pwc-archive/papers-with-abstracts huggingface dataset:
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
Authors: Xiao Lin, Xiaokai Chen, Linfeng Song, Jingwei Liu, Biao Li, Peng Jiang
Date: 2023-06-06 00:00:00
Tasks: Recommendation Systems, regression

An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected. Therefore we propose TPM for watch time prediction. Specifically, the ordinal ranks of watch time are introduced into TPM and the problem is decomposed into a series of conditional dependent classification tasks which are organized into a tree structure. The expectation of watch time can be generated by traversing the tree and the variance of watch time predictions is explicitly introduced into the objective function as a measurement for uncertainty. Moreover, we illustrate that backdoor adjustment can be seamlessly incorporated into TPM, which alleviates bias amplifications. Extensive offline evaluations have been conducted in public datasets and TPM have been deployed in a real-world video app Kuaishou with over 300 million DAUs. The results indicate that TPM outperforms state-of-the-art approaches and indeed improves video consumption significantly.
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction
Authors: Yuhang Wang, Dongyuan Lu, Chao Kong, Jitao Sang
Date: 2023-06-06 00:00:00
Tasks: Object

Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pretrained Language Models. In this paper, we observe that the optimized prompts, including discrete prompts and continuous prompts, exhibit undesirable object bias. To handle this problem, we propose a novel prompt tuning method called MeCoD. consisting of three modules: Prompt Encoder, Object Equalization and Biased Object Obstruction. Experimental results show that MeCoD can significantly reduce the object bias and at the same time improve accuracy of factual knowledge extraction.
Construction d'un système de recommandation basé sur des contraintes via des graphes de connaissances
Authors: Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou
Date: 2023-06-05 00:00:00
Tasks: Knowledge Graphs, Recommendation Systems

Knowledge graphs in RDF model entities and their relations using ontologies, and have gained popularity for information modeling. In recommender systems, knowledge graphs help represent more links and relationships between users and items. Constraint-based recommender systems leverage deep recommendation knowledge to identify relevant suggestions. When combined with knowledge graphs, they offer benefits in constraint sets. This paper explores a constraint-based recommender system using RDF knowledge graphs for the vehicle purchase/sale domain. Our experiments demonstrate that the proposed approach efficiently identifies recommendations based on user preferences.
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients
Authors: Zhendong Wang, Isak Samsten, Panagiotis Papapetrou
Date: 2021-06-15 00:00:00
Proceeding: International Conference on Artificial Intelligence in Medicine 2021 6
Tasks: counterfactual, Counterfactual Explanation, Interpretable Machine Learning, Prediction, Style Transfer, Survival Prediction, Text Style Transfer

In recent years, machine learning methods have been rapidly implemented in the medical domain. However, current state-of-the-art methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to develop interpretable machine learning methods. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual explanation solution for predicting the survival of cardiovascular ICU patients, by representing their electronic health record as a sequence of medical events, and generating counterfactuals by adopting and employing a text style-transfer technique. Experimental results on the MIMIC-III dataset strongly suggest that text style-transfer methods can …
Learning Time Series Counterfactuals via Latent Space Representations
Authors: Zhendong Wang, Isak Samsten, Rami Mochaourab, Panagiotis Papapetrou
Date: 2021-10-11 00:00:00
Proceeding: International Conference on Discovery Science 2021 10
Tasks: counterfactual, Counterfactual Explanation, Time Series, Time Series Classification, valid

Counterfactual explanations can provide sample-based explanations of features required to modify from the original sample to change the classification result from an undesired state to a desired state; hence it provides interpretability of the model. Previous work of LatentCF presents an algorithm for image data that employs auto-encoder models to directly transform original samples into counterfactuals in a latent space representation. In our paper, we adapt the approach to time series classification and propose an improved algorithm named LatentCF++ which introduces additional constraints in the counterfactual generation process. We conduct an extensive experiment on a total of 40 datasets from the UCR archive, comparing to current state-of-the-art methods. Based on our evaluation metrics, we show that the LatentCF++ framework can with high probability generate valid counterfactuals and achieve …
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients
Authors: Zhendong Wang, Isak Samsten, Vasiliki Kougia, Panagiotis Papapetrou
Date: 2023-01-01 00:00:00
Proceeding: Artificial Intelligence in Medicine 2023 1
Tasks: counterfactual, Counterfactual Explanation, Interpretable Machine Learning, Style Transfer, Text Style Transfer

