Pearl Pu

AAAI Conference 2022 Conference Paper

HEAL: A Knowledge Graph for Distress Management Conversations

• Anuradha Welivita
• Pearl Pu

The demands of the modern world are increasingly responsible for causing psychological burdens and bringing adverse impacts on our mental health. As a result, neural conversational agents with empathetic responding and distress management capabilities have recently gained popularity. However, existing end-to-end empathetic conversational agents often generate generic and repetitive empathetic statements such as “I am sorry to hear that”, which fail to convey specificity to a given situation. Due to the lack of controllability in such models, they also impose the risk of generating toxic responses. Chatbots leveraging reasoning over knowledge graphs is seen as an efficient and fail-safe solution over end-to-end models. However, such resources are limited in the context of emotional distress. To address this, we introduce HEAL, a knowledge graph developed based on 1M distress narratives and their corresponding consoling responses curated from Reddit. It consists of 22K nodes identifying different types of stressors, speaker expectations, responses, and feedback types associated with distress dialogues and forms 104K connections between different types of nodes. Each node is associated with one of 41 affective states. Statistical and visual analysis conducted on HEAL reveals emotional dynamics between speakers and listeners in distress-oriented conversations and identifies useful response patterns leading to emotional relief. Automatic and human evaluation experiments show that HEAL’s responses are more diverse, empathetic, and reliable compared to the baselines.

TIST Journal 2018 Journal Article

A Bayesian Approach to Intervention-Based Clustering

• Igor Kulev
• Pearl Pu
• Boi Faltings

An important task for intelligent healthcare systems is to predict the effect of a new intervention on individuals. This is especially true for medical treatments. For example, consider patients who do not respond well to a new drug or have adversary reactions. Predicting the likelihood of positive or negative response before trying the drug on the patient can potentially save his or her life. We are therefore interested in identifying distinctive subpopulations that respond differently to a given intervention. For this purpose, we have developed a novel technique, Intervention-based Clustering, based on a Bayesian mixture model. Compared to the baseline techniques, the novelty of our approach lies in its ability to model complex decision boundaries by using soft clustering, thus predicting the effect for individuals more accurately. It can also incorporate prior knowledge, making the method useful even for smaller datasets. We demonstrate how our method works by applying it to both simulated and real data. Results of our evaluation show that our model has strong predictive power and is capable of producing high-quality clusters compared to the baseline methods.

TIST Journal 2016 Journal Article

Dystemo

• Valentina Sintsova
• Pearl Pu

Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports events, or discerning opinions in political debates. Most successful approaches rely heavily on costly manual annotation. To alleviate this burden, we propose a distant supervision method—Dystemo—for automatically producing emotion classifiers from tweets labeled using existing or easy-to-produce emotion lexicons. The goal is to obtain emotion classifiers that work more accurately for specific applications than available emotion lexicons. The success of this method depends mainly on a novel classifier—Balanced Weighted Voting (BWV)—designed to overcome the imbalance in emotion distribution in the initial dataset, and on novel heuristics for detecting neutral tweets. We demonstrate how Dystemo works using Twitter data about sports events, a fine-grained 20-category emotion model, and three different initial emotion lexicons. Through a series of carefully designed experiments, we confirm that Dystemo is effective both in extending initial emotion lexicons of small coverage to find correctly more emotional tweets and in correcting emotion lexicons of low accuracy to perform more accurately.

AAAI Conference 2014 Conference Paper

Decomposing Activities of Daily Living to Discover Routine Clusters

• Onur Yürüten
• Jiyong Zhang
• Pearl Pu

The modern sensor technology helps us collect time series data for activities of daily living (ADLs), which in turn can be used to infer broad patterns, such as common daily routines. Most of the existing approaches either rely on a model trained by a preselected and manually labeled set of activities, or perform micro-pattern analysis with manually selected length and number of micro-patterns. Since real life ADL datasets are massive, such approaches would be too costly to apply. Thus, there is a need to formulate unsupervised methods that can be applied to different time scales. We propose a novel approach to discover clusters of daily activity routines. We use a matrix decomposition method to isolate routines and deviations to obtain two different sets of clusters. We obtain the final memberships via the cross product of these sets. We validate our approach using two real-life ADL datasets and a well-known artificial dataset. Based on average silhouette width scores, our approach can capture strong structures in the underlying data. Furthermore, results show that our approach improves on the accuracy of the baseline algorithms by 12% with a statistical significance (p <0. 05) using the Wilcoxon signed-rank comparison test.

