pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues
Authors: Maneesh Bilalpur, Mert Inan, Dorsa Zeinali, Jeffrey F. Cohn, Malihe Alikhani
Date: 2024-02-13 00:00:00

Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility and efficacy of therapeutic support. Embodied agents with advanced interactive capabilities emerge as a promising and cost-effective supplement to traditional caregiving methods. Crucial to these agents' effectiveness is their ability to simulate non-verbal behaviors, like backchannels, that are pivotal in establishing rapport and understanding in therapeutic contexts but remain under-explored. To improve the rapport-building capabilities of embodied agents we annotated backchannel smiles in videos of intimate face-to-face conversations over topics such as mental health, illness, and relationships. We hypothesized that both speaker and listener behaviors affect the duration and intensity of backchannel smiles. Using cues from speech prosody and language along with the demographics of the speaker and listener, we found them to contain significant predictors of the intensity of backchannel smiles. Based on our findings, we introduce backchannel smile production in embodied agents as a generation problem. Our attention-based generative model suggests that listener information offers performance improvements over the baseline speaker-centric generation approach. Conditioned generation using the significant predictors of smile intensity provides statistically significant improvements in empirical measures of generation quality. Our user study by transferring generated smiles to an embodied agent suggests that agent with backchannel smiles is perceived to be more human-like and is an attractive alternative for non-personal conversations over agent without backchannel smiles.
Sequence graphs realizations and ambiguity in language models
Authors: Sammy Khalife, Yann Ponty, Laurent Bulteau
Date: 2024-02-13 00:00:00

Several popular language models represent local contexts in an input text as bags of words. Such representations are naturally encoded by a sequence graph whose vertices are the distinct words occurring in x, with edges representing the (ordered) co-occurrence of two words within a sliding window of size w. However, this compressed representation is not generally bijective, and may introduce some degree of ambiguity. Some sequence graphs may admit several realizations as a sequence, while others may not admit any realization. In this paper, we study the realizability and ambiguity of sequence graphs from a combinatorial and computational point of view. We consider the existence and enumeration of realizations of a sequence graph under multiple settings: window size w, presence/absence of graph orientation, and presence/absence of weights (multiplicities). When w = 2, we provide polynomial time algorithms for realizability and enumeration in all cases except the undirected/weighted setting, where we show the #P-hardness of enumeration. For a window of size at least 3, we prove hardness of all variants, even when w is considered as a constant, with the notable exception of the undirected/unweighted case for which we propose an XP algorithms for both (realizability and enumeration) problems, tight due to a corresponding W[1]-hardness result. We conclude with an integer program formulation to solve the realizability problem, and with dynamic programming to solve the enumeration problem. This work leaves open the membership to NP for both problems, a non-trivial question due to the existence of minimum realizations having exponential size on the instance encoding.
Syllable based DNN-HMM Cantonese Speech to Text System
Authors: Timothy Wong, Claire Li, Sam Lam, Billy Chiu, Qin Lu, Minglei Li, Dan Xiong, Roy Shing Yu, Vincent T. Y. Ng
Date: 2024-02-13 00:00:00
Proceeding: LREC 2016 5
Tasks: speech-recognition, Speech Recognition, Speech-to-Text

This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid dyslexic students who have cognitive deficiency in writing skills but have no problem expressing their ideas through speech. For Cantonese speech recognition, the basic unit of acoustic models can either be the conventional Initial-Final (IF) syllables, or the Onset-Nucleus-Coda (ONC) syllables where finals are further split into nucleus and coda to reflect the intra-syllable variations in Cantonese. By using the Kaldi toolkit, our system is trained using the stochastic gradient descent optimization model with the aid of GPUs for the hybrid Deep Neural Network and Hidden Markov Model (DNN-HMM) with and without I-vector based speaker adaptive training technique. The input features of the same Gaussian Mixture Model with speaker adaptive training (GMM-SAT) to DNN are used in all cases. Experiments show that the ONC-based syllable acoustic modeling with I-vector based DNN-HMM achieves the best performance with the word error rate (WER) of 9.66% and the real time factor (RTF) of 1.38812.
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
Authors: Jianing Wang, Junda Wu, Yupeng Hou, Yao Liu, Ming Gao, Julian McAuley
Date: 2024-02-13 00:00:00
Tasks: Hallucination

Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment. Specifically, we first propose a structured format verbalizer to unify all graph data into a universal code-like format, which can simply represent the graph without any external graph-specific encoders. Furthermore, a graph instruction tuning stage is introduced to guide LLMs in solving graph reasoning and generation tasks. Finally, we identify potential hallucination problems in graph tasks and sample negative instances for preference alignment, the target of which is to enhance the output's reliability of the model. Extensive experiments across multiple graph-centric tasks exhibit that InstructGraph can achieve the best performance and outperform GPT-4 and LLaMA2 by more than 13\% and 38\%, respectively.
A Dataset for the Detection of Dehumanizing Language
Authors: Paul Engelmann, Peter Brunsgaard Trolle, Christian Hardmeier
Date: 2024-02-13 00:00:00

Dehumanization is a mental process that enables the exclusion and ill treatment of a group of people. In this paper, we present two data sets of dehumanizing text, a large, automatically collected corpus and a smaller, manually annotated data set. Both data sets include a combination of political discourse and dialogue from movie subtitles. Our methods give us a broad and varied amount of dehumanization data to work with, enabling further exploratory analysis and automatic classification of dehumanization patterns. Both data sets will be publicly released.
Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots
Authors: Alican Mertan, Nick Cheney
Date: 2024-02-14 00:00:00

Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process -- fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimization of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.
Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
Authors: Jeroen Rombouts, Marie Ternes, Ines Wilms
Date: 2024-02-14 00:00:00
Tasks: Decision Making

Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all levels of the hierarchy to ensure aligned decision making across different planning units such as pricing, product, controlling and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces cross-temporal reconciled forecasts in a direct and automated way through the use of popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle sharing system in New York City.
Fusing Individualized Treatment Rules Using Secondary Outcomes
Authors: Daiqi Gao, Yuanjia Wang, Donglin Zeng
Date: 2024-02-13 00:00:00

An individualized treatment rule (ITR) is a decision rule that recommends treatments for patients based on their individual feature variables. In many practices, the ideal ITR for the primary outcome is also expected to cause minimal harm to other secondary outcomes. Therefore, our objective is to learn an ITR that not only maximizes the value function for the primary outcome, but also approximates the optimal rule for the secondary outcomes as closely as possible. To achieve this goal, we introduce a fusion penalty to encourage the ITRs based on different outcomes to yield similar recommendations. Two algorithms are proposed to estimate the ITR using surrogate loss functions. We prove that the agreement rate between the estimated ITR of the primary outcome and the optimal ITRs of the secondary outcomes converges to the true agreement rate faster than if the secondary outcomes are not taken into consideration. Furthermore, we derive the non-asymptotic properties of the value function and misclassification rate for the proposed method. Finally, simulation studies and a real data example are used to demonstrate the finite-sample performance of the proposed method.
Large Language Model Simulator for Cold-Start Recommendation
Authors: Feiran Huang, Yuanchen Bei, Zhenghang Yang, Junyi Jiang, Hao Chen, Qijie Shen, Senzhang Wang, Fakhri Karray, Philip S. Yu
Date: 2024-02-14 00:00:00
Tasks: Collaborative Filtering, Language Modeling, Language Modelling, Large Language Model, model, Recommendation Systems

Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their recommendation performance and impacting user experience and revenue. Current models generate synthetic behavioral embeddings from content features but fail to address the core issue: the absence of historical behavior data. To tackle this, we introduce the LLM Simulator framework, which leverages large language models to simulate user interactions for cold items, fundamentally addressing the cold-start problem. However, simply using LLM to traverse all users can introduce significant complexity in billion-scale systems. To manage the computational complexity, we propose a coupled funnel ColdLLM framework for online recommendation. ColdLLM efficiently reduces the number of candidate users from billions to hundreds using a trained coupled filter, allowing the LLM to operate efficiently and effectively on the filtered set. Extensive experiments show that ColdLLM significantly surpasses baselines in cold-start recommendations, including Recall and NDCG metrics. A two-week A/B test also validates that ColdLLM can effectively increase the cold-start period GMV.
Recommendation Algorithm Based on Recommendation Sessions
Authors: Michał Malinowski
Date: 2024-02-14 00:00:00
Tasks: Recommendation Systems

