74 research outputs found
Interaction Analytics of Software Factory Recordings
abstract: A human communications research project at Arizona State University aurally
recorded the daily interactions of aware and consenting employees and their visiting
clients at the Software Factory, a software engineering consulting team, over a three
year period. The resulting dataset contains valuable insights on the communication
networks that the participants formed however it is far too vast to be processed manually
by researchers. In this work, digital signal processing techniques are employed
to develop a software toolkit that can aid in estimating the observable networks contained
in the Software Factory recordings. A four-step process is employed that starts
with parsing available metadata to initially align the recordings followed by alignment
estimation and correction. Once aligned, the recordings are processed for common
signals that are detected across multiple participantsâ recordings which serve as a
proxy for conversations. Lastly, visualization tools are developed to graphically encode
the estimated similarity measures to efficiently convey the observable network
relationships to assist in future human communications research.Dissertation/ThesisMasters Thesis Electrical Engineering 201
Automatic Personalization of User Interfaces based on User Interaction Analytics
The default user interface (UI) of software applications is the same for all users, even though users differ in terms of their needs and preferences for using the software. UI customization is typically limited to the most advanced and/or highly active users. As a result, a significant proportion of users of a software do not reap the benefits of having a UI that is personalized to them. This disclosure describes techniques to determine and present a personalized UI to each user or an application, with the userâs permission. UI personalization is performed based on analytics of user-permitted data of user interaction and other relevant information. The analysis can be performed by a suitably trained machine learning model which outputs the optimal personalized UI for each user. Model training and execution is performed on the user device, and if the user permits, on a server that trains the model based on aggregated, non-identifiable user data
Automatically Deploying Connected and Centrally-managed Retail Demo Devices
Manufacturers of electronic devices have thousands of demonstration (demo) devices deployed in retail environments across the world. Deploying connected retail demo devices across third-party retailers is expensive, time-consuming, unscalable, and subject to human error. This disclosure describes mechanisms for automatically deploying connected and centrally-managed demo devices at retail locations. Wireless network credentials are pre-registered with a device manufacturer and included in a configuration file that is imaged onto a demo device. Upon power on, the demo device automatically searches for and connects to a retailer Wi-Fi network. The device sends heartbeats to a centralized dashboard to enable geolocating and monitoring. The device can then be remotely configured and can transmit demo interaction analytics to the centralized dashboard
Challenges and opportunities for RISC-V architectures towards genomics-based workloads
The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations.This work has been partially financed by the European Commission (EU-HORIZON NEARDATA GA.101092644, VITAMIN-V GA.101093062), the MEEP Project which received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 946002. The JU receives support from the European Unionâs Horizon 2020 research and innovation program and Spain, Croatia and Turkey. Also by the Spanish Ministry of Science (MICINN) under scholarship BES-2017-081635, the Research State Agency (AEI) and European Regional Development Funds (ERDF/FEDER) under DALEST grant agreement PID2021-126248OBI00, MCIN/AEI/10.13039/ 501100011033/FEDER and PID GA PID2019-107255GB-C21, and the Generalitat de Catalunya (AGAUR) under grant agreements 2021-SGR-00478, 2021-SGR-01626 and âFSE Invertint en el teu futurâ.Peer ReviewedPostprint (author's final draft
Challenges and Opportunities for RISC-V Architectures towards Genomics-based Workloads
The use of large-scale supercomputing architectures is a hard requirement for
scientific computing Big-Data applications. An example is genomics analytics,
where millions of data transformations and tests per patient need to be done to
find relevant clinical indicators. Therefore, to ensure open and broad access
to high-performance technologies, governments, and academia are pushing toward
the introduction of novel computing architectures in large-scale scientific
environments. This is the case of RISC-V, an open-source and royalty-free
instruction-set architecture. To evaluate such technologies, here we present
the Variant-Interaction Analytics use case benchmarking suite and datasets.
