousand words, how can you communicate its meaning and content in less than 250 words when that is all you have? Images are often used to convey information, supplement textual content, and/or add visual appeal to documents. The Usability Engineering Lab (USERLab) at the University of Saskatchewan developed an approach for generating informative alternative text for all types of images. This paper describes the approach and reports on the results of applying the approach by developers, content providers, usability and accessibility specialists, and the general public users.

[17] Experiencing Accessibility Issues and Options Part V / Inclusive Design and Accessibility / Tang, Lisa / Fourney, David W. / Carter, Jim A. HCI International 2011: 14th International Conference on HCI - Posters' Extended Abstracts, Part I 2011-07-09 v.5 p.399-403

Keywords: Accessibility; demonstrations; experiences; web accessibility

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Summary: This paper introduces a comprehensive and well structured set of accessibility demonstration experiences (ADEs) that are accessible to a wide range of users. The ADEs are designed to help students as well as software and user interface designers understand the needs and expectations of users with disabilities. They cover a wide range of issues and options in accessible computing. The paper concludes with a discussion of lessons learned both about teaching using ADEs and making ADEs more truly accessible.

[18] Column-based cluster and bar axis density in parallel coordinates Parallel coordinates and graph / Tang, Lei / Li, Xue-qing / Qi, Wen-jing / Jiang, Zhi-fang Proceedings of the 2010 International Symposium on Visual Information Communication and Interaction 2010-09-28 p.9

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Summary: In this paper we organize multi-dimensional datasets with column-based approach instead of the traditional row-based method, each column referring to one dimension and we use bar axis in place of line axis to represent corresponding dimension. Then parallel coordinates with column-based cluster, bar axis density and other techniques is used to convey a large complex multi-dimensional dataset in a relative small screen through the following steps: (a) visualization of column-based clusters with user-defined granularity to simplify the corresponding dimension where we group all the data points into several discrete values; (b) several distinct colors to distinguish the lines contain different amount of data points; (c) opacity is introduced to visualization to tell the difference among the lines with the same color; (d) brand instead of polyline to reveal the centre and the extent of each cluster; (e) layer-based drawing technique to emphasize the heavy lines and to denote the trend of multi-dimensional datasets; (f) bar axis to provide special space to illustrate the density of the dataset on each axis. Anyway, our work has two primary goals: one is to convey large dataset with legible compact vivid visualization on a limited screen area. The other one is to simultaneously reveal as many information features as possible away from clutter.

[19] Column-based Cluster and Bar Axis Density in Parallel Coordinates Poster Session / Tang, Lei Proceedings of the 2010 International Symposium on Visual Information Communication and Interaction 2010-09-28 p.P3

[20] Scalable learning of collective behavior based on sparse social dimensions KM link analysis and social computing / Tang, Lei / Liu, Huan Proceedings of the 2009 ACM Conference on Information and Knowledge Management 2009-11-02 p.1107-1116

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Summary: The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension based approach is adopted to address the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. The scale of networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.

[21] Large scale multi-label classification via metalabeler Data mining/session: learning / Tang, Lei / Rajan, Suju / Narayanan, Vijay K. Proceedings of the 2009 International Conference on the World Wide Web 2009-04-20 p.211-220

Keywords: hierarchical classification, large scale, meta model, metalabeler, multi-label classification, query categorization

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Summary: The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.

[22] Secondary Encoding COMPUTER SYSTEMS: CS3 - Accessibility / Tang, Lisa / Fourney, David / Huang, Fei / Carter, Jim Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting 2008-09-22 v.52 p.561-565

Link to HFES Digital Content

Summary: Translation between media is often used to make information encoded in one modality available to users who are unable to make use of that modality. Translation acts on the primary means of encoding the information (e.g., text, sound). Secondary encoding (e.g., color, intonations) is often added to primarily encoded information to help the user correctly interpret its intended meaning. During translation between media, secondary encodings are often removed and their information remains inaccessible. When this happens, miscommunications may occur. Although secondary encoding is a multi-discipline problem, different types of secondary encoding are usually analyzed as separate problems. In reality, they are aspects of a single problem. This paper presents a taxonomy of secondary encodings and guidance for improving the accessibility of secondary encoded information.

[23] A method for measuring co-authorship relationships in MediaWiki Wiki writers / Tang, Libby Veng-Sam / Biuk-Aghai, Robert P. / Fong, Simon Proceedings of the 2008 International Symposium on Wikis 2008-09-08 p.16

Keywords: analysis, co-authorship, wiki

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Summary: Collaborative writing through wikis has become increasingly popular in recent years. When users contribute to a wiki article they implicitly establish a co-authorship relationship. Discovering these relationships can be of value, for example in finding experts on a given topic. However, it is not trivial to determine the main co-authors for a given author among the potentially thousands who have contributed to a given author's edit history. We have developed a method and algorithm for calculating a co-authorship degree for a given pair of authors. We have implemented this method as an extension for the MediaWiki system and demonstrate its performance which is satisfactory in the majority of cases. This paper also presents a method of determining an expertise group for a chosen topic.

[24] Designing an intelligent user interface for instructional video indexing and browsing Short papers / Tang, Lijun / Kender, John R. Proceedings of the 2006 International Conference on Intelligent User Interfaces 2006-01-29 p.318-320

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Summary: Instructional videos are used intensively in universities for remote education and e-learning, and a typical university course consists of videos of more than two thousand minutes in total length. This paper presents a novel graphics user interface for indexing and browsing such extensive but thematically related content. We present how the interface automatically extracts semantic indices from the visual content, and then presents both high- and low-level cues from five different conceptual viewpoints. We detail each of these novel UI units, and show how they are integrated into a user-adjustable main framework, and interconnected and navigated through user mouse events.

[25] A portable system for anywhere interactions Demonstrations / Sukaviriya, Noi / Kjeldsen, Rick / Pinhanez, Claudio / Tang, Lijun / Levas, Anthony / Pingali, Gopal / Podlaseck, Mark Proceedings of ACM CHI 2004 Conference on Human Factors in Computing Systems 2004-04-24 v.2 p.789-790

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Summary: Interactions have taken off from the confinement of a single screen into various personal devices. Projected an interface onto different parts of a physical environment is an escape beyond traditional display devices. Imagine that any walls or floors can turn into a direct manipulation space without a lot of effort. This demonstration of ED-lite, a combination of a laptop, custom software, off-the-shelf digital camera and projector, shows projected interfaces with interactions on any surfaces including those not necessarily perpendicular to the projector. ED-lite is a derivation of our previous work on Everywhere Displays (ED) and steerable interfaces. This portable version has an automatic calibration feature that makes applications usable on any surfaces in a drop. More importantly, it is now possible to be taken on the road for demonstrations.