In this paper, we report on findings from an ethnographic study of how people use their calendars for personal information management (PIM). Our participants were faculty, staff and students who were not required to use or contribute to any specific calendaring solution, but chose to do so anyway. The study was conducted in three parts: first, an initial survey provided broad insights into how calendars were used; second, this was followed up with personal interviews of a few participants which were transcribed and content-analyzed; and third, examples of calendar artifacts were collected to inform our analysis. Findings from our study include the use of multiple reminder alarms, the reliance on paper calendars even among regular users of electronic calendars, and wide use of calendars for reporting and life-archival purposes. We conclude the paper with a discussion of what these imply for designers of interactive calendar systems and future work in PIM research.
Several interesting design concepts have been suggested to make electronic calendar systems less error-prone and smarter. These cover a wide range, from systems that retrieve tasks from email messages @cite_7 , to systems that learn from users' behavior to recommend intelligent defaults @cite_3 , to calendar systems that predict attendance at events @cite_30 . @cite_36 assessed the effectiveness of a priority-based calendar prototype and concluded that integration with other personal information systems (such as email) would make the system more useful for users. Visualizing calendar information on desktop computers and mobile devices has been explored in several studies @cite_4 @cite_11 .
@cite_30 @cite_4 @cite_7 @cite_36 @cite_3 @cite_11
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This article is an interview study of calendar use and a cognitive analysis of the interactions between the design of calendars and the task of prospective remembering. The study and analysis are coordinated to present a general critique of current electronic calendar designs and to note opportunities for future designs. The interview data reveal continued use of paper calendars in a highly computerized setting. A key conclusion is that paper calendars support prospective remembering by promoting browsing of existing appointments during subsequent calendar keeping but that this advantage is compromised in current electronic designs. Other aspects of the interviews and the analyses address the representational limitations of both paper and electronic calendars. This research explores a methodology in which design implications of qualitative empirical data are understood and systematized through theoretical analyses of existing artifacts. Digital devices today have little understanding of their real-world context, and as a result they often make stupid mistakes. To improve this situation we are developing a database of world knowledge called ThoughtTreasure at the same time that we develop intelligent applications. In this paper we present one such application, SensiCal, a calendar with a degree of common sense. We discuss the pieces of common sense important in calendar management and present methods for extracting relevant information from calendar items. Manufacturers of integrated electronic office systems have included electronic versions of the calendar in almost every system they offer. This paper describes a survey of office workers, carried out to examine their use both of paper calendars and of electronic calendars that are commercially available as part of integrated office systems. It assesses the degree to which electronic calendars meet the needs of users. Our survey shows that the simple paper calendar is a tool whose power and flexibility is matched by few, if any, of the current commercially available electronic calendars. Recommendations for features that should be included in electronic calendars and automatic schedulers are included. In this paper, we describe Augur, a groupware calendar system to support personal calendaring practices, informal workplace communication, and the socio-technical evolution of the calendar system within a workgroup. Successful design and deployment of groupware calendar systems have been shown to depend on several converging, interacting perspectives. We describe calendar-based work practices as viewed from these perspectives, and present the Augur system in support of them. Augur allows users to retain the flexibility of personal calendars by anticipating and compensating for inaccurate calendar entries and idiosyncratic event names. We employ predictive user models of event attendance, intelligent processing of calendar text, and discovery of shared events to drive novel calendar visualizations that facilitate interpersonal communication. In addition, we visualize calendar access to support privacy management and long-term evolution of the calendar system. Recommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the item cold-start, i.e., when a new item is introduced in the system and no past information is available, then no effective recommendations can be produced. The item cold-start is a very common problem in practice: modern online platforms have hundreds of new items published every day. To address this problem, we propose to learn Local Collective Embeddings: a matrix factorization that exploits items' properties and past user preferences while enforcing the manifold structure exhibited by the collective embeddings. We present a learning algorithm based on multiplicative update rules that are efficient and easy to implement. The experimental results on two item cold-start