pwc-archive/papers-with-abstracts huggingface dataset:
Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation
Authors: Akriti Verma, Shama Islam, Valeh Moghaddam, Adnan Anwar, Sharon Horwood
Date: 2024-08-05 00:00:00
Tasks: Multi-Armed Bandits

Interpersonal communication plays a key role in managing people's emotions, especially on digital platforms. Studies have shown that people use social media and consume online content to regulate their emotions and find support for rest and recovery. However, these platforms are not designed for emotion regulation, which limits their effectiveness in this regard. To address this issue, we propose an approach to enhance Interpersonal Emotion Regulation (IER) on online platforms through content recommendation. The objective is to empower users to regulate their emotions while actively or passively engaging in online platforms by crafting media content that aligns with IER strategies, particularly empathic responding. The proposed recommendation system is expected to blend system-initiated and user-initiated emotion regulation, paving the way for real-time IER practices on digital media platforms. To assess the efficacy of this approach, a mixed-method research design is used, including the analysis of text-based social media data and a user survey. Digital applications has served as facilitators in this process, given the widespread recognition of digital media applications for Digital Emotion Regulation (DER). The study collects 37.5K instances of user posts and interactions on Reddit over a year to design a Contextual Multi-Armed Bandits (CMAB) based recommendation system using features from user activity and preferences. The experimentation shows that the empathic recommendations generated by the proposed recommendation system are preferred by users over widely accepted ER strategies such as distraction and avoidance.
Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier
Authors: HUMERA SABIR, KIFAYAT ULLAH KHAN, OMER ISHAQ, ABDULWAHAB ALAZEB, HANAN ALJUAID, ASAAD ALGARNI, JEONGMIN PARK
Date: 2024-05-08 00:00:00
Proceeding: IEEE Access 2024 5
Tasks: CBC TEST, feature selection, Photoplethysmography (PPG)

Hemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to
rest of the body tissues. Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition
characterized by a deficiency of red blood cells. This measurement is particularly vital before initiating
blood transfusions for thalassemia patients. Non-invasive estimation of hemoglobin levels can be achieved
through photoplethysmography (PPG)-based methods. PPG is an optical method to measure blood volume
changes in successive heart beats. PPG signals can be obtained from fingertip videos using a light source
and a photodetector. SmartphonePPG utilizes a smartphone’s flashlight as a light source and its camera as
a photodetector to acquire PPG signals, offering an affordable and portable point-of-care tool. Despite the
ubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG
characteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses
a significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset
comprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone’s
camera. The dataset, publicly accessible for research purposes (https://forms.gle/LB4qn81ZMqEuy3V27),
covers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL - 12.4 gm/dL).
Utilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to
estimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection
from PPG signals, overcoming a limitation in existing smartphone PPG-based hemoglobin estimation
systems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared
with the gold standard laboratory method, Complete Blood Count (CBC) test reports. In contrast, HemaApp,
a state-of-the-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on
our dataset. The accuracy and simplicity of our model position it as a promising alternative to existing noninvasive hemoglobin level estimation methods.
Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displays
Authors: Doğa Yılmaz, Towaki Takikawa, Duygu Ceylan, Kaan Akşit
Date: 2024-07-31 00:00:00
Tasks: Denoising, Image Denoising

Immersive displays are advancing rapidly in terms of delivering perceptually realistic images by utilizing emerging perceptual graphics methods such as foveated rendering. In practice, multiple such methods need to be performed sequentially for enhanced perceived quality. However, the limited power and computational resources of the devices that drive immersive displays make it challenging to deploy multiple perceptual models simultaneously. We address this challenge by proposing a computationally-lightweight, text-guided, learned multitasking perceptual graphics model. Given RGB input images, our model outputs perceptually enhanced images by performing one or more perceptual tasks described by the provided text prompts. Our model supports a variety of perceptual tasks, including foveated rendering, dynamic range enhancement, image denoising, and chromostereopsis, through multitask learning. Uniquely, a single inference step of our model supports different permutations of these perceptual tasks at different prompted rates (i.e., mildly, lightly), eliminating the need for daisy-chaining multiple models to get the desired perceptual effect. We train our model on our new dataset of source and perceptually enhanced images, and their corresponding text prompts. We evaluate our model's performance on embedded platforms and validate the perceptual quality of our model through a user study. Our method achieves on-par quality with the state-of-the-art task-specific methods using a single inference step, while offering faster inference speeds and flexibility to blend effects at various intensities.
CatalogBank: A Structured and Interoperable Catalog Dataset with a Semi-Automatic Annotation Tool (DocumentLabeler) for Engineering System Design
Authors: Hasan Sinan Bank, Daniel R. Herber
Date: 2024-08-15 00:00:00