The enormous development of the Internet, both in the geographical scale and in the area of using its possibilities in everyday life, determines the creation and collection of huge amounts of data. Due to the scale, it is not possible to analyse them using traditional methods, therefore it makes a necessary to use modern methods and techniques. Such methods are provided, among others, by the area of recommendations. The aim of this study is to present a new algorithm in the area of recommendation systems, the algorithm based on data from various sets of information, both static (categories of objects, features of objects) and dynamic (user behaviour).
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Authors: Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Philip S. Yu
Date: 2024-02-14 00:00:00
Tasks: Conformal Prediction, Model Selection, Recommendation Systems, Sequential Recommendation

In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user satisfaction metrics. CE loss focuses on maximizing the confidence of the ground truth item, which is challenging to achieve universally across all users and sessions. It also overlooks the practical acceptability of ranking the ground truth item within the top-$K$ positions, a common metric in SRecsys. To address this limitation, we propose \textbf{CPFT}, a novel fine-tuning framework that integrates Conformal Prediction (CP)-based losses with CE loss to optimize accuracy alongside confidence that better aligns with widely used top-$K$ metrics. CPFT embeds CP principles into the training loop using differentiable proxy losses and computationally efficient calibration strategies, enabling the generation of high-confidence prediction sets. These sets focus on items with high relevance while maintaining robust coverage guarantees. Extensive experiments on five real-world datasets and four distinct sequential models demonstrate that CPFT improves precision metrics and confidence calibration. Our results highlight the importance of confidence-aware fine-tuning in delivering accurate, trustworthy recommendations that enhance user satisfaction.
Multi-Label Zero-Shot Product Attribute-Value Extraction
Authors: Jiaying Gong, Hoda Eldardiry
Date: 2024-02-13 00:00:00
Tasks: Attribute, Attribute Value Extraction, Inductive Link Prediction, Link Prediction

E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values, large quantities of labeled training data are needed to train a traditional supervised learning model. Typically, it is difficult, time-consuming, and costly to manually label large quantities of new product profiles. In this paper, we propose a novel method to efficiently and effectively extract unseen attribute values from new products in the absence of labeled data (zero-shot setting). We propose HyperPAVE, a multi-label zero-shot attribute value extraction model that leverages inductive inference in heterogeneous hypergraphs. In particular, our proposed technique constructs heterogeneous hypergraphs to capture complex higher-order relations (i.e. user behavior information) to learn more accurate feature representations for graph nodes. Furthermore, our proposed HyperPAVE model uses an inductive link prediction mechanism to infer future connections between unseen nodes. This enables HyperPAVE to identify new attribute values without the need for labeled training data. We conduct extensive experiments with ablation studies on different categories of the MAVE dataset. The results demonstrate that our proposed HyperPAVE model significantly outperforms existing classification-based, generation-based large language models for attribute value extraction in the zero-shot setting.
Microwave Photonic Imaging Radar with a Millimeter-level Resolution
Authors: Cong Ma, Yue Yang, Ce Liu, Beichen Fan, Xingwei Ye, Yamei Zhang, Xiangchuan Wang, Shilong Pan
Date: 2020-04-09 00:00:00

Microwave photonic radars enable fast or even real-time high-resolution imaging thanks to its broad bandwidth. Nevertheless, the frequency range of the radars usually overlaps with other existed radio-frequency (RF) applications, and only a centimeter-level imaging resolution has been reported, making them insufficient for civilian applications. Here, we propose a microwave photonic imaging radar with a millimeter-level resolution by introducing a frequency-stepped chirp signal based on an optical frequency shifting loop. As compared with the conventional linear-frequency modulated (LFM) signal, the frequency-stepped chirp signal can bring the system excellent capability of anti-interference. In an experiment, a frequency-stepped chirp signal with a total bandwidth of 18.2 GHz (16.9 to 35.1 GHz) is generated. Postprocessing the radar echo, radar imaging with a two-dimensional imaging resolution of ~8.5 mm$\times$~8.3 mm is achieved. An auto-regressive algorithm is used to reconstruct the disturbed signal when a frequency interference exists, and the high-resolution imaging is sustained.
LLMs Among Us: Generative AI Participating in Digital Discourse
Authors: Kristina Radivojevic, Nicholas Clark, Paul Brenner
Date: 2024-02-08 00:00:00
Tasks: Human Detection

