6,178 research outputs found
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
Feeding-Back Error Patterns to Stimulate Self-Reflection versus Automated Debiasing of Judgments
Automated debiasing, referring to automatic statistical correction of human estimations, can improve accuracy, whereby benefits are limited by cases where experts derive accurate judgments but are then falsely "corrected". We present ongoing work on a feedback-based decision support system that learns a statistical model for correcting identified error patterns observed on judgments of an expert. The model is then mirrored to the expert as feedback to stimulate self-reflection and selective adjustment of further judgments instead of using it for auto-debiasing. Our assumption is that experts are capable to incorporate the feedback wisely when making another judgment to reduce overall error levels and mitigate this false-correction problem. To test the assumption, we present the design and results of a pilot-experiment conducted. Results indicate that subjects indeed use the feedback wisely and selectively to improve their judgments and overall accuracy
An Intelligent Robot and Augmented Reality Instruction System
Human-Centered Robotics (HCR) is a research area that focuses on how robots can empower people to live safer, simpler, and more independent lives. In this dissertation, I present a combination of two technologies to deliver human-centric solutions to an important population. The first nascent area that I investigate is the creation of an Intelligent Robot Instructor (IRI) as a learning and instruction tool for human pupils. The second technology is the use of augmented reality (AR) to create an Augmented Reality Instruction (ARI) system to provide instruction via a wearable interface.
To function in an intelligent and context-aware manner, both systems require the ability to reason about their perception of the environment and make appropriate decisions. In this work, I construct a novel formulation of several education methodologies, particularly those known as response prompting, as part of a cognitive framework to create a system for intelligent instruction, and compare these methodologies in the context of intelligent decision making using both technologies.
The IRI system is demonstrated through experiments with a humanoid robot that uses object recognition and localization for perception and interacts with students through speech, gestures, and object interaction. The ARI system uses augmented reality, computer vision, and machine learning methods to create an intelligent, contextually aware instructional system. By using AR to teach prerequisite skills that lend themselves well to visual, augmented reality instruction prior to a robot instructor teaching skills that lend themselves to embodied interaction, I am able to demonstrate the potential of each system independently as well as in combination to facilitate students\u27 learning.
I identify people with intellectual and developmental disabilities (I/DD) as a particularly significant use case and show that IRI and ARI systems can help fulfill the compelling need to develop tools and strategies for people with I/DD.
I present results that demonstrate both systems can be used independently by students with I/DD to quickly and easily acquire the skills required for performance of relevant vocational tasks. This is the first successful real-world application of response-prompting for decision making in a robotic and augmented reality intelligent instruction system
The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
Current smartphone operating systems regulate application permissions by
prompting users on an ask-on-first-use basis. Prior research has shown that
this method is ineffective because it fails to account for context: the
circumstances under which an application first requests access to data may be
vastly different than the circumstances under which it subsequently requests
access. We performed a longitudinal 131-person field study to analyze the
contextuality behind user privacy decisions to regulate access to sensitive
resources. We built a classifier to make privacy decisions on the user's behalf
by detecting when context has changed and, when necessary, inferring privacy
preferences based on the user's past decisions and behavior. Our goal is to
automatically grant appropriate resource requests without further user
intervention, deny inappropriate requests, and only prompt the user when the
system is uncertain of the user's preferences. We show that our approach can
accurately predict users' privacy decisions 96.8% of the time, which is a
four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure
Evaluating the Effects of Picture Exchange Communication SystemÂź (PECSÂź) Mediator Training Via Telehealth Using Behavioural Skills Training and General Case Training
