pwc-archive/papers-with-abstracts huggingface dataset:
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Authors: Qi Liu, Xuyang Hou, Haoran Jin, Xiaolong Chen, Jin Chen, Defu Lian, Zhe Wang, Jia Cheng, Jun Lei
Date: 2023-11-15 00:00:00
Tasks: Click-Through Rate Prediction

Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency: they first select historical behaviors related to the candidate item and then deduce the user's interest from this narrowed-down behavior sub-sequence. This two-stage paradigm, though effective, leads to information loss. Solely using users' lifelong click behaviors doesn't provide a complete picture of their interests, leading to suboptimal performance. In our research, we introduce the Deep Group Interest Network (DGIN), an end-to-end method to model the user's entire behavior history. This includes all post-registration actions, such as clicks, cart additions, purchases, and more, providing a nuanced user understanding. We start by grouping the full range of behaviors using a relevant key (like item_id) to enhance efficiency. This process reduces the behavior length significantly, from O(10^4) to O(10^2). To mitigate the potential loss of information due to grouping, we incorporate two categories of group attributes. Within each group, we calculate statistical information on various heterogeneous behaviors (like behavior counts) and employ self-attention mechanisms to highlight unique behavior characteristics (like behavior type). Based on this reorganized behavior data, the user's interests are derived using the Transformer technique. Additionally, we identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence. The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy. Our comprehensive evaluation, both on industrial and public datasets, validates DGIN's efficacy and efficiency.
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
Authors: Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
Date: 2023-10-31 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Offline RL, reinforcement-learning, Reinforcement Learning (RL), Representation Learning, Sequential Decision Making

In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline datasets. However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets. In this paper, we introduce the saFety-aware strUctured Scenario representatION (FUSION), a pioneering representation learning method in offline RL to facilitate the learning of a generalizable end-to-end driving policy by leveraging structured scenario information. FUSION capitalizes on the causal relationships between the decomposed reward, cost, state, and action space, constructing a framework for structured sequential reasoning in dynamic traffic environments. We conduct extensive evaluations in two typical real-world settings of the distribution shift in autonomous vehicles, demonstrating the good balance between safety cost and utility reward compared to the current state-of-the-art safe RL and IL baselines. Empirical evidence in various driving scenarios attests that FUSION significantly enhances the safety and generalizability of autonomous driving agents, even in the face of challenging and unseen environments. Furthermore, our ablation studies reveal noticeable improvements in the integration of causal representation into the offline safe RL algorithm. Our code implementation is available at: https://sites.google.com/view/safe-fusion/.
Model-Based Reinforcement Learning for Eco-Driving Control of Electric Vehicles
Authors: HEEYUN LEE, NAMWOOK KIM, SUK WON CHA
Date: 2020-11-09 00:00:00
Proceeding: IEEE Access 2020 11
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning

The optimal control problem of determining the speed profile of the vehicle for minimizing energy consumption is a challenging problem that necessitates the consideration of various aspects, such as the vehicle energy consumption, slope of the road, and driving environment. The proposed approach utilizes a model-based reinforcement learning algorithm that separates the vehicle's energy consumption approximation model and driving environment model. This allows for the utilization of domain knowledge of vehicle dynamics and the powertrain system for the reinforcement learning process, while maintaining model-free characteristics by updating the approximation model using experience replay. The paper also evaluates the proposed algorithm under various driving scenarios and compares it with other eco-driving control methods.
Deduplication Over Heterogeneous Attribute Types (D-HAT)
Authors: Loujain Liekah, George Papadakis
Date: 2022-11-24 00:00:00
Proceeding: Advanced Data Mining and Applications (ADMA) 2022 11
Tasks: Attribute, Clustering, Entity Resolution, Feature Engineering, Missing Values