In the realm of document engineering and Natural Language Processing (NLP), the integration of digitally born catalogs into product design processes presents a novel avenue for enhancing information extraction and interoperability. This paper introduces CatalogBank, a dataset developed to bridge the gap between textual descriptions and other data modalities related to engineering design catalogs. We utilized existing information extraction methodologies to extract product information from PDF-based catalogs to use in downstream tasks to generate a baseline metric. Our approach not only supports the potential automation of design workflows but also overcomes the limitations of manual data entry and non-standard metadata structures that have historically impeded the seamless integration of textual and other data modalities. Through the use of DocumentLabeler, an open-source annotation tool adapted for our dataset, we demonstrated the potential of CatalogBank in supporting diverse document-based tasks such as layout analysis and knowledge extraction. Our findings suggest that CatalogBank can contribute to document engineering and NLP by providing a robust dataset for training models capable of understanding and processing complex document formats with relatively less effort using the semi-automated annotation tool DocumentLabeler.
AUTH at BioLaySumm 2024: Bringing Scientific Content to Kids
Authors: Loukritia Stefanou, Tatiana Passali, Grigorios Tsoumakas
Date: 2024-08-01 00:00:00
Proceeding: Association for Computational Linguistics 2024 8
Tasks: Articles

The BioLaySumm 2024 shared task at the ACL 2024 BioNLP workshop aims to transform biomedical research articles into lay summaries suitable for a broad audience, including children. We utilize the BioBART model, designed for the biomedical sector, to convert complex scientific data into clear, concise summaries. Our dataset, which includes a range of scientific abstracts, enables us to address the diverse information needs of our audience. This focus ensures that our summaries are accessible to both general and younger lay audience. Additionally, we employ specialized tokens and augmentation techniques to optimize the model’s performance. Our methodology proved effective, earning us the 7th rank on the final leaderboard out of 57 participants.
Handwritten Code Recognition for Pen-and-Paper CS Education
Authors: Md Sazzad Islam, Moussa Koulako Bala Doumbouya, Christopher D. Manning, Chris Piech
Date: 2024-08-07 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Optical Character Recognition, Optical Character Recognition (OCR)

Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intelligent support tools or "just trying things out". The familiar environment of pens and paper also lessens the cognitive load of students with no prior experience with computers, for whom the mere basic usage of computers can be intimidating. Finally, this teaching approach opens learning opportunities to students with limited access to computers. However, a key obstacle is the current lack of teaching methods and support software for working with and running handwritten programs. Optical character recognition (OCR) of handwritten code is challenging: Minor OCR errors, perhaps due to varied handwriting styles, easily make code not run, and recognizing indentation is crucial for languages like Python but is difficult to do due to inconsistent horizontal spacing in handwriting. Our approach integrates two innovative methods. The first combines OCR with an indentation recognition module and a language model designed for post-OCR error correction without introducing hallucinations. This method, to our knowledge, surpasses all existing systems in handwritten code recognition. It reduces error from 30\% in the state of the art to 5\% with minimal hallucination of logical fixes to student programs. The second method leverages a multimodal language model to recognize handwritten programs in an end-to-end fashion. We hope this contribution can stimulate further pedagogical research and contribute to the goal of making CS education universally accessible. We release a dataset of handwritten programs and code to support future research at https://github.com/mdoumbouya/codeocr
RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction
Authors: Hongbo Chen, Yuchong Gao, Shuhang Zhang, Jiangjie Wu, Yuexin Ma, Rui Zheng
Date: 2024-08-14 00:00:00

The reconstruction of high-quality shape geometry is crucial for developing freehand 3D ultrasound imaging. However, the shape reconstruction of multi-view ultrasound data remains challenging due to the elevation distortion caused by thick transducer probes. In this paper, we present a novel learning-based framework RoCoSDF, which can effectively generate an implicit surface through continuous shape representations derived from row-column scanned datasets. In RoCoSDF, we encode the datasets from different views into the corresponding neural signed distance function (SDF) and then operate all SDFs in a normalized 3D space to restore the actual surface contour. Without requiring pre-training on large-scale ground truth shapes, our approach can synthesize a smooth and continuous signed distance field from multi-view SDFs to implicitly represent the actual geometry. Furthermore, two regularizers are introduced to facilitate shape refinement by constraining the SDF near the surface. The experiments on twelve shapes data acquired by two ultrasound transducer probes validate that RoCoSDF can effectively reconstruct accurate geometric shapes from multi-view ultrasound data, which outperforms current reconstruction methods. Code is available at https://github.com/chenhbo/RoCoSDF.
Abductive Reasoning in a Paraconsistent Framework
Authors: Meghyn Bienvenu, Katsumi Inoue, Daniil Kozhemiachenko
Date: 2024-08-01 00:00:00

