Mixed-Tenancy in the Wild - Applicability of Mixed-Tenancy for Real-World Enterprise SaaS-Applications

2014 ◽  
Author(s):  
Stefan T. Ruehl ◽  
Malte Rupprecht ◽  
Bjorn Morr ◽  
Matthias Reinhardt ◽  
Stephan A. W. Verclas
Keyword(s):  
Real World ◽  
In The Wild

scholarly journals Understanding Smartwatch Battery Utilization in the Wild

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3784 ◽  
Author(s):  
Morteza Homayounfar ◽  
Amirhossein Malekijoo ◽  
Aku Visuri ◽  
Chelsea Dobbins ◽  
Ella Peltonen ◽  
...  
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Indexing Method ◽  
In The Wild ◽  
Art Research ◽  
Battery Discharge ◽  
Changes Over Time ◽  
Application Developers

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

The ontogeny of a mammalian cognitive map in the real world

Science ◽  
2020 ◽  
Vol 369 (6500) ◽  
pp. 194-197 ◽  
Author(s):  
Lee Harten ◽  
Amitay Katz ◽  
Aya Goldshtein ◽  
Michal Handel ◽  
Yossi Yovel
Keyword(s):  
Real World ◽  
Large Scale ◽  
Cognitive Maps ◽  
Cognitive Map ◽  
Interindividual Differences ◽  
The Real ◽  
Fruit Bat ◽  
Wild Fruit ◽  
In The Wild

How animals navigate over large-scale environments remains a riddle. Specifically, it is debated whether animals have cognitive maps. The hallmark of map-based navigation is the ability to perform shortcuts, i.e., to move in direct but novel routes. When tracking an animal in the wild, it is extremely difficult to determine whether a movement is truly novel because the animal’s past movement is unknown. We overcame this difficulty by continuously tracking wild fruit bat pups from their very first flight outdoors and over the first months of their lives. Bats performed truly original shortcuts, supporting the hypothesis that they can perform large-scale map-based navigation. We documented how young pups developed their visual-based map, exemplifying the importance of exploration and demonstrating interindividual differences.

Testing in the Wild: The Social and Organisational Dimensions of Real World Practice

2009 ◽  
Vol 18 (5-6) ◽  
pp. 559-580 ◽  
Author(s):  
John Rooksby ◽  
Mark Rouncefield ◽  
Ian Sommerville
Keyword(s):  
Real World ◽  
The Social ◽  
In The Wild

scholarly journals Energy Monitoring in the Wild: Platform Development and Lessons Learned from a Real-World Demonstrator

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5786
Author(s):  
Filipe Quintal ◽  
Daniel Garigali ◽  
Dino Vasconcelos ◽  
Jonathan Cavaleiro ◽  
Wilson Santos ◽  
...  
Keyword(s):  
Real World ◽  
Lessons Learned ◽  
Long Term Study ◽  
Consumption Data ◽  
Custom Made ◽  
In The Wild ◽  
Platform Development ◽  
A Site

This paper presents the development and evaluation of EnnerSpectrum, a platform for electricity monitoring. The development was motivated by a gap between academic, fully custom-made monitoring solutions and commercial proprietary monitoring approaches. EnnerSpectrum is composed of two main entities, the back end, and the Gateway. The back end is a server comprised of flexible entities that can be configured to different monitoring scenarios. The Gateway interacts with equipment at a site that cannot interact directly with the back end. The paper presents the architecture and configuration of EnnerSpectrum for a long-term case study with 13 prosumers of electricity for approximately 36 months. During this period, the proposed system was able to adapt to several building and monitoring conditions while acquiring 95% of all the available consumption data. To finalize, the paper presents a set of lessons learned from running such a long-term study in the real world.

scholarly journals How to Effectively Collect and Process Network Data for Intrusion Detection?