Deduplication is the task of recognizing multiple representations of the same real-world object. The majority of existing solutions focuses on textual data, this means that data sets containing boolean and numerical attribute types are rarely considered in the literature, while the problem of missing values is inadequately covered. Supervised solutions cannot be applied without an adequate number of labelled examples, but training data for deduplication can only be obtained through time-costly processes. In high dimensional data sets, feature engineering is also required to avoid the risk of overfitting. To address these challenges, we go beyond existing works through D-HAT, a clustering-based pipeline that is inherently capable of handling high dimensional, sparse and heterogeneous attribute types. At its core lies: (i) a novel matching function that effectively summarizes multiple matching signals, and (ii) MutMax, a greedy clustering algorithm that designates as duplicates the pairs with a mutually maximum matching score. We evaluate D-HAT on five established, real-world benchmark data sets, demonstrating that our approach outperforms the state-of-the-art supervised and unsupervised deduplication algorithms to a significant extent.
Adapting Vision Foundation Models for Plant Phenotyping
Authors: Feng Chen, Mario Valerio Giuffrida, Sotirios A. Tsaftaris
Date: 2023-10-01 00:00:00
Proceeding: ICCV 2023 10
Tasks: Decoder, Instance Segmentation, Plant Phenotyping, Semantic Segmentation

Foundation models are large models pre-trained on tremendous amount of data. They can be typically adapted to diverse downstream tasks with minimal effort. However, as foundation models are usually pre-trained on images or texts sourced from the Internet, their performance in specialized domains, such as plant phenotyping, comes into question. In addition, fully fine-tuning foundation models is time-consuming and requires high computational power. This paper investigates the efficient adaptation of foundation models for plant phenotyping settings and tasks. We perform extensive experiments on fine-tuning three foundation models, MAE, DINO, and DINOv2 on three essential plant phenotyping tasks: leaf counting, instance segmentation, and disease classification. In particular, the pretrained backbones are kept frozen, while two distinct finetuning methods are evaluated, namely adapter tuning (using LoRA) and decoder tuning. The experimental results show that a foundation model can be efficiently adapted to multiple plant phenotyping tasks, yielding similar performance as the state-of-the-art (SoTA) models specifically designed or trained for each task. Despite exhibiting great transferability over different tasks, the fine-tuned foundation models perform slightly worse than the SoTA task-specific models in some scenarios, which requires further investigation.
Lung cancer detection from thoracic CT scans using an ensemble of deep learning models
Authors: Nandita Gautam, Abhishek Basu, Ram Sarkar
Date: 2023-11-21 00:00:00
Proceeding: Neural Computing and Applications, Springer 2023 11
Tasks: Computed Tomography (CT), Diagnostic, Ensemble Learning, Lung Nodule Classification, Transfer Learning

Lung cancer remains a prevalent and deadly disease, claiming numerous lives annually. Early detection plays a pivotal role in significantly improving survival rates, by up to 50–70%. Therefore, developing a robust lung cancer detection system holds immense potential to positively impact human survival. Computed tomography (CT) scan images offer invaluable information about lung nodules, and the emergence of machine learning and deep learning techniques has empowered radiologists in their diagnostic tasks. In this study, we propose a new ensemble of deep learning models to accurately classify the severity of lung nodules. Our approach leverages deep transfer learning and adopts an ensemble learning approach. Specifically, three state-of-the-art convolutional neural networks (CNN) models, namely ResNet-152, DenseNet-169, and EfficientNet-B7, are employed. To enhance the ensemble method’s performance, we introduce a novel scheme for selecting and assigning weights to each base model. Unlike conventional methods that often rely on manual experimentation to set weights, our approach fuses the scores of two standard assessment metrics, ROC-AUC score and F1-score, for a more accurate weight vector determination. To evaluate the effectiveness of our method, we conduct extensive testing using the publicly available CT scan dataset, LIDC-IDRI. Our proposed ensemble achieves an accuracy of 97.23%, surpassing various recent methods and outperforming commonly used ensemble techniques. Furthermore, our novel weight optimization strategy significantly reduces false negatives, leading to a sensitivity of 98.6%. The codes for the proposed work are available at https://github.com/iabh1shekbasu/LungCancerDetectionEnsemble.
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
Authors: Yihao Sun, Jiaji Zhang, Chengxing Jia, Haoxin Lin, Junyin Ye, Yang Yu
Date: 2023-07-01 00:00:00
Proceeding: PMLR 2023 7
Tasks: D4RL, model, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Uncertainty Quantification