AAAI Conference 2014 Conference Paper

Prediction of Helpful Reviews Using Emotions Extraction

• Lionel Martin
• Pearl Pu

Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length and readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners’ attention, we believe that emotional words can serve as important parameters for predicting helpfulness of review text. Using GALC, a general lexicon of emotional words associated with a model representing 20 different categories, we extracted the emotionality from the review text and applied supervised classification method to derive the emotion-based helpful review prediction. As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites. This framework shows that emotion-based methods are outperforming the structure-based approach, by up to 9%.

ECAI Conference 1996 Conference Paper

Human and Machine Collaboration in Creative Design

• Pearl Pu
• Denis Lalanne

ICRA Conference 1995 Conference Paper

Assembly Planning Using Case Adaptation Methods

• Pearl Pu
• Lisa Purvis

In our previous paper, we have shown that case-based reasoning (CBR) techniques can be used as a viable formulation for solving assembly sequence generation problems. The issues covered in that paper were case base organization, case selection and matching, and case indexing. The part on case adaptation was not addressed in a formal way to allow satisfactory generalization of the method to a large class of assembly planning problems. We present in this paper a methodology which formalizes the adaptation process of CBR using constraint satisfaction techniques. Combining CBR with constraint satisfaction provides a generalized formalism for assembly planning problem solving.

ICRA Conference 1994 Conference Paper

Integrating AGV Schedules in a Scheduling System for a Flexible Manufacturing Environment

• Pearl Pu
• James Hughes 0001

In a job shop where machining time takes a comparable amount as material transportation time, it is no longer realistic to ignore the scheduling of material transportation systems such as automated guided vehicles (AGVs). The authors' algorithm discussed in this paper integrates AGV schedules into a heuristic-based scheduling algorithm to achieve the maximum amount of flexibility and optimization for a flexible manufacturing environment. Furthermore in order to allow their system to achieve different optimization goals such as shortest schedules or schedules that require the minimum computation time, the authors' system architecture consists of individual modules of heuristics so that one or any combination of these modules can be used. The authors then discuss how they control the use of these heuristic modules to achieve the best results with some experimental data. >

ICRA Conference 1992 Conference Paper

An assembly sequence generation algorithm using case-based search techniques

• Pearl Pu

The author explores the possibility of using case-based reasoning (CBR) techniques to handle search in assembly sequence generation (ASG). CBR solves a new problem by retrieving from its case library a solution which has solved a similar problem in the past and then adapting the solution to the new problem. To illustrate how the system works, two experiments are described to show how a case-based search derived from a time-consuming spatial problem in ASG, the receptacle device, can be efficiently applied to two similar problems: a ball-point pen assembly and a complicated industrial assembly. >

ECAI Conference 1992 Conference Paper

Crossroad Diagnosis

• Pearl Pu
• Y. Huang

IROS Conference 1991 Conference Paper

Applying means-ends analysis to spatial planning

• Boi Faltings
• Pearl Pu

Existing methods for robot planning fall far behind human capabilities: they require approximations of shapes, and they cannot generate plans which involve moving obstacles to clear a path for the moving object. The authors explore the hypothesis that means-ends analysis based on a world model involving mental imagery allows more human-like solutions. The method is based on a way or representing planning constraints which makes it possible to generate incrementally the symbolic representations for means-ends planning using only imagery operations.

ICRA Conference 1985 Conference Paper

A new development in camera calibration calibrating a pair of mobile cameras

• Alberto Izaguirre
• Pearl Pu
• John Summers

A new method of calibration is proposed for active visual sensing for use in 3D analysis. The method, based on an adaptation of the two camera plane m model, permits the calibration of a mobile camera as a function of the position and orientation of the camera.