186,307 research outputs found
Improving Remote Customer Interaction Experiences Using Machine Learning
A common problem in contact centers is high employee turnover. Artificial intelligence (AI) techniques that have been introduced to smoothen interaction and improve the customer’s experience can have opposite effects, e.g., by requiring the customer to navigate complex menu options. This disclosure describes AI-based techniques applied to agent training and customer calls. The techniques can reduce turnover at contact centers and improve the experience of end users who interact with customer service agents. Per the techniques, suitable AI techniques are implemented to train human customer agents, and human feedback is in turn used to train AI techniques. Human-AI augmentation can be used to mirror the communication styles of customers to improve the interaction experience. The techniques can also be used to improve safety, e.g., by automatically detecting scam calls and alerting users. The techniques enable the creation of scalable, standalone, artificial or human-AI augmented customer service agents
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
In recent years, the utilization of Artificial Intelligence (AI) in the
contact center industry is on the rise. One area where AI can have a
significant impact is in the coaching of contact center agents. By analyzing
call transcripts using Natural Language Processing (NLP) techniques, it would
be possible to quickly determine which calls are most relevant for coaching
purposes. In this paper, we present AI Coach Assist, which leverages the
pre-trained transformer-based language models to determine whether a given call
is coachable or not based on the quality assurance (QA) questions asked by the
contact center managers or supervisors. The system was trained and evaluated on
a large dataset collected from real-world contact centers and provides an
effective way to recommend calls to the contact center managers that are more
likely to contain coachable moments. Our experimental findings demonstrate the
potential of AI Coach Assist to improve the coaching process, resulting in
enhancing the performance of contact center agents.Comment: ACL 2023 Industry Trac
Equation of state of hard oblate ellipsoids by replica exchange Monte Carlo
We implemented the replica exchange Monte Carlo technique to produce the
equation of state of hard 1:5 aspect-ratio oblate ellipsoids for a wide density
range. For this purpose, we considered the analytical approximation of the
overlap distance given by Bern and Pechukas and the exact numerical solution
given by Perram and Wertheim. For both cases we capture the expected
isotropic-nematic transition at low densities and a nematic-crystal transition
at larger densities. For the exact case, these transitions occur at the volume
fraction 0.341, and in the interval , respectively.Comment: 4 pages, 2 figure
Hepatitis C Diagnoses in an American Indian Primary Care Population
BACKGROUND: Despite large disparities in the burden of chronic liver disease, data on hepatitis C virus (HCV) infection among American Indians (AIs) are lacking. We reviewed hepatitis C diagnoses in 35,712 AI/AN primary care patients.
MAIN FINDINGS: At least one HCV-associated ICD-9 code was recorded in 251 (1%) patients between October 1, 2001 and September 30, 2003. An HCV enzyme-linked immunoassay (HCVEIA) was sent in 209 (83.0%); 206/209 (99%) were positive. Confirmatory testing was performed in 144/206 (70%) HCV-EIA positive patients; HCV infection was confirmed in 144 (100%). In the 90/144 (63%) charts with risk factor documentation, injection drug use was the most common risk factor (61/90, 68%). Deficiencies were present in hepatitis B and HIV testing, and hepatitis A and B vaccination.
PRINCIPAL CONCLUSIONS: Improvements in laboratory workup of HCV and co-infections, risk factor ascertainment and documentation, and adult vaccination are needed to address HCV effectively in this population
HR Shared Services (HRSS): Model and Trends
[Excerpt] The findings of this research project are based on interviews with 44 Human Resources (HR) leaders across 39 national and international companies within 15 industries ranging from manufacturing to consulting services. The interviews ranged from 45 minutes to one hour, and sought to understand models, best practices, and trends. The interview included questions about employee experience, technology, and the integration between HR Shared Services (HRSS) and the overall HR Organization. To provide background information and data, the HR leaders answered a short survey, giving details about the structure of their HRSS, locations, areas of HR that had work performed in the shared services organization, systems, and technology capabilities
Range-Free Localization with the Radical Line
Due to hardware and computational constraints, wireless sensor networks
(WSNs) normally do not take measurements of time-of-arrival or
time-difference-of-arrival for rangebased localization. Instead, WSNs in some
applications use rangefree localization for simple but less accurate
determination of sensor positions. A well-known algorithm for this purpose is
the centroid algorithm. This paper presents a range-free localization technique
based on the radical line of intersecting circles. This technique provides
greater accuracy than the centroid algorithm, at the expense of a slight
increase in computational load. Simulation results show that for the scenarios
studied, the radical line method can give an approximately 2 to 30% increase in
accuracy over the centroid algorithm, depending on whether or not the anchors
have identical ranges, and on the value of DOI.Comment: Proc. IEEE ICC'10, Cape Town, South Africa, May, 201
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