For offline reinforcement learning (RL), model-based methods are expected to be data-efficient as they incorporate dynamics models to generate more data. However, due to inevitable model errors, straightforwardly learning a policy in the model typically fails in the offline setting. Previous studies have incorporated conservatism to prevent out-of-distribution exploration. For example, MOPO penalizes rewards through uncertainty measures from predicting the next states, which we have discovered are loose bounds of the ideal uncertainty, i.e., the Bellman error. In this work, we propose MOdel-Bellman Inconsistency penalized offLinE Policy Optimization (MOBILE), a novel uncertainty-driven offline RL algorithm. MOBILE conducts uncertainty quantification through the inconsistency of Bellman estimations under an ensemble of learned dynamics models, which can be a better approximator to the true Bellman error, and penalizes the Bellman estimation based on this uncertainty. Empirically we have verified that our proposed uncertainty quantification can be significantly closer to the true Bellman error than the compared methods. Consequently, MOBILE outperforms prior offline RL approaches on most tasks of D4RL and NeoRL benchmarks.
High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods
Authors: Fabricio Batista Narcizo, Fernando Eustáquio Dantas dos Santos, Dan Witzner Hansen
Date: 2021-09-15 00:00:00
Proceeding: Vision 2021 9
Tasks: Gaze Estimation

This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5° and 0.5°. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.
uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional Computing
Authors: Sercan Aygun, Mehran Shoushtari Moghadam, M. Hassan Najafi
Date: 2023-11-16 00:00:00
Tasks: Position

Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without taking their bit significance into consideration. HDC has been shown to be efficient and robust in various data processing applications, including computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, the optimal design of hypervectors with high model accuracy requires powerful computing platforms for training. A more efficient approach to generating hypervectors is to create them dynamically during the training phase, which results in accurate, low-cost, and highly performable vectors. To this aim, we use low-discrepancy sequences to generate intensity hypervectors only, while avoiding position hypervectors. By doing so, the multiplication step in vector encoding is eliminated, resulting in a power-efficient HDC system. For the first time in the literature, our proposed approach employs lightweight vector generators utilizing unary bit-streams for efficient encoding of data instead of using conventional comparator-based generators.
Measuring Five Accountable Talk Moves to Improve Instruction at Scale
Authors: Ashlee Kupor, Candice Morgan, Dorottya Demszky
Date: 2023-11-02 00:00:00

Providing consistent, individualized feedback to teachers on their instruction can improve student learning outcomes. Such feedback can especially benefit novice instructors who teach on online platforms and have limited access to instructional training. To build scalable measures of instruction, we fine-tune RoBERTa and GPT models to identify five instructional talk moves inspired by accountable talk theory: adding on, connecting, eliciting, probing and revoicing students' ideas. We fine-tune these models on a newly annotated dataset of 2500 instructor utterances derived from transcripts of small group instruction in an online computer science course, Code in Place. Although we find that GPT-3 consistently outperforms RoBERTa in terms of precision, its recall varies significantly. We correlate the instructors' use of each talk move with indicators of student engagement and satisfaction, including students' section attendance, section ratings, and assignment completion rates. We find that using talk moves generally correlates positively with student outcomes, and connecting student ideas has the largest positive impact. These results corroborate previous research on the effectiveness of accountable talk moves and provide exciting avenues for using these models to provide instructors with useful, scalable feedback.
EIT: Earnest Insight Toolkit for Evaluating Students' Earnestness in Interactive Lecture Participation Exercises
Authors: Mihran Miroyan, Shiny Weng, Rahul Shah, Lisa Yan, Narges Norouzi
Date: 2023-10-31 00:00:00

In today's rapidly evolving educational landscape, traditional modes of passive information delivery are giving way to transformative pedagogical approaches that prioritize active student engagement. Within the context of large-scale hybrid classrooms, the challenge lies in fostering meaningful and active interaction between students and course content. This study delves into the significance of measuring students' earnestness during interactive lecture participation exercises. By analyzing students' responses to interactive lecture poll questions, establishing a clear rubric for evaluating earnestness, and conducting a comprehensive assessment, we introduce EIT (Earnest Insight Toolkit), a tool designed to assess students' engagement within interactive lecture participation exercises - particularly in the context of large-scale hybrid classrooms. Through the utilization of EIT, our objective is to equip educators with valuable means of identifying at-risk students for enhancing intervention and support strategies, as well as measuring students' levels of engagement with course content.
AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?
Authors: Imen Azaiz, Oliver Deckarm, Sven Strickroth
Date: 2023-10-24 00:00:00
Tasks: Code Generation, Fault localization