The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the "LLMs Among Us" experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants. We built 10 personas with three different LLMs, GPT-4, LLama 2 Chat, and Claude. We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.
Towards the Human Digital Twin: Definition and Design -- A survey
Authors: Martin Wolfgang Lauer-Schmaltz, Philip Cash, John Paulin Hansen, Anja Maier
Date: 2024-02-03 00:00:00
Tasks: Survey

Human Digital Twins (HDTs) are a fast-emerging technology with significant potential in fields ranging from healthcare to sports. HDTs extend the traditional understanding of Digital Twins by representing humans as the underlying physical entity. This has introduced several significant challenges, including ambiguity in the definition of HDTs and a lack of guidance for their design. This survey brings together the recent advances in the field of HDTs to guide future developers by proposing a first cross-domain definition of HDTs based on their characteristics, as well as eleven key design considerations that emerge from the associated challenges.
Prompt4Vis: Prompting Large Language Models with Example Mining and Schema Filtering for Tabular Data Visualization
Authors: Shuaimin Li, Xuanang Chen, Yuanfeng Song, Yunze Song, Chen Zhang
Date: 2024-01-29 00:00:00
Tasks: Data Visualization, In-Context Learning

Data visualization (DV) systems are increasingly recognized for their profound capability to uncover insights from vast datasets, gaining attention across both industry and academia. Crafting data queries is an essential process within certain declarative visualization languages (DVLs, e.g., Vega-Lite, EChart.). The evolution of natural language processing (NLP) technologies has streamlined the use of natural language interfaces to visualize tabular data, offering a more accessible and intuitive user experience. However, current methods for converting natural language questions into data visualization queries, such as Seq2Vis, ncNet, and RGVisNet, despite utilizing complex neural network architectures, still fall short of expectations and have great room for improvement. Large language models (LLMs) such as ChatGPT and GPT-4, have established new benchmarks in a variety of NLP tasks, fundamentally altering the landscape of the field. Inspired by these advancements, we introduce a novel framework, Prompt4Vis, leveraging LLMs and in-context learning to enhance the performance of generating data visualization from natural language. Prompt4Vis comprises two key components: (1) a multi-objective example mining module, designed to find out the truly effective examples that strengthen the LLM's in-context learning capabilities for text-to-vis; (2) a schema filtering module, which is proposed to simplify the schema of the database. Extensive experiments through 5-fold cross-validation on the NVBench dataset demonstrate the superiority of Prompt4Vis, which notably surpasses the state-of-the-art (SOTA) RGVisNet by approximately 35.9% and 71.3% on dev and test sets, respectively. To the best of our knowledge, Prompt4Vis is the first work that introduces in-context learning into the text-to-vis for generating data visualization queries.
Computing Threshold Circuits with Void Reactions in Step Chemical Reaction Networks
Authors: Rachel Anderson, Alberto Avila, Bin Fu, Timothy Gomez, Elise Grizzell, Aiden Massie, Gourab Mukhopadhyay, Adrian Salinas, Robert Schweller, Evan Tomai, Tim Wylie
Date: 2024-02-13 00:00:00

We introduce a new model of \emph{step} Chemical Reaction Networks (step CRNs), motivated by the step-wise addition of materials in standard lab procedures. Step CRNs have ordered reactants that transform into products via reaction rules over a series of steps. We study an important subset of weak reaction rules, \emph{void} rules, in which chemical species may only be deleted but never changed. We demonstrate the capabilities of these simple limited systems to simulate threshold circuits and compute functions using various configurations of rule sizes and step constructions, and prove that without steps, void rules are incapable of these computations, which further motivates the step model. Additionally, we prove the coNP-completeness of verifying if a given step CRN computes a function, holding even for $O(1)$ step systems.