96,186 research outputs found
A new methodology to evaluate Human Workload State Inference
Electronics and automation are increasingly part
of our daily lives, and led to the introduction of systems and
intelligent machines to which the human work is delegated and
that collaborate and support the user in the conduct of mancritical
operations.
The University of Modena and Reggio Emilia is partner of
the european project “Designing Dynamic Distributed
Cooperative Human-Machine Systems” (D3CoS,
www.d3cos.eu), that aim is the definition of affordable methods,
techniques and tools which go beyond assistance systems and
consequently address the specification, development and
evaluation of cooperative systems from a multi-agent
perspective where human and machine agents are in charge of
common tasks, assigned to the system as a whole. The key on
which to base the cooperation between the machine and the
human is the amount of workload of the human operator.
So we were involved into investigate aspects of the functional
state of human operators interacting with the demonstrator in
the unmanned aerial vehicle (UAV) and maritime domains. We
analyzed and correlated objective psycho-physiological
measures: eye blinks, respiration rate and amplitude, electro
dermal activity, heart rate variability, blood pressure; with
subjective self-assessed measure evaluated with two
questionnaires: NASA-TLX and Rating Scale Mental Effort
(RSME); with the aim to realize a mathematical regression
model for classifying the mental operators workload.
Keywords— Workload, psycho-physiological measures,
statistical analysis, cooperative systems
Multi-level Memory for Task Oriented Dialogs
Recent end-to-end task oriented dialog systems use memory architectures to
incorporate external knowledge in their dialogs. Current work makes simplifying
assumptions about the structure of the knowledge base, such as the use of
triples to represent knowledge, and combines dialog utterances (context) as
well as knowledge base (KB) results as part of the same memory. This causes an
explosion in the memory size, and makes the reasoning over memory harder. In
addition, such a memory design forces hierarchical properties of the data to be
fit into a triple structure of memory. This requires the memory reader to infer
relationships across otherwise connected attributes. In this paper we relax the
strong assumptions made by existing architectures and separate memories used
for modeling dialog context and KB results. Instead of using triples to store
KB results, we introduce a novel multi-level memory architecture consisting of
cells for each query and their corresponding results. The multi-level memory
first addresses queries, followed by results and finally each key-value pair
within a result. We conduct detailed experiments on three publicly available
task oriented dialog data sets and we find that our method conclusively
outperforms current state-of-the-art models. We report a 15-25% increase in
both entity F1 and BLEU scores.Comment: Accepted as full paper at NAACL 201
Skill Rating by Bayesian Inference
Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure
An End-to-End Conversational Style Matching Agent
We present an end-to-end voice-based conversational agent that is able to
engage in naturalistic multi-turn dialogue and align with the interlocutor's
conversational style. The system uses a series of deep neural network
components for speech recognition, dialogue generation, prosodic analysis and
speech synthesis to generate language and prosodic expression with qualities
that match those of the user. We conducted a user study (N=30) in which
participants talked with the agent for 15 to 20 minutes, resulting in over 8
hours of natural interaction data. Users with high consideration conversational
styles reported the agent to be more trustworthy when it matched their
conversational style. Whereas, users with high involvement conversational
styles were indifferent. Finally, we provide design guidelines for multi-turn
dialogue interactions using conversational style adaptation
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
- …