2,981 research outputs found
Modeling Interdependent and Periodic Real-World Action Sequences
Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.Comment: Accepted at WWW 201
Legal Challenges and Market Rewards to the Use and Acceptance of Remote Sensing and Digital Information as Evidence
Bakgrund I den nutida forskningen Ă€r det essentiellt att företag tar hĂ€nsyn till medarbetarnas motivation sĂ„ att de gynnas av det arbetssĂ€tt som tillĂ€mpas. En arbetsmetod som blivit allt vanligare Ă€r konceptet Lean som ursprungligen kommer frĂ„n den japanska bilindustrin. Lean har idag utvecklats till ett allmĂ€ngiltigt koncept som tillĂ€mpas i flertalet branscher vĂ€rlden över. Trots att konceptet innebĂ€r flertalet positiva aspekter har det fĂ„tt utstĂ„ stark kritik nĂ€r det kommer till de mĂ€nskliga aspekterna och forskare har stĂ€llt sig frĂ„gan om Lean Ă€r "Mean". Kritiken hĂ€rleds frĂ€mst till medarbetares arbetsmiljö i form av stress och brist pĂ„ variation, sjĂ€lvbestĂ€mmande, hĂ€lsa och vĂ€lmĂ„ende. FĂ„ empiriska studier har dĂ€remot genomförts som undersöker konsekvenserna som Lean fĂ„r pĂ„ medarbetares upplevda motivation. Syfte VĂ„rt syfte Ă€r att undersöka och öka förstĂ„elsen för medarbetares upplevelser av motivationen i företag som tillĂ€mpar Lean. Vidare har studien för avsikt att utreda om det föreligger en paradox mellan Lean och vad som motiverar medarbetare pĂ„ en arbetsplats. Metod Studien har utgĂ„tt frĂ„n en kvalitativ metod via intervjuer. För att göra en djupare undersökning och analysera hur vĂ„rt fenomen, motivation, upplevs i en kontext med Lean tillĂ€mpade vi SmĂ„-N-studier. Vi har Ă€ven haft en iterativ forskningsansats som förenat den deduktiva och induktiva ansatsen dĂ€r studien pendlat mellan teorier och empiriska observationer fram tills det slutgiltiga resultatet. Slutsatser Utefter medarbetarnas upplevelser har vi identifierat att det inte föreligger nĂ„gon paradox mellan Lean och motivation eftersom övervĂ€gande antal medarbetare upplevde att de Ă€r motiverade Ă€ven om företaget tillĂ€mpar Lean. Dock har studien kunnat urskilja bĂ„de stödjande och motverkande faktorer nĂ€r det kommer till medarbetarnas upplevda arbetsförhĂ„llanden som i sin tur inverkar pĂ„ motivationen. De motverkande faktorerna menar vi frĂ€mst beror pĂ„ att arbetsförhĂ„llandena i somliga fall innehĂ„ller höga prestationskrav, mĂ„lstyrning samt standardiseringar. Vidare upplevs motivationen överlag som mer positiv nĂ€r företagen anvĂ€nder en mjukare form av Lean dĂ€r samtliga medlemmars intressen beaktas.Background In modern research, it is essential that companies consider employeesâ motivation so that they benefit from the applied practices. A working method that has become increasingly common is the concept Lean, which has its origin in the Japanese automotive industry. Today, Lean has evolved into a universal concept that is applied in many industries worldwide. Although the concept involves numerous positive aspects it has endured strong criticism when it comes to the human aspects and researchers have raised the question if Lean is "Mean". Criticism is derived primarily to employeesâ working conditions in terms of stress and lack, variation, autonomy, health and wellbeing. However, few empirical studies have been carried out that examines the impact that Lean has on employeesâ experienced motivation. Aim The aim is to increase the understanding of employeesâ experienced motivation in companies that practice Lean. Further on the study has the intention to investigate if there is a paradox between Lean and what motivates employees on work. Methodology The study has been conducted through a qualitative method by interviews and to be able to do a deeper examination and analyze how our phenomenon, motivation, is experienced in a Lean context we applied small-N-studies. Our strategy has been iterative, combining both a deductive and inductive approach, where the study has varied between theories and empirical observations until the final result. Conclusions We have identified that there is no paradox between Lean and motivation since the majority of employeesâ experienced that they are motivated even though the company practice Lean. Nevertheless the study shows that there are both supportive and counteractive factors when it comes to the employeesâ experienced working conditions. The counteractive factors consists foremost of high performance standards, goal steering and standardizations, and have in some cases a negative influence on the working conditions. Furthermore the experienced motivation is more positive overall when the companies use a softer form of Lean where all the membersâ interests are taken into account
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
Packet Analysis of Unmodified Bluetooth Communication Devices
Bluetooth technology has potential for widespread use within the Department of Defense and the Air Force. An office environment using Bluetooth technology can wirelessly connect computers, printers, and other office equipment in order to share information over short distances. The clutter and annoyance of cables connecting equipment can be eliminated. Bluetooth provides a standard interface for connection, as opposed to many different proprietary cables. The research is conducted indoors in a climate controlled environment, with minimal obstructions, to closely follow free-space signal propagation. Four different antenna orientations are used. The factors varied are the distance between devices, and the antenna orientation. This research determined that two of the four cards tested have a specific distance where a change from Data High rate packets and Data Medium rate are used. The change occurs at 2 meters for one and 3 meters for the other. This research also shows that manufacturers transmit identical data in identical formats. Also, this research shows that antenna orientation, and receiver signal strength indicator values have no predictive value in determining packet type used for transmission
Never-ending Learning of User Interfaces
Machine learning models have been trained to predict semantic information
about user interfaces (UIs) to make apps more accessible, easier to test, and
to automate. Currently, most models rely on datasets that are collected and
labeled by human crowd-workers, a process that is costly and surprisingly
error-prone for certain tasks. For example, it is possible to guess if a UI
element is "tappable" from a screenshot (i.e., based on visual signifiers) or
from potentially unreliable metadata (e.g., a view hierarchy), but one way to
know for certain is to programmatically tap the UI element and observe the
effects. We built the Never-ending UI Learner, an app crawler that
automatically installs real apps from a mobile app store and crawls them to
discover new and challenging training examples to learn from. The Never-ending
UI Learner has crawled for more than 5,000 device-hours, performing over half a
million actions on 6,000 apps to train three computer vision models for i)
tappability prediction, ii) draggability prediction, and iii) screen
similarity
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