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1532
Author(s):  
Mikołaj Komisarek ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Witold Hołubowicz ◽  
Michał Choraś
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
Minimal Amount ◽  
Security Breaches ◽  
Network Intrusion ◽  
Benchmark Datasets ◽  
In The Wild

The number of security breaches in the cyberspace is on the rise. This threat is met with intensive work in the intrusion detection research community. To keep the defensive mechanisms up to date and relevant, realistic network traffic datasets are needed. The use of flow-based data for machine-learning-based network intrusion detection is a promising direction for intrusion detection systems. However, many contemporary benchmark datasets do not contain features that are usable in the wild. The main contribution of this work is to cover the research gap related to identifying and investigating valuable features in the NetFlow schema that allow for effective, machine-learning-based network intrusion detection in the real world. To achieve this goal, several feature selection techniques have been applied on five flow-based network intrusion detection datasets, establishing an informative flow-based feature set. The authors’ experience with the deployment of this kind of system shows that to close the research-to-market gap, and to perform actual real-world application of machine-learning-based intrusion detection, a set of labeled data from the end-user has to be collected. This research aims at establishing the appropriate, minimal amount of data that is sufficient to effectively train machine learning algorithms in intrusion detection. The results show that a set of 10 features and a small amount of data is enough for the final model to perform very well.

scholarly journals How do 3-year-olds use relevance inferencing to interpret indirect speech?

2021 ◽  
pp. 014272372110435
Author(s):  
Kirsten Abbot-Smith ◽  
Cornelia Schulze ◽  
Nefeli Anagnostopoulou ◽  
Maria Zajączkowska ◽  
Danielle Matthews
Keyword(s):  
Theory Of Mind ◽  
Real World ◽  
Cognitive Skills ◽  
Sentence Comprehension ◽  
Outcome Variable ◽  
Pragmatic Language ◽  
World Knowledge ◽  
Language Interpretation ◽  
Pragmatic Language Skills ◽  
In The Wild

If a child asks a friend to play football and the friend replies, ‘I have a cough’, the requesting child must make a ‘relevance inference’ to determine the communicative intent. Relevance inferencing is a key component of pragmatics, that is, the ability to integrate social context into language interpretation and use. We tested which cognitive skills relate to relevance inferencing. In addition, we asked whether children’s lab-based pragmatic performance relates to children’s parent-assessed pragmatic language skills. We tested 3.5- to 4-year-old speakers of British English (Study 1: N = 40, Study 2: N = 32). Children were presented with video-recorded vignettes ending with an utterance requiring a relevance inference, for which children made a forced choice. Study 1 measured children’s Theory of Mind, their sentence comprehension and their real-world knowledge and found that only real-world knowledge retained significance in a regression analysis with children’s relevance inferencing as the outcome variable. Study 2 then manipulated children’s world-knowledge through priming but found this did not improve children’s performance on the relevance inferencing task. Study 2 did, however, reveal a significant correlation between children’s relevance inferencing and a measure of morpho-syntactic production. In both studies parents rated their children’s pragmatic language usage in daily life, which was found to relate to performance in our lab-based relevance inferencing task. This set of studies is the first to empirically demonstrate that lab-based measures of relevance inferencing are reflective of children’s pragmatic abilities ‘in the wild’. There was no clear association between relevance inferencing and Theory of Mind. There was mixed evidence for the role of formal language, which should be further investigated. Finally, real-world knowledge was indeed associated with relevance inferencing but future experimental work is required to test causal relations.

scholarly journals Exploration in the wild

2018 ◽  
Author(s):  
Eric Schulz ◽  
Rahul Bhui ◽  
Bradley C Love ◽  
Bastien Brier ◽  
Michael T Todd ◽  
...  
Keyword(s):  
Real World ◽  
Exploratory Behavior ◽  
Computational Models ◽  
Food Delivery ◽  
Data Set ◽  
Delivery Service ◽  
Theoretical Predictions ◽  
In The Wild ◽  
Feature Based ◽  
New Evidence

Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models have successfully explained exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to complex real world choice problems. We investigate the factors guiding exploratory behavior in a data set consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. We find evidence for several theoretical predictions: (1) customers engage in uncertainty-directed exploration, (2) they adjust their level of exploration to the average restaurant quality in a city, and (3) they use feature-based generalization to guide exploration towards promising restaurants. Our results provide new evidence that people use sophisticated strategies to explore complex, real-world environments.

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