We explore the problem of explaining observations starting from a classically inconsistent theory by adopting a paraconsistent framework. We consider two expansions of the well-known Belnap--Dunn paraconsistent four-valued logic $\mathsf{BD}$: $\mathsf{BD}_\circ$ introduces formulas of the form $\circ\phi$ (the information on $\phi$ is reliable), while $\mathsf{BD}_\triangle$ augments the language with $\triangle\phi$'s (there is information that $\phi$ is true). We define and motivate the notions of abduction problems and explanations in $\mathsf{BD}_\circ$ and $\mathsf{BD}_\triangle$ and show that they are not reducible to one another. We analyse the complexity of standard abductive reasoning tasks (solution recognition, solution existence, and relevance / necessity of hypotheses) in both logics. Finally, we show how to reduce abduction in $\mathsf{BD}_\circ$ and $\mathsf{BD}_\triangle$ to abduction in classical propositional logic, thereby enabling the reuse of existing abductive reasoning procedures.
Evidence and quantification of cooperation of driving agents in mixed traffic flow
Authors: Di Chen, Jia Li, H. Michael Zhang
Date: 2024-07-31 00:00:00

Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems with multiple agents. Understanding the formation of cooperation in mixed traffic is of theoretical interest in its own right, and could also benefit the design and operations of future automated and mixed-autonomy transportation systems. However, how cooperativeness of driving agents can be defined and identified from empirical data seems ambiguous and this hinders further empirical characterizations of the phenomenon and revealing its behavior mechanisms. Towards mitigating this gap, in this paper, we propose a unified conceptual framework to identify collective cooperativeness of driving agents. This framework expands the concept of collective rationality from our recent model (Li et al. 2022a), making it empirically identifiable and behaviorally interpretable in realistic (microscopic and dynamic) settings. This framework integrates mixed traffic observations at both microscopic and macroscopic scales to estimate critical behavioral parameters that describe the collective cooperativeness of driving agents. Applying this framework to NGSIM I-80 trajectory data, we empirically confirm the existence of collective cooperation and quantify the condition and likelihood of its emergence. This study provides the first empirical understanding of collective cooperativeness in human-driven mixed traffic and points to new possibilities to manage mixed autonomy traffic systems.
Regularized Contrastive Partial Multi-view Outlier Detection
Authors: Yijia Wang, Qianqian Xu, Yangbangyan Jiang, Siran Dai, Qingming Huang
Date: 2024-08-02 00:00:00
Tasks: Attribute, Contrastive Learning, Outlier Detection

In recent years, multi-view outlier detection (MVOD) methods have advanced significantly, aiming to identify outliers within multi-view datasets. A key point is to better detect class outliers and class-attribute outliers, which only exist in multi-view data. However, existing methods either is not able to reduce the impact of outliers when learning view-consistent information, or struggle in cases with varying neighborhood structures. Moreover, most of them do not apply to partial multi-view data in real-world scenarios. To overcome these drawbacks, we propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD). In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Specifically, we propose (1) An outlier-aware contrastive loss with a potential outlier memory bank to eliminate their bias motivated by a theoretical analysis. (2) A neighbor alignment contrastive loss to capture the view-shared local structural correlation. (3) A spreading regularization loss to prevent the model from overfitting over outliers. With the Cross-view Relation Transfer technique, we could easily impute the missing view samples based on the features of neighbors. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors under different settings.
Cortical network reconfiguration aligns with shifts of basal ganglia and cerebellar influence
Authors: Kimberly Nestor
Date: 2024-08-15 00:00:00

Mammalian functional architecture flexibly adapts, transitioning from integration where information is distributed across the cortex, to segregation where information is focal in densely connected communities of brain regions. This flexibility in cortical brain networks is hypothesized to be driven by control signals originating from subcortical pathways, with the basal ganglia shifting the cortex towards integrated processing states and the cerebellum towards segregated states. In a sample of healthy human participants (N=242), we used fMRI to measure temporal variation in global brain networks while participants performed two tasks with similar cognitive demands (Stroop and Multi-Source Inference Task (MSIT)). Using the modularity index, we determined cortical networks shifted from integration (low modularity) at rest to high modularity during easier i.e. congruent (segregation). Increased task difficulty (incongruent) resulted in lower modularity in comparison to the easier counterpart indicating more integration of the cortical network. Influence of basal ganglia and cerebellum was measured using eigenvector centrality. Results correlated with decreases and increases in cortical modularity respectively, with only the basal ganglia influence preceding cortical integration. Our results support the theory the basal ganglia shifts cortical networks to integrated states due to environmental demand. Cerebellar influence correlates with shifts to segregated cortical states, though may not play a causal role.
Enhancing Expressway Ramp Merge Safety and Efficiency via Spatiotemporal Cooperative Control
Authors: Ting Peng, Xiaoxue Xu, Yuan Li, Jie Wu, Tao Li, Xiang Dong, Yincai Cai, Peng Wu, Sana Ullah
Date: 2024-08-15 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

