pwc-archive/papers-with-abstracts huggingface dataset:
Information literacy development and assessment at school level: a systematic review of the literature
Authors: Luz Chourio-Acevedo, Jacqueline Köhler, Carla Coscarelli, Daniel Gacitúa, Verónica Proaño-Ríos, Roberto González-Ibáñez
Date: 2024-04-29 00:00:00
Tasks: Articles, Systematic Literature Review

Information literacy (IL) involves a group of competences and fundamental skills in the 21st century. Today, society operates around information, which is challenging considering the vast amount of content available online. People must be capable of searching, critically assessing, making sense of, and communicating information. This set of competences must be properly developed since childhood, especially if considering early age access to online resources. To better understand the evolution and current status of IL development and assessment at school (K-12) level, we conducted a systematic literature review based on the guidelines established by the PRISMA statement. Our review led us to an initial set of 1,234 articles, from which 53 passed the inclusion criteria. These articles were used to address six research questions focused on IL definitions, skills, standards, and assessment tools. Our review shows IL evolution over the years and how it has been formalisedthrough definitions and standards. These findings reveal key gaps that must be addressed in order to advance the field further. Keywords: Elementary education, Information literacy, Secondary education, 21st Century abilities.
Using deep learning for community discovery in social networks
Authors: Di Jin, Meng Ge, Zhixuan Li, Wenhuan Lu, Dongxiao He, Francoise Fogelman-Soulie
Date: 2017-11-06 00:00:00
Proceeding: International Conference on Tools with Artificial Intelligence (ICTAI) 2017 11
Tasks: Clustering, Community Detection, Deep Learning

Community detection is an important task in social network analysis. Existing methods typically use the topological information alone, and ignore the rich information available in the content data. Recently, some researchers have noticed that user profiles can also benefit to community detection, and hence the combination of topology and node contents has become a new hot topic. Some methods using both topology and content have been proposed. However, they often suffer from two drawbacks: 1) they cannot extract a potential deep representation of the network; 2) they cannot automatically weight different information sources with adequate balance parameters. To overcome these issues, we propose a deep integration representation (DIR) algorithm via deep joint reconstruction, which is motivated by the similarity between deep feedforward auto-encoders and spectral clustering in terms of matrix reconstruction. Thanks to spectral clustering which is one of the best community detection methods, the proposed new method is also good at community discovery task. In addition, DIR has further benefit because it not only provides a nonlinear and deep representation of the network, but also learns the most suitable balance between different components automatically. We compare the proposed new approach with nine state-of-the-art community detection methods on eight real relatively large networks. The experimental results show the definite superiority of this new approach.
2D Amodal Instance Segmentation Guided by 3D Shape Prior
Authors: Zhixuan Li, Weining Ye, Tingting Jiang, Tiejun Huang
Date: 2022-06-01 00:00:00
Proceeding: European Conference on Computer Vision (ECCV) 2022 6
Tasks: 3D Reconstruction, Amodal Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

Amodal instance segmentation aims to predict the complete mask of the occluded instance, including both visible and invisible regions. Existing 2D AIS methods learn and predict the complete silhouettes of target instances in 2D space. However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances. With the 2D masks learned, 2D amodal methods are hard to generalize to new viewpoints not included in the training dataset. To tackle these problems, we are motivated by observations that (1) a 2D amodal mask is the projection of a 3D complete model, and (2) the 3D complete model can be recovered and reconstructed from the occluded 2D object instances. This paper builds a bridge to link the 2D occluded instances with the 3D complete models by 3D reconstruction and utilizes 3D shape prior for 2D AIS. To deal with the diversity of 3D shapes, our method is pretrained on large 3D reconstruction datasets for high-quality results. And we adopt the unsupervised 3D reconstruction method to avoid relying on 3D annotations. In this approach, our method can reconstruct 3D models from occluded 2D object instances and generalize to new unseen 2D viewpoints of the 3D object. Experiments demonstrate that our method outperforms all existing 2D AIS methods.
OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation
Authors: Zhixuan Li, Ruohua Shi, Tiejun Huang, Tingting Jiang
Date: 2023-06-04 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 6
Tasks: Amodal Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

