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.
Nearly 10 years after Kelley and Chapanis' original study, Payne @cite_19 conducted interviews with 30 knowledge workers about both calendars and to-do lists, followed by a task analysis of his observations. He concluded that the central task supported by calendars was . Prospective remembering is the use of memory for remembering to do things in the future, as different from retrospective memory functions such as recalling past events.
@cite_19
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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. A rapidly growing number of recent studies show that imagining the future depends on much of the same neural machinery that is needed for remembering the past. These findings have led to the concept of the prospective brain; an idea that a crucial function of the brain is to use stored information to imagine, simulate and predict possible future events. We suggest that processes such as memory can be productively re-conceptualized in light of this idea. Twenty-three professional persons were interviewed extensively to find out how they keep their appointment calendars and to extract from that information suggestions about how calendars could be computerized. For the majority of the persons interviewed calendars are indispensable to the conduct of their business, and, in some cases, their personal lives. At the same time the data show an unexpectedly large amount of diversity in the kinds of calendars people use and in the ways they use them. Substantially more than half of the respondents have more than one calendar, with two persons using as many as six calendars at once. Portability and access from diverse locations are important for many persons. Concerns about privacy vary widely: some persons keep their calendars closely guarded, others allow free access to them. Relevant time spans covered by calendars are enormous. Some few people are concerned only with the current day and the day following, others may plan appointments a year or more in advance. A substantial number of appointments are changed after they have been made and, once again, the range is large, from about 2 per cent for some persons to about 80 per cent for others. Archiving, query patterns, and the insertion of correlated information into calendars also vary greatly among various users. Modern enterprises are replete with numerous online processes. Many must be performed frequently and are tedious, while others are done less frequently yet are complex or hard to remember. We present interviews with knowledge workers that reveal a need for mechanisms to automate the execution of and to share knowledge about these processes. In response, we have developed the CoScripter system (formerly Koala [11]), a collaborative scripting environment for recording, automating, and sharing web-based processes. We have deployed CoScripter within a large corporation for more than 10 months. Through usage log analysis and interviews with users, we show that CoScripter has addressed many user automation and sharing needs, to the extent that more than 50 employees have voluntarily incorporated it into their work practice. We also present ways people have used CoScripter and general issues for tools that support automation and sharing of how-to knowledge. In this position paper we will focus on wearable activity recognitions tools in regard to their function of detecting human activities and thus enabling the user to recall everyday experience in a new way. The capabilities of activity recognition to detect, store and present activities to the person who has performed it can not only help to recall the activities but also encourage the user to remember experiences related to the activities. In order to demonstrate this, we present two projects (cases) in which wearable activity recognition is used to support the users' recall capabilities. In the next step, we present a narrative theory of action and mind, which focuses on how humans retrospectively interpret and structure personal experience in their minds, their so called autobiographical memory. Finally, we present some further concepts and distinctions about what it means to memorize and recall personal data. 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. Episodic memory is widely conceived as a fundamentally constructive, rather than reproductive, process that is prone to various kinds of errors and illusions. With a view towards examining the functions served by a constructive episodic memory system, we consider recent neuropsychological and neuroimaging studies indicating that some types of memory distortions reflect the operation of adaptive processes. An important function of a constructive episodic memory is to allow individuals to simulate or imagine future episodes, happenings and scenarios. Since the future is not an exact repetition of the past, simulation of future episodes requires a system that can draw on the past in a manner that flexibly extracts and recombines elements of previous experiences. Consistent with this constructive episodic simulation hypothesis, we consider cognitive, neuropsychological and neuroimaging evidence showing that there is considerable overlap in the psychological and neural processes involved in remembering the past and imagining the future. Abstract A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using them to make a prediction. In this paper we consider the problem of training RNNs to predict sequences that exhibit significant long-term dependencies, focusing on a serial recall task where the RNN needs to remember a sequence of characters for a large number of steps before reconstructing it. We introduce the Temporal-Kernel Recurrent Neural Network (TKRNN), which is a variant of the RNN that can cope with long-term dependencies much more easily than a standard RNN, and show that the TKRNN develops short-term memory that successfully solves the serial recall task by representing the input string with a stable state of its hidden units. 