In the context of autonomous driving on expressways, the issue of ensuring safe and efficient ramp merging remains a significant challenge. Existing systems often struggle to accurately assess the status and intentions of other vehicles, leading to a persistent occurrence of accidents despite efforts to maintain safe distances. This study proposes a novel spatiotemporal cooperative control approach integrating vehicle-road coordination to address this critical issue. A comprehensive methodology is developed, beginning with the calculation of safe distances under varying spatiotemporal conditions. This involves considering multiple factors, including vehicle speed differentials, positioning errors, and clock synchronization errors. Subsequently, an advanced vehicle conflict risk evaluation model is constructed. By incorporating collision acceleration and emergency acceleration as key parameters, this model offers a more accurate and detailed evaluation of potential risks during the ramp merging process. Based on the calculated safe distances and conflict risk evaluations, a mainline priority coordinated control method is formulated. This method enables the pre-planning of vehicle trajectories, effectively reducing conflicts among vehicles. Through rigorous simulations using diverse traffic volume and speed scenarios, the efficacy of the proposed strategy is validated. The results demonstrate remarkable improvements, with the average delay time reduced by an impressive 97.96% and fuel consumption decreased by 6.01%. These outcomes indicate that the proposed approach not only enhances the speed of vehicle merging but also significantly reduces latency and fuel consumption, thereby enhancing the overall performance of ramp merging operations.
Analytical Model of Modular Upper Limb Rehabilitation
Authors: M. Hasanlu, M. Siavashi
Date: 2024-08-15 00:00:00
Tasks: Articles, model

Configurable robots are made up of robotic modules that can be assembled or can configure themselves into multiple robot configurations. In this research plan, a method for upper-body rehabilitation will be discussed in the form of a modular robot with different morphologies. The advantage and superiority of designing an example of a robotic module for upper body rehabilitation is the ability to reset the modular robot system. In this research, a number of modules will be designed and implemented according to the needs of one-hand rehabilitation with different degrees of freedom. The design modules' performance and efficiency will be evaluated by simulating, making samples, and testing them. This article's research includes presenting a modular upper body rehabilitation robot in the wrist, elbow, and shoulder areas, as well as providing a suitable kinematic and dynamic model of the upper body rehabilitation robot to determine human-robot interaction forces and movement. The research also involves analyzing the mathematical model of the upper body rehabilitation robot to identify advanced control strategies that rely on force control and torque control. After reviewing the articles and research of others, we concluded that no one has yet worked on the design of a prototype robotic module for upper body rehabilitation in the specified order. In our pioneering research, we intend to address this important matter.
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation
Authors: Jinglei Feng, Zhengshuo Li
Date: 2024-08-15 00:00:00
Tasks: Federated Learning, Scheduling

The rapid growth of behind-the-meter (BTM) solar power generation systems presents challenges for distribution system planning and scheduling due to invisible solar power generation. To address the data leakage problem of centralized machine-learning methods in BTM solar power generation estimation, the federated learning (FL) method has been investigated for its distributed learning capability. However, the conventional FL method has encountered various challenges, including heterogeneity, communication failures, and malicious privacy attacks. To overcome these challenges, this study proposes a communication-robust and privacy-safe distributed estimation method for heterogeneous community-level BTM solar power generation. Specifically, this study adopts multi-task FL as the main structure and learns the common and unique features of all communities. Simultaneously, it embeds an updated parameters estimation method into the multi-task FL, automatically identifies similarities between any two clients, and estimates the updated parameters for unavailable clients to mitigate the negative effects of communication failures. Finally, this study adopts a differential privacy mechanism under the dynamic privacy budget allocation strategy to combat malicious privacy attacks and improve model training efficiency. Case studies show that in the presence of heterogeneity and communication failures, the proposed method exhibits better estimation accuracy and convergence performance as compared with traditional FL and localized learning methods, while providing stronger privacy protection.