use cases: news recommendation and email recipient recommendation, demonstrate the effectiveness of this approach and show that it significantly outperforms six state-of-the-art methods for item cold-start. Scheduling group meetings requires access to participants' calendars, typically located in scattered pockets or desks. Placing participants' calendars on-line and using a rule-based scheduler to find a time slot would alleviate the problem to some extent, but it often is difficult to trust the results, because correct scheduling rules are elusive, varying with the participants and the agenda of a particular meeting. What's needed is a comprehensive scheduling system that summarizes the available information for quick, flexible, and reliable scheduling. We have developed a prototype of a priority-based, graphical scheduling system called Visual Scheduler (VS). A controlled experiment comparing automatic scheduling with VS to manual scheduling demonstrated the former to be faster and less error prone. A field study conducted over six weeks at the UNC-CH Computer Science Department showed VS to be a generally useful system and provided valuable feedback on ways to enhance the functionality of the system to increase its value as a groupwork tool. In particular, users found priority-based time-slots and access to scheduling decision reasoning advantageous. VS has been in use by more than 75 faculty, staff, and graduate students since Fall 1987. Recommendation Systems have become an important research area in mobile computing. Although various recommendation systems have been developed to help users to deal with information overload, few systems focus on proactive information recommendation. This paper presents a news recommender system that proactively pushes just-in-time personalized news articles to mobile users based on user’s contextual information as well as news content. User’s information needs are estimated based on Bayesian network technique. An Analytic Hierarchy Process (AHP) Model, which supports both Content-based filtering and Collaborative filtering, is developed to rate the relevance of news articles. The weight of contexts (criteria) is automatically adjusted via individual-based and or group-based (group decision making) assignment. The experiments show that the system can push relevant news to mobile users. Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing work-flow will require significant customization to each individual user. For example, an assistant that helps schedule a particular user’s calendar will have to know that user’s scheduling preferences. This paper explores the potential of machine learning methods to automatically create and maintain such customized knowledge for personal software assistants. We describe the design of one particular learning assistant: a calendar manager, called CAP (Calendar APprentice), that learns user scheduling preferences from experience. Results are summarized from approximately five user-years of experience, during which CAP has learned an evolving set of several thousand rules that characterize the scheduling preferences of its users. Based on this experience, we suggest that machine learning methods may play an important role in future personal software assistants. This paper describes experimental results on using Winnow and Weighted-Majority based algorithms on a real-world calendar scheduling domain. These two algorithms have been highly studied in the theoretical machine learning literature. We show here that these algorithms can be quite competitive practically, outperforming the decision-tree approach currently in use in the Calendar Apprentice system in terms of both accuracy and speed. One of the contributions of this paper is a new variant on the Winnow algorithm (used in the experiments) that is especially suited to conditions with string-valued classifications, and we give a theoretical analysis of its performance. In addition we show how Winnow can be applied to achieve a good accuracy coverage tradeoff and explore issues that arise such as concept drift. We also provide an analysis of a policy for discarding predictors in Weighted-Majority that allows it to speed up as it learns. Reducing energy consumption has become an important issue in designing hardware and software systems in recent years. Although low power hardware components are critical for reducing energy consumption, the switching activity, which is the main source of dynamic power dissipation in electronic systems, is largely determined by the software running on these systems. In this paper, we present and evaluate several instruction scheduling algorithms that reorder a given sequence of instructions taking into account the energy considerations. We first compare a performance-oriented scheduling technique with three energy-oriented instruction scheduling algorithms from both performance (execution cycles of the resulting schedules) and energy consumption points of view. Then, we propose three scheduling algorithms that consider energy and performance at the same time. Our experimentation with these scheduling techniques shows that the best scheduling from the performance perspective is not necessarily the best scheduling from the energy perspective. Further, scheduling techniques that consider both energy and performance simultaneously are found to be desirable, that is, these techniques are quite successful in reducing energy consumption and their performance (in terms of execution cycles) is comparable to that of a pure performance-oriented scheduling. We also illuminate the inherent approximations and difficulties in building energy