Timely formative feedback is considered as one of the most important drivers for effective learning. Delivering timely and individualized feedback is particularly challenging in large classes in higher education. Recently Large Language Models such as GPT-3 became available to the public that showed promising results on various tasks such as code generation and code explanation. This paper investigates the potential of AI in providing personalized code correction and generating feedback. Based on existing student submissions of two different real-world assignments, the correctness of the AI-aided e-assessment as well as the characteristics such as fault localization, correctness of hints, and code style suggestions of the generated feedback are investigated. The results show that 73 % of the submissions were correctly identified as either correct or incorrect. In 59 % of these cases, GPT-3.5 also successfully generated effective and high-quality feedback. Additionally, GPT-3.5 exhibited weaknesses in its evaluation, including localization of errors that were not the actual errors, or even hallucinated errors. Implications and potential new usage scenarios are discussed.
Proceedings of the 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD-23)
Authors: Masoud Makrehchi, Dell Zhang, Alina Petrova, John Armour
Date: 2023-10-19 00:00:00
Tasks: Management

This is the Proceedings of the 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD-23) which took place in conjunction with the 32nd ACM International Conference on Information and Knowledge Management (CIKM-2023) at the University of Birmingham, Birmingham, UK on Sunday 22nd October 2023.
Chatbot-supported Thesis Writing: An Autoethnographic Report
Authors: Nicolas Schwenke, Heinrich Söbke, Eckhard Kraft
Date: 2023-10-14 00:00:00
Tasks: Chatbot, Language Modelling, Large Language Model, valid

The release of the large language model based chatbot ChatGPT in November 2022 has brought considerable attention to the subject of artificial intelligence, not only in the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this study, the first author was asked to write his bachelor's thesis exploiting ChatGPT. For tracing the impact of ChatGPT, methodically an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs and secondly, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along to the recommendations for students regarding the use of ChatGPT suggested by Gimpel et al. (2023). In conclusion, ChatGPT is beneficial in thesis writing during various activities, such as brainstorming, structuring and text revision. However, there arise limitations, e.g., in referencing. Thus, ChatGPT requires a continuous validation of the outcomes generated fostering learning. Currently, ChatGPT is to be valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner's meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected and the research question needs to be reevaluated from time to time.
ChatGPT at the Speed of Light: Optical Comb-Based Monolithic Photonic-Electronic Linear-Algebra Accelerators
Authors: Tzu-Chien Hsueh, Yeshaiahu Fainman, Bill Lin
Date: 2023-11-19 00:00:00

This paper proposes to adopt advanced monolithic silicon-photonics integrated-circuits manufacturing capabilities to achieve a system-on-chip photonic-electronic linear-algebra accelerator with the features of optical comb-based broadband incoherent photo-detections and high-dimensional operations of consecutive matrix-matrix multiplications to enable substantial leaps in computation density and energy efficiency, with practical considerations of power/area overhead due to photonic-electronic on-chip conversions, integrations, and calibrations through holistic co-design approaches to support attention-head mechanism based deep-learning neural networks used in Large Language Models and other emergent applications.
Quantum Image Segmentation Based on Grayscale Morphology
Authors: Wenjie Liu, Lu Wang, Mengmeng Cui
Date: 2023-10-02 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem will emerge. In order to solve this problem, a quantum image segmentation algorithm is proposed in this paper, which can use quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and then quickly segment the image into a binary image. In addition, several quantum circuit units, including dilation, erosion, bottom hat transformation, top hat transformation, etc., are designed in detail, and then they are combined together to construct the complete quantum circuits for segmenting the NEQR images. For a 2^n * 2^n image with q grayscale levels, the complexity of our algorithm can be reduced to O(n^2+q), which is an exponential speedup than the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.