In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to developing interpretable machine learning models. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique. We propose three model augmentations for MedSeqCF to integrate additional medical knowledge for generating more trustworthy counterfactuals. Experimental results on the MIMIC-III dataset strongly suggest that augmented style-transfer methods can be effectively adapted for the problem of counterfactual explanations in healthcare applications and can further improve the model performance in terms of validity, BLEU-4, local outlier factor, and edit distance. In addition, our qualitative analysis of the results by consultation with medical experts suggests that our style-transfer solutions can generate clinically relevant and actionable counterfactual explanations.
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
Authors: Jinfu Liu, Xinshun Wang, Can Wang, Yuan Gao, Mengyuan Liu
Date: 2023-05-01 00:00:00
Proceeding: IEEE Transactions on Multimedia 2023 5
Tasks: Gesture Recognition, Hand Gesture Recognition, Skeleton Based Action Recognition

Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN), which commonly uses an adjacency matrix to model the spatial topology of skeletons. However, previous methods use the same adjacency matrix for skeletons from different frames, which limits the flexibility of GCN to model temporal information. To solve this problem, we propose a Temporal Decoupling Graph Convolutional Network (TD-GCN), which applies different adjacency matrices for skeletons from different frames. The main steps of each convolution layer in our proposed TD-GCN are as follows. To extract deep spatiotemporal information from skeleton joints, we first extract high-level spatiotemporal features from skeleton data. Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints. Finally, to fuse topology information from neighbor skeleton joints, spatiotemporal features of skeleton joints are fused based on channel-dependent and temporal-dependent adjacency matrices. To the best of our knowledge, we are the first to use temporal-dependent adjacency matrices for temporal-sensitive topology learning from skeleton joints. The proposed TD-GCN effectively improves the modeling ability of GCN and achieves state-of-the-art results on gesture datasets including SHREC'17 Track and DHG-14/28. Our code is available at: https://github.com/liujf69/TD-GCN-Gesture .
CorrFL: Correlation-based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment
Authors: Shaer, Ibrahim; Shami, Abdallah
Date: 2023-05-23 00:00:00
Proceeding: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023 5
Tasks: Federated Learning, Representation Learning

The Federated Learning (FL) paradigm faces several challenges that limit its application in real-world environments. These challenges include the local models’ architecture heterogeneity and the unavailability of distributed Internet of Things (IoT) nodes due to connectivity problems. These factors posit the question of “how can the available models fill the training gap of the unavailable models?". This question is referred to as the “Oblique Federated Learning" problem. This problem is encountered in the studied environment that includes distributed IoT nodes responsible for predicting CO2 concentrations. This paper proposes the Correlation-based FL (CorrFL) approach influenced by the representational learning field to address this problem. CorrFL projects the various model weights to a common latent space to address the model heterogeneity. Its loss function minimizes the reconstruction loss when models are absent and maximizes the correlation between the generated models. The latter factor is critical because of the intersection of the feature spaces of the IoT devices. CorrFL is evaluated on a realistic use case, involving the unavailability of one IoT device and heightened activity levels that reflect occupancy. The generated CorrFL models for the unavailable IoT device from the available ones trained on the new environment are compared against models trained on different use cases, referred to as the benchmark model. The evaluation criteria combine the mean absolute error (MAE) of predictions and the impact of the amount of exchanged data on the prediction performance improvement. Through a comprehensive experimental procedure, the CorrFL model outperformed the benchmark model in every criterion.
Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots
Authors: Sukhpal Singh Gill, Minxian Xu, Panos Patros, Huaming Wu, Rupinder Kaur, Kamalpreet Kaur, Stephanie Fuller, Manmeet Singh, Priyansh Arora, Ajith Kumar Parlikad, Vlado Stankovski, Ajith Abraham, Soumya K. Ghosh, Hanan Lutfiyya, Salil S. Kanhere, Rami Bahsoon, Omer Rana, Schahram Dustdar, Rizos Sakellariou, Steve Uhlig, Rajkumar Buyya
Date: 2023-05-25 00:00:00
Tasks: Chatbot