models for enabling energy-aware instruction scheduling and explore alternative options using cycle-accurate energy simulator. The simulation results show that the energy-oriented scheduling reduces energy consumption by up to 30 compared to the performance-oriented scheduling. The frequently changing user preferences and or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach. One subclass of human computation applications are those directed at tasks that involve planning (e.g. tour planning) and scheduling (e.g. conference scheduling). Interestingly, work on these systems shows that even primitive forms of automated oversight on the human contributors helps in significantly improving the effectiveness of the humans crowd. In this paper, we argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering the crowd. Straightforward adaptation of current planning technology is however hampered by the mismatch between the capabilities of human workers and automated planners. We identify and partially address two important challenges that need to be overcome before such adaptation of planning technology can occur: (i) interpreting inputs of the human workers (and the requester) and (ii) steering or critiquing plans produced by the human workers, armed only with incomplete domain and preference models. To these ends, we describe the implementation of AI-MIX, a tour plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers; and present a preliminary evaluation of the effectiveness of steering provided by automated planning. In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events. This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5 @math 3.5 ) for a previously published four class card pip recognition task and an accuracy of 84.9 @math 1.9 for a new more difficult 36 class character recognition task. Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time. This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction utility in push-based systems. It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23 and the total duration improved by 10 , compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month. The increasing mass of information confronting a business or an individual have created a demand for information management applications. Time-based information, in particular, is an important part of many information access tasks. This paper explores how to use 3D graphics and interactive animation to design and implement visualizers that improve access to large masses of time-based information. Two new visualizers have been developed for the Information Visualizer: 1) the Spiral Calendar was designed for rapid access to an individual's daily schedule, and 2) the Time Lattice was designed for analyzing the time relationships among the schedules of groups of people. The Spiral Calendar embodies a new 3D graphics technique for integrating detail and context by placing objects in a 3D spiral. It demonstrates that advanced graphics techniques can enhance routine office information tasks. The Time Lattice is formed by aligning a collection of 2D calendars. 2D translucent shadows provide views and interactive access to the resulting complex 3D object. The paper focuses on how these visualizations were developed. The Spiral Calendar, in particular, has gone through an entire cycle of development, including design, implementation, evaluation, revision and reuse. Our experience should prove useful to others developing user interfaces based on advanced graphics. Recent trends in System-on-a-Chip show that an increasing number of special-purpose processors are being added to improve the efficiency of common operations. Unfortunately, the use of these processors may introduce suspension delays incurred by communication, synchronization and external I O operations. When these processors are used in real-time systems, conventional schedulability analyses incorporate these delays in the worst-case execution response time, hence significantly reducing the schedulable utilization. In this paper, we provide schedulability analyses and propose segment-fixed priority scheduling for self-suspending tasks. We model the tasks as segments of execution separated by suspensions. We start from providing response-time analyses for self-suspending tasks under Rate Monotonic Scheduling (RMS). While RMS is shown to not be optimal, it can be used effectively in some special cases that we have identified. We then derive a utilization bound for the cases as a function of the ratio of the suspension duration to the period of the tasks. For general cases, we develop a segment-fixed priority scheduling scheme. Our scheme assigns individual segments different priorities and phase offsets that are used for phase enforcement to control the unexpected self-suspending nature. With the exact schedulability analysis designed for our scheme, our experiments show that the proposed scheme provides up to 40 times more schedulable utilization than RMS. From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and estimate a user's future activities given their current situation. We demonstrate Augur-powered, activity-based systems such as a phone that silences itself when the odds of you answering it are low, and a dynamic music player that adjusts to your present activity. A field deployment of an Augur-powered wearable camera resulted in 96 recall and 71 precision on its unsupervised predictions of common daily activities. A second evaluation where human judges rated the system's predictions over a broad set of input images found that 94 were rated sensible. This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.