Through this use case, we search for possible genetic interactions using
computational and statistical methods, providing a representative case for
heavy ETL (Extract, Transform, Load) data processing. Current implementations
are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the
Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part
of the next MareNostrum generations. Here we describe the Variant Interaction
Use Case, highlighting the characteristics leveraging high-performance
computing, indicating the caveats and challenges towards the next RISC-V
developments and designs to come from a first comparison between x86 and RISC-V
architectures on real Variant Interaction executions over real hardware
implementations
Interaction analytics for automatic assessment of communication quality in primary care
Effective doctor-patient communication is a crucial element of health care, influencing patientsâ personal and medical outcomes following the interview.
The set of skills used in interpersonal interaction is complex, involving verbal
and non-verbal behaviour. Precise attributes of good non-verbal behaviour
are difficult to characterise, but models and studies offer insight on relevant
factors. In this PhD, I studied how the attributes of non-verbal behaviour can
be automatically extracted and assessed, focusing on turn-taking patterns of
and the prosody of patient-clinician dialogues.
I described clinician-patient communication and the tools and methods used to
train and assess communication during the consultation. I then proceeded to
a review of the literature on the existing efforts to automate assessment, depicting an emerging domain focused on the semantic content of the exchange
and a lack of investigation on interaction dynamics, notably on the structure of
turns and prosody.
To undertake the study of these aspects, I initially planned the collection of
data. I underlined the need for a system that follows the requirements of sensitive data collection regarding data quality and security. I went on to design a
secure system which records participantsâ speech as well as the body posture
of the clinician. I provided an open-source implementation and I supported its
use by the scientific community.
I investigated the automatic extraction and analysis of some non-verbal components of the clinician-patient communication on an existing corpus of GP
consultations. I outlined different patterns in the clinician-patient interaction
and I further developed explanations of known consulting behaviours, such as
the general imbalance of the doctor-patient interaction and differences in the
control of the conversation.
I compared behaviours present in face to face, telephone, and video consultations, finding overall similarities alongside noticeable differences in patterns of
overlapping speech and switching behaviour.
I further studied non-verbal signals by analysing speech prosodic features, investigating differences in participantsâ behaviour and relations between the assessment of the clinician-patient communication and prosodic features. While
limited in their interpretative power on the explored dataset, these signals
nonetheless provide additional metrics to identify and characterise variations
in the non-verbal behaviour of the participants.
Analysing clinician-patient communication is difficult even for human experts.
Automating that process in this work has been particularly challenging. I demonstrated the capacity of automated processing of non-verbal behaviours to analyse clinician-patient communication. I outlined the ability to explore new aspects, interaction dynamics, and objectively describe how patients and clinicians interact. I further explained known aspects such as clinician dominance
in more detail. I also provided a methodology to characterise participantsâ turns
taking behaviour and speech prosody for the objective appraisal of the quality of non-verbal communication. This methodology is aimed at further use in
research and education
Group-In: Group Inference from Wireless Traces of Mobile Devices
This paper proposes Group-In, a wireless scanning system to detect static or
mobile people groups in indoor or outdoor environments. Group-In collects only
wireless traces from the Bluetooth-enabled mobile devices for group inference.
The key problem addressed in this work is to detect not only static groups but
also moving groups with a multi-phased approach based only noisy wireless
Received Signal Strength Indicator (RSSIs) observed by multiple wireless
scanners without localization support. We propose new centralized and
decentralized schemes to process the sparse and noisy wireless data, and
leverage graph-based clustering techniques for group detection from short-term
and long-term aspects. Group-In provides two outcomes: 1) group detection in
short time intervals such as two minutes and 2) long-term linkages such as a
month. To verify the performance, we conduct two experimental studies. One
consists of 27 controlled scenarios in the lab environments. The other is a
real-world scenario where we place Bluetooth scanners in an office environment,
and employees carry beacons for more than one month. Both the controlled and
real-world experiments result in high accuracy group detection in short time
intervals and sampling liberties in terms of the Jaccard index and pairwise
similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under
Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The
content of this paper does not reflect the official opinion of the EU.
Responsibility for the information and views expressed therein lies entirely
with the authors. Proc. of ACM/IEEE IPSN'20, 202
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