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. A new priority queue implementation for the future event set problem is described in this article. The new implementation is shown experimentally to be O(1) in queue size for the priority increment distributions recently considered by Jones in his review article. It displays hold times three times shorter than splay trees for a queue size of 10,000 events. The new implementation, called a calendar queue, is a very simple structure of the multiple list variety using a novel solution to the overflow problem. Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task. Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. @PARASPLIT Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task. 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. History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societ al views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support. This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles. In this paper we aim at an understanding of the fundamental algorithmic limitations on what a set of autonomous mobile robots can or cannot achieve. We study a hard task for a set of weak robots. The task is for the robots in the plane to form any arbitrary pattern that is given in advance. The robots are weak in several aspects. They are anonymous; they cannot explicitly communicate with each other, but only observe the positions of the others; they cannot remember the past; they operate in a very strong form of asynchronicity. We show that the tasks that such a system of robots can perform depend strongly on their common knowledge about their environment, i.e., the readings of their environment sensors. We report on a user study that provides evidence that spaced repetition and a specific mnemonic technique enable users to successfully recall multiple strong passwords over time. Remote research participants were asked to memorize 4 Person-Action-Object (PAO) stories where they chose a famous person from a drop-down list and were given machine-generated random action-object pairs. Users were also shown a photo of a scene and asked to imagine the PAO story taking place in the scene (e.g., Bill Gates---swallowing---bike on a beach). Subsequently, they were asked to recall the action-object pairs when prompted with the associated scene-person pairs following a spaced repetition schedule over a period of 127+ days. While we evaluated several spaced repetition schedules, the best results were obtained when users initially returned after 12 hours and then in @math increasing intervals: 77 of the participants successfully recalled all 4 stories in 10 tests over a period of 158 days. Much of the forgetting happened in the first test period (12 hours): 89 of participants who remembered their stories during the first test period successfully remembered them in every subsequent round. These findings, coupled with recent results on naturally rehearsing password schemes, suggest that 4 PAO stories could be used to create usable and strong passwords for 14 sensitive accounts following this spaced repetition schedule, possibly with a few extra upfront rehearsals. In addition, we find that there is an interference effect across multiple PAO stories: the recall rate of 100 (resp. 90 ) for participants who were asked to memorize 1 PAO story (resp. 2 PAO stories) is significantly better than the recall rate for participants who were asked to memorize 4 PAO stories. These findings yield concrete advice for improving constructions of password management schemes and future user studies. Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory. A study of email responsiveness was conducted to understand how the timing of email responses conveys important information. Interviews and observations explored users' perceptions of how they responded to email and formed expectations of others' responses to them. We identified ways in which users maintain and cultivate a responsiveness image for projecting expectations about their email response. We also discuss other contextual cues people use to discover email responsiveness, which include using other tools such as the calendar and phone, accounting for the amount of work time overlap available, and establishing a pacing between email correspondents. These cues help users develop a sense of when to expect a response and when breakdown has occurred, requiring further action. Why Do We Need Memory? Perceiving and Remembering. How Many Kinds of Memory? The Evidence for STM. The Role of Memory in Cognition - Working Memory. Visual Memory and the Visuo-spatial Sketchpad. Attention and the Control of Memory. When Practice Makes Perfect. Organizing and Learning. Acquiring Habits. When Memory Fails. Retrieval. Recollection and Autobiographical Memory. Where Next? Connectionism Rides Again. Knowledge. Memory, Emotion and Cognition. Understanding Amnesia. Treating Memory Problems. Consciousness. Implicit Knowledge and Learning. Implicit Memory and Recollection.