The Amodal Instance Segmentation (AIS) task aims to infer the complete mask of occluded instance. Under many circumstances, existing methods treat occluded objects as unoccluded ones, and vice versa, leading to inaccurate predictions. This is because existing AIS methods do not explicitly utilize the occlusion rates of each object as supervision. However, occlusion information is critical for the methods to recognize whether the target objects are occluded. Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance. In this paper, a simple yet effective Occlusion-aware transformer-based model, OAFormer, is proposed for accurate amodal instance segmentation. The goal of OAFormer is to learn the occlusion discriminative features. Novel components are proposed to enable OAFormer to be occlusion distinguishable. We conduct extensive experiments on two challenging AIS datasets to evaluate the effectiveness of our method. OAFormer outperforms state-of-the-art methods by large margins.
GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation
Authors: Zhixuan Li, Weining Ye, Tingting Jiang, Tiejun Huang
Date: 2023-09-22 00:00:00
Proceeding: IEEE Transactions on Multimedia 2023 9
Tasks: Amodal Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

Amodal instance segmentation (AIS) predicts the complete shape of the occluded object, including both visible and occluded regions. Because visual clues are lacking, the occluded region is difficult to segment accurately. In human amodal perception, shape-prior knowledge is helpful for AIS. The previous method uses a 2D shape prior by rote memorizing , establishing a shape dictionary and retrieving the closest mask to the segmentation result. However, this approach cannot obtain the shape prior, which is not prestored in the shape dictionary. In this article, to improve generalization ability, we propose a generative invariant shape-prior network (GIN), simulating the human perception process that learns the basic shape, which is invariant to transformations, including translation, rotation, and scaling. We design a novel framework that decouples the learning of shape priors from transformation . GIN is end-to-end trainable and needs no dictionary establishment, making the whole pipeline efficient. GIN outperforms state-of-the-art methods on three public datasets (D2SA, COCOA-cls, and KINS) with large margins.
Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition
Authors: Katerina Papadimitriou, Gerasimos Potamianos
Date: 2023-08-22 00:00:00
Proceeding: Conference of the International Speech Communication Association (INTERSPEECH) 2023 8
Tasks: Knowledge Distillation, Position, Sign Language Recognition

In this paper, we propose a novel Transformer-based approach for continuous sign language recognition (CSLR) from videos, aiming to address the shortcomings of traditional Transformers in learning local semantic context of SL. Specifically, the proposed relies on two distinct components: (a) a window-based RNN module to capture local temporal context and (b) a Transformer encoder, enhanced with local modeling via Gaussian bias and relative position information, as well as with global structure modeling through multi-head attention. To further improve model performance, we design a multimodal framework that applies the proposed to both appearance and motion signing streams, aligning their posteriors through a guiding CTC technique. Further, we achieve visual feature and gloss sequence alignment by incorporating a knowledge distillation loss. Experimental evaluation on two popular German CSLR datasets, demonstrates the superiority of our model.
Sign Language Recognition via Deformable 3D Convolutions and Modulated Graph Convolutional Networks
Authors: Katerina Papadimitriou, Gerasimos Potamianos
Date: 2023-06-07 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 6
Tasks: graph construction, Sign Language Recognition

Automatic sign language recognition (SLR) remains challenging, especially when employing RGB video alone (i.e., with no depth or special glove-based input) and under a signer-independent (SI) framework, due to inter-personal signing variation. In this paper, we address SI isolated SLR from RGB video, proposing an innovative deep-learning framework that leverages multi-modal appearanceand skeleton-based information. Specifically, we propose three components for the first time in SLR: (i) a modified version of the ResNet2+1D network to capture signing appearance information, where spatial and temporal convolutions are substituted by their deformable counterparts, accomplishing both prevalent spatial modeling potential and motion-aware modeling adaptability; (ii) a novel spatio-temporal graph convolutional network (ST-GCN) that integrates a GCN variant, involving weight and affinity modulation for modeling diverse correlations between different body joints beyond the physical human skeleton structure, followed by a self-attention layer and a temporal convolution; and (iii) the “PIXIE” 3D human pose and shape regressor to generate 3D joint-rotation parameterization used for ST-GCN graph construction. Both appearance- and skeleton-based streams are ensembled in the proposed system and evaluated on two datasets of isolated signs, one in Turkish and one in Greek. Our system outperforms the state-of-the-art on the second set, yielding 53% relative error rate reduction (2.45% absolute), while it performs on par with the best reported system on the first.
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges
Authors: Hyunjin Bae, Keunyoung Jang, Yun-Kyu An
Date: 2020-05-29 00:00:00
Proceeding: Structural Health Monitoring 2020 5
Tasks: Crack Segmentation, Deep Learning, Image Generation, Super-Resolution