The Picture Exchange Communication SystemÂź (PECSÂź) is often used by children diagnosed with autism spectrum disorder (ASD) as a means of functional communication. Although there is extensive research indicating that PECS is an evidence-based intervention for children with ASD (e.g., Wong et al., 2015), little is known about how best to train parents to support their childâs PECS use. Of those studies that do explore parent training approaches, few measure the caregiverâs fidelity implementing PECS or explore whether parents generalize or maintain skills post-training. Similarly, little is known about how to train parents to implement PECS via telehealth. The purpose of the current study was to bridge the gap between PECS and telehealth research and to explore strategies to help parents support their childâs PECS use at home. One father-mother dyad was recruited. The father was the primary training recipient (i.e., parent trainee). The mother participated in training sessions as the role play partner (i.e., surrogate parent). Researchers used behavioural skills training (BST) to teach target PECS skills and applied strategies of general case training (GCT) to actively program for generalized behaviour change. A multiple baseline design across skills was used to monitor the fatherâs fidelity during mediator training sessions and a multiple probe design was embedded to monitor both the fatherâs and motherâs fidelity in the natural environment with their child. Results demonstrated that the parent trainee acquired PECS skills within the training setting. However, parents did not reliably demonstrate all of the PECS skills in the generalization setting during follow-up
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Large language models (LLMs) have demonstrated remarkable performance across
a wide array of NLP tasks. However, their efficacy is undermined by undesired
and inconsistent behaviors, including hallucination, unfaithful reasoning, and
toxic content. A promising approach to rectify these flaws is self-correction,
where the LLM itself is prompted or guided to fix problems in its own output.
Techniques leveraging automated feedback -- either produced by the LLM itself
or some external system -- are of particular interest as they are a promising
way to make LLM-based solutions more practical and deployable with minimal
human feedback. This paper presents a comprehensive review of this emerging
class of techniques. We analyze and taxonomize a wide array of recent work
utilizing these strategies, including training-time, generation-time, and
post-hoc correction. We also summarize the major applications of this strategy
and conclude by discussing future directions and challenges.Comment: Work in Progress. Version
4D Continuous Descent Operations Supported by an Electronic Flight Bag
This paper describes a set of flight simulation
experiments carried out with the DLRâs Generic Cockpit
Simulator (GECO). A new concept named time and energy
managed operations (TEMO), which aims to enable advanced
four dimensional (4D) continuous descent operations (CDO), was
evaluated after three full days of experiments with qualified
pilots. The experiment focused to investigate the possibility of
using a 4D-controller on a modern aircraft with unmodified or
only slightly modified avionic systems. This was achieved by
executing the controller in an Electronic Flight Bag (EFB) and
using the pilot to âclose the loopâ by entering speed and other
advisories into the autopilot Flight Control Unit (FCU). The
outcome of the experiments include subjective (questionnaires
answered by pilots) and objective (trajectory logs) data. Data
analysis showed a very good acceptance (both in terms of safety
and operability of the procedure) from the participating crews,
only with minor suggestions to be improved in future versions of
the controller and the speed advisories update rates. Good time
accuracy all along the descent trajectory was also observed.Peer ReviewedPostprint (published version
PECSperts! Exploring Child and Caregiver Outcomes Following Participation in a Brief Communication Camp
Children with neurodevelopmental disorders, such as autism spectrum disorder (ASD), often have communication impairments. As a result, augmentative and alternative communication systems such as the Picture Exchange Communication SystemÂź (PECSÂź; Frost & Bondy, 2006) are often recommended. Although substantial evidence supports child PECS use, and emerging evidence supports caregiver PECS training, no research specifically explores brief caregiver and child PECS training models. As such, little is known about how to effectively train caregivers and children in an efficient manner. Further, few studies investigate whether caregivers and children generalize and maintain their PECS skills. This study explored child PECS accuracy and caregiver PECS treatment integrity following participation in a brief, 1-week caregiver and child PECS training camp that included caregiver training, child teaching, and caregiver-child coaching. Eight children diagnosed with ASD and their caregivers participated. A pre-post group design was implemented to assess caregiver and child performance in camp-clinic and home settings over time. Results suggest that average caregiver PECS treatment integrity and child PECS accuracy increased from pre- to post-intervention and caregiver treatment integrity remained stable during maintenance assessments. In contrast, child PECS accuracy was variable during maintenance assessments. Results were similar in both the camp-clinic and home settings for caregivers and children. These results indicate that a brief PECS training camp may improve caregiver and child PECS skills in both camp-clinic and home settings
CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends
CMOS Image Sensors (CIS) are key for imaging technol-ogies. These chips are conceived for capturing opticalscenes focused on their surface, and for delivering elec-trical images, commonly in digital format. CISs may incor-porate intelligence; however, their smartness basicallyconcerns calibration, error correction and other similartasks. The term CVISs (CMOS VIsion Sensors) definesother class of sensor front-ends which are aimed at per-forming vision tasks right at the focal plane. They havebeen running under names such as computational imagesensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical imple-mentation. However, while inputs of both CIS and CVISare images captured by photo-sensors placed at thefocal-plane, CVISs primary outputs may not be imagesbut either image features or even decisions based on thespatial-temporal analysis of the scenes. We may hencestate that CVISs are more âintelligentâ than CISs as theyfocus on information instead of on raw data. Actually,CVIS architectures capable of extracting and interpretingthe information contained in images, and prompting reac-tion commands thereof, have been explored for years inacademia, and industrial applications are recently ramp-ing up.One of the challenges of CVISs architects is incorporat-ing computer vision concepts into the design flow. Theendeavor is ambitious because imaging and computervision communities are rather disjoint groups talking dif-ferent languages. The Cellular Nonlinear Network Univer-sal Machine (CNNUM) paradigm, proposed by Profs.Chua and Roska, defined an adequate framework forsuch conciliation as it is particularly well suited for hard-ware-software co-design [1]-[4]. This paper overviewsCVISs chips that were conceived and prototyped at IMSEVision Lab over the past twenty years. Some of them fitthe CNNUM paradigm while others are tangential to it. Allthem employ per-pixel mixed-signal processing circuitryto achieve sensor-processing concurrency in the quest offast operation with reduced energy budget.Junta de AndalucĂa TIC 2012-2338Ministerio de EconomĂa y Competitividad TEC 2015-66878-C3-1-R y TEC 2015-66878-C3-3-
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
Advancements in deep neural networks have allowed automatic speech
recognition (ASR) systems to attain human parity on several publicly available
clean speech datasets. However, even state-of-the-art ASR systems experience
performance degradation when confronted with adverse conditions, as a
well-trained acoustic model is sensitive to variations in the speech domain,
e.g., background noise. Intuitively, humans address this issue by relying on
their linguistic knowledge: the meaning of ambiguous spoken terms is usually
inferred from contextual cues thereby reducing the dependency on the auditory
system. Inspired by this observation, we introduce the first open-source
benchmark to utilize external large language models (LLMs) for ASR error
correction, where N-best decoding hypotheses provide informative elements for
true transcription prediction. This approach is a paradigm shift from the
traditional language model rescoring strategy that can only select one
candidate hypothesis as the output transcription. The proposed benchmark
contains a novel dataset, HyPoradise (HP), encompassing more than 334,000 pairs
of N-best hypotheses and corresponding accurate transcriptions across prevalent
speech domains. Given this dataset, we examine three types of error correction
techniques based on LLMs with varying amounts of labeled
hypotheses-transcription pairs, which gains a significant word error rate (WER)
reduction. Experimental evidence demonstrates the proposed technique achieves a
breakthrough by surpassing the upper bound of traditional re-ranking based
methods. More surprisingly, LLM with reasonable prompt and its generative
capability can even correct those tokens that are missing in N-best list. We
make our results publicly accessible for reproducible pipelines with released
pre-trained models, thus providing a new evaluation paradigm for ASR error
correction with LLMs.Comment: Accepted to NeurIPS 2023, 24 pages. Datasets and Benchmarks Track.
Added the first Mandarin and code-switching (zh-cn and en-us) results from
the LLM-based generative ASR error correction to Table 8 on Page 2
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