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.
Rethinking Model Evaluation as Narrowing the Socio-Technical Gap
Authors: Q. Vera Liao, Ziang Xiao
Date: 2023-06-01 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), nlg evaluation, valid

The recent development of generative large language models (LLMs) poses new challenges for model evaluation that the research community and industry have been grappling with. While the versatile capabilities of these models ignite much excitement, they also inevitably make a leap toward homogenization: powering a wide range of applications with a single, often referred to as ``general-purpose'', model. In this position paper, we argue that model evaluation practices must take on a critical task to cope with the challenges and responsibilities brought by this homogenization: providing valid assessments for whether and how much human needs in diverse downstream use cases can be satisfied by the given model (\textit{socio-technical gap}). By drawing on lessons about improving research realism from the social sciences, human-computer interaction (HCI), and the interdisciplinary field of explainable AI (XAI), we urge the community to develop evaluation methods based on real-world contexts and human requirements, and embrace diverse evaluation methods with an acknowledgment of trade-offs between realisms and pragmatic costs to conduct the evaluation. By mapping HCI and current NLG evaluation methods, we identify opportunities for evaluation methods for LLMs to narrow the socio-technical gap and pose open questions.
Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses
Authors: Logan Stapleton, Jordan Taylor, Sarah Fox, Tongshuang Wu, Haiyi Zhu
Date: 2023-05-30 00:00:00
Tasks: Red Teaming

Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be beneficial. So, when GMs filter out content, they preclude beneficial use cases along with harmful ones. Which use cases are precluded reflects the values embedded in GM content filtering. Recent work on red teaming proposes methods to bypass GM content filters to generate harmful content. We coin the term green teaming to describe methods of bypassing GM content filters to design for beneficial use cases. We showcase green teaming by: 1) Using ChatGPT as a virtual patient to simulate a person experiencing suicidal ideation, for suicide support training; 2) Using Codex to intentionally generate buggy solutions to train students on debugging; and 3) Examining an Instagram page using Midjourney to generate images of anti-LGBTQ+ politicians in drag. Finally, we discuss how our use cases demonstrate green teaming as both a practical design method and a mode of critique, which problematizes and subverts current understandings of harms and values in generative AI.
Unified Information Dynamic Analysis of Quantum Decision-Making and Search Algorithms: Computational Intelligence Measure
Authors: Sergey V. Ulyanov, Fabio Ghisi, Ichiro Kurawaki, Viktor S. Ulyanov
Date: 2023-05-30 00:00:00
Tasks: Decision Making

There are important algorithms built upon a mixture of basic techniques described; for example, the Fast Fourier Transform (FFT) employs both Divide-and-Conquer and Transform-and-Conquer techniques. In this article, the evolution of a quantum algorithm (QA) is examined from an information theory viewpoint. The complex vector entering the quantum algorithmic gate - QAG is considered as an information source both from the classical and the quantum level. The analysis of the classical and quantum information flow in Deutsch-Jozsa, Shor and Grover algorithms is used. It is shown that QAG, based on superposition of states, quantum entanglement and interference, when acting on the input vector, stores information into the system state, minimizing the gap between classical Shannon entropy and quantum von Neumann entropy. Minimizing of the gap between Shannon and von Neumann entropies is considered as a termination criterion of QA computational intelligence measure.
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
Authors: Yochai Yemini, Aviv Shamsian, Lior Bracha, Sharon Gannot, Ethan Fetaya
Date: 2023-06-05 00:00:00
Tasks: Lip Reading

Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce high-quality speech with high levels of intelligibility for challenging and realistic datasets such as LRS3. In this work, we present LipVoicer, a novel method that generates high-quality speech, even for in-the-wild and rich datasets, by incorporating the text modality. Given a silent video, we first predict the spoken text using a pre-trained lip-reading network. We then condition a diffusion model on the video and use the extracted text through a classifier-guidance mechanism where a pre-trained ASR serves as the classifier. LipVoicer outperforms multiple lip-to-speech baselines on LRS2 and LRS3, which are in-the-wild datasets with hundreds of unique speakers in their test set and an unrestricted vocabulary. Moreover, our experiments show that the inclusion of the text modality plays a major role in the intelligibility of the produced speech, readily perceptible while listening, and is empirically reflected in the substantial reduction of the WER metric. We demonstrate the effectiveness of LipVoicer through human evaluation, which shows that it produces more natural and synchronized speech signals compared to competing methods. Finally, we created a demo showcasing LipVoicer's superiority in producing natural, synchronized, and intelligible speech, providing additional evidence of its effectiveness. Project page and code: https://github.com/yochaiye/LipVoicer
The Eater and the Mover Game
Authors: Violetta Rostobaya, Yue Guan, James Berneburg, Michael Dorothy, Daigo Shishika
Date: 2023-06-06 00:00:00

This paper studies the idea of ``deception by motion'' through a two-player dynamic game played between a Mover who must retrieve resources at a goal location, and an Eater who can consume resources at two candidate goals. The Mover seeks to minimize the resource consumption at the true goal, and the Eater tries to maximize it. While the Mover has the knowledge about the true goal, the Eater cannot differentiate between the two candidates. Unlike existing works on deceptive motion control that measures the deceptiveness through the quality of inference made by a distant observer (an estimator), we incorporate their actions to directly measure the efficacy of deception through the outcome of the game. An equilibrium concept is then proposed without the notion of an estimator. We further identify a pair of equilibrium strategies and demonstrate that if the Eater optimizes for the worst-case scenario, hiding the intention (deception by ambiguity) is still effective, whereas trying to fake the true goal (deception by exaggeration) is not.
Probabilistic Planning for Maritime Search and Rescue
Authors: Luís Marques, Jose Javier Escribano Macias, Panagiotis Angeloudis
Date: 2023-06-06 00:00:00

Maritime accidents cause thousands of disappearances every year, with migrant crossings being particularly dangerous and under-reported. Current coastal and NGO search and rescue services are unable to provide a timely response, so new technologies such as autonomous UAVs are needed. We present a thorough formalization of the maritime search and rescue problem considering its time-critical and probabilistic nature. Further, we introduce a method for determining the optimal search altitude for any aerial thermal-based detection system, so as to maximize overall mission success.
Parameter Estimation in Electrical Distribution Systems with limited Measurements using Regression Methods
Authors: Steven de Jongh, Felicitas Mueller, Claudio Cañizares, Thomas Leibfried, Kankar Bhattacharya
Date: 2023-06-06 00:00:00
Tasks: parameter estimation, regression

This paper presents novel methods for parameter identification in electrical grids with small numbers of spatially distributed measuring devices, which is an issue for distribution system operators managing aged and not properly mapped underground Low Voltage (LV) grids, especially in Germany. For this purpose, the total impedance of individual branches of the overall system is estimated by measuring currents and voltages at a subset of all system nodes over time. It is shown that, under common assumptions for electrical distsribution systems, an estimate of the total impedance can be made using readily computable proxies. Different regression methods are then used and compared to estimate the total impedance of the respective branches, with varying weights of the input data. The results on realistic LV feeders with different branch lengths and number of unmeasured segments are discussed and multiple influencing factors are investigated through simulations. It is shown that estimates of the total impedances can be obtained with acceptable quality under realistic assumptions.