This article proposes a new end-to-end deep super-resolution crack network (SrcNet) for improving computer vision–based automated crack detectability. The digital images acquired from large-scale civil infrastructures for crack detection using unmanned robots often suffer from motion blur and lack of pixel resolution, which may degrade the corresponding crack detectability. The proposed SrcNet is able to significantly enhance the crack detectability by augmenting the pixel resolution of the raw digital image through deep learning. SrcNet basically consists of two phases: phase I—deep learning–based super resolution (SR) image generation and phase II—deep learning–based automated crack detection. Once the raw digital images are obtained from a target bridge surface, phase I of SrcNet generates the corresponding SR images to the raw digital images. Then, phase II automatically detects cracks from the generated SR images, making it possible to remarkably improve the crack detectability. SrcNet is experimentally validated using the digital images obtained using a climbing robot and an unmanned aerial vehicle from in situ concrete bridges located in South Korea. The validation test results reveal that the proposed SrcNet shows 24% better crack detectability compared to the crack detection results using the raw digital images.
Evaluation of Thermal Performance of a Wick-free Vapor Chamber in Power Electronics Cooling
Authors: Arani Mukhopadhyay, Anish Pal, Congbo Bao, Mohamad Jafari Gukeh, Sudip K. Mazumder, Constantine M. Megaridis
Date: 2024-04-30 00:00:00

Efficient thermal management in high-power electronics cooling can be achieved using phase-change heat transfer devices, such as vapor chambers. Traditional vapor chambers use wicks to transport condensate for efficient thermal exchange and to prevent "dry-out" of the evaporator. However, wicks in vapor chambers present significant design challenges arising out of large pressure drops across the wicking material, which slows down condensate transport rates and increases the chances for dry-out. Thicker wicks add to overall thermal resistance, while deterring the development of thinner devices by limiting the total thickness of the vapor chamber. Wickless vapor chambers eliminate the use of metal wicks entirely, by incorporating complementary wettability-patterned flat plates on both the evaporator and the condenser side. Such surface modifications enhance fluid transport on the evaporator side, while allowing the chambers to be virtually as thin as imaginable, thereby permitting design of thermally efficient thin electronic cooling devices. While wick-free vapor chambers have been studied and efficient design strategies have been suggested, we delve into real-life applications of wick-free vapor chambers in forced air cooling of high-power electronics. An experimental setup is developed wherein two Si-based MOSFETs of TO-247-3 packaging having high conduction resistance, are connected in parallel and switched at 100 kHz, to emulate high frequency power electronics operations. A rectangular copper wick-free vapor chamber spreads heat laterally over a surface 13 times larger than the heating area. This chamber is cooled externally by a fan that circulates air at room temperature. The present experimental setup extends our previous work on wick-free vapor chambers, while demonstrating the effectiveness of low-cost air cooling in vapor-chamber enhanced high-power electronics applications.
Investigating the dissemination of STEM content on social media with computational tools
Authors: Oluwamayokun Oshinowo, Priscila Delgado, Meredith Fay, C. Alessandra Luna, Anjana Dissanayaka, Rebecca Jeltuhin, David R. Myers
Date: 2024-04-25 00:00:00
Tasks: Clustering, Sentiment Analysis

Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. We also share insights on how to optimize dissemination by analyzing data available exclusively to content creators as well as via sentiment analysis of comments.
Deep Learning for Educational Data Science
Authors: Juan D. Pinto, Luc Paquette
Date: 2024-04-12 00:00:00
Tasks: Deep Learning, Knowledge Tracing

With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of various interrelated research communities -- have turned their attention to leveraging these powerful algorithms within the domain of education. Use cases range from advanced knowledge tracing models that can leverage open-ended student essays or snippets of code to automatic affect and behavior detectors that can identify when a student is frustrated or aimlessly trying to solve problems unproductively -- and much more. This chapter provides a brief introduction to deep learning, describes some of its advantages and limitations, presents a survey of its many uses in education, and discusses how it may further come to shape the field of educational data science.
A Continual Relation Extraction Approach for Knowledge Graph Completeness
Authors: Sefika Efeoglu
Date: 2024-04-20 00:00:00
Tasks: Continual Relation Extraction, Knowledge Graphs, Management, named-entity-recognition, Named Entity Recognition, Relation, Relation Extraction

Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be converted into Knowledge Graphs, by leveraging an information extraction pipeline whose main tasks are named entity recognition and relation extraction. This thesis aims to develop a novel continual relation extraction method to identify relations (interconnections) between entities in a data stream coming from the real world. Domain-specific data of this thesis is corona news from German and Austrian newspapers.
Radio Resource Management Design for RSMA: Optimization of Beamforming, User Admission, and Discrete/Continuous Rates with Imperfect SIC
Authors: L. F. Abanto-Leon, A. Krishnamoorthy, A. Garcia-Saavedra, G. H. Sim, R. Schober, M. Hollick
Date: 2024-04-30 00:00:00
Tasks: Management

