Reviewerly/exHarmony huggingface dataset:
Where and What

Human drivers use their attentional mechanisms to focus on critical objects and make decisions while driving. As human attention can be revealed from gaze data, capturing and analyzing gaze information has emerged in recent years to benefit autonomous driving technology. Previous works in this context have primarily aimed at predicting "where" human drivers look at and lack knowledge of "what" objects drivers focus on. Our work bridges the gap between pixel-level and object-level attention prediction. Specifically, we propose to integrate an attention prediction module into a pretrained object detection framework and predict the attention in a grid-based style. Furthermore, critical objects are recognized based on predicted attended-to areas. We evaluate our proposed method on two driver attention datasets, BDD-A and DR(eye)VE. Our framework achieves competitive state-of-the-art performance in the attention prediction on both pixel-level and object-level but is far more efficient (75.3 GFLOPs less) in computation.
Variational quantum support vector machine based on $$\Gamma $$ matrix expansion and variational universal-quantum-state generator

We analyze a binary classification problem by using a support vector machine based on variational quantum-circuit model. We propose to solve a linear equation of the support vector machine by using a [Formula: see text] matrix expansion. In addition, it is shown that an arbitrary quantum state is prepared by optimizing a universal quantum circuit representing an arbitrary [Formula: see text] based on the steepest descent method. It may be a quantum generalization of Field-Programmable-Gate Array (FPGA).
Making Space for Cultural Infrastructure: The Breakdown and Maintenance Work of Independent Movie Theaters During Crisis

Independent movie theaters (IMTs) are a part of the cultural infrastructure that offers shared spaces for patron communities to access, share, and engage with cultural artifacts. In light of the COVID-19 pandemic, IMTs were mandated to shut down, resulting in unanticipated infrastructural breakdown. Drawing insights from a preliminary survey and interviews with staff members from 18 IMTs in the U.S., this paper attends to how this breakdown disrupted art and community engagement within patron communities. We investigate the sociotechnical practices of maintaining cultural infrastructure through 1) collaborating with community partners and external stakeholders, 2) screening films through online virtual cinema platforms, and 3) retaining community members