This paper investigates the radio resource management (RRM) design for multiuser rate-splitting multiple access (RSMA), accounting for various characteristics of practical wireless systems, such as the use of discrete rates, the inability to serve all users, and the imperfect successive interference cancellation (SIC). Specifically, failure to consider these characteristics in RRM design may lead to inefficient use of radio resources. Therefore, we formulate the RRM of RSMA as optimization problems to maximize respectively the weighted sum rate (WSR) and weighted energy efficiency (WEE), and jointly optimize the beamforming, user admission, discrete/continuous rates, accounting for imperfect SIC, which result in nonconvex mixed-integer nonlinear programs that are challenging to solve. Despite the difficulty of the optimization problems, we develop algorithms that can find high-quality solutions. We show via simulations that carefully accounting for the aforementioned characteristics, can lead to significant gains. Precisely, by considering that transmission rates are discrete, the transmit power can be utilized more intelligently, allocating just enough power to guarantee a given discrete rate. Additionally, we reveal that user admission plays a crucial role in RSMA, enabling additional gains compared to random admission by facilitating the servicing of selected users with mutually beneficial channel characteristics. Furthermore, provisioning for possibly imperfect SIC makes RSMA more robust and reliable.
The Future of Scientific Publishing: Automated Article Generation
Authors: Jeremy R. Harper
Date: 2024-04-11 00:00:00
Tasks: Articles, Language Modeling, Language Modelling, Large Language Model

This study introduces a novel software tool leveraging large language model (LLM) prompts, designed to automate the generation of academic articles from Python code a significant advancement in the fields of biomedical informatics and computer science. Selected for its widespread adoption and analytical versatility, Python served as a foundational proof of concept; however, the underlying methodology and framework exhibit adaptability across various GitHub repo's underlining the tool's broad applicability (Harper 2024). By mitigating the traditionally time-intensive academic writing process, particularly in synthesizing complex datasets and coding outputs, this approach signifies a monumental leap towards streamlining research dissemination. The development was achieved without reliance on advanced language model agents, ensuring high fidelity in the automated generation of coherent and comprehensive academic content. This exploration not only validates the successful application and efficiency of the software but also projects how future integration of LLM agents which could amplify its capabilities, propelling towards a future where scientific findings are disseminated more swiftly and accessibly.
Disentangling Exploration from Exploitation
Authors: Alessandro Lizzeri, Eran Shmaya, Leeat Yariv
Date: 2024-04-29 00:00:00
Tasks: Disentanglement, Multi-Armed Bandits

Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio. We characterize the optimal experimentation policy when exploration and exploitation are disentangled in the case of Poisson bandits, allowing for general news structures. The optimal policy features complete learning asymptotically, exhibits lots of persistence, but cannot be identified by an index a la Gittins. Disentanglement is particularly valuable for intermediate parameter values.
Uncovering the Metaverse within Everyday Environments: a Coarse-to-Fine Approach
Authors: Liming Xu, Dave Towey, Andrew P. French, Steve Benford
Date: 2024-04-11 00:00:00

The recent release of the Apple Vision Pro has reignited interest in the metaverse, showcasing the intensified efforts of technology giants in developing platforms and devices to facilitate its growth. As the metaverse continues to proliferate, it is foreseeable that everyday environments will become increasingly saturated with its presence. Consequently, uncovering links to these metaverse items will be a crucial first step to interacting with this new augmented world. In this paper, we address the problem of establishing connections with virtual worlds within everyday environments, especially those that are not readily discernible through direct visual inspection. We introduce a vision-based approach leveraging Artcode visual markers to uncover hidden metaverse links embedded in our ambient surroundings. This approach progressively localises the access points to the metaverse, transitioning from coarse to fine localisation, thus facilitating an exploratory interaction process. Detailed experiments are conducted to study the performance of the proposed approach, demonstrating its effectiveness in Artcode localisation and enabling new interaction opportunities.
Downlink Pilots are Essential for Cell-Free Massive MIMO with Multi-Antenna Users
Authors: Eren Berk Kama, Junbeom Kim, Emil Björnson
Date: 2024-04-29 00:00:00

We consider a cell-free massive MIMO system with multiple antennas on the users and access points. In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well when having single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent since the effective channel matrix does not harden when having multi-antenna users. We propose a pilot-based downlink estimation scheme and derive a new SE expression that utilizes zero-forcing combining. We show numerically how the number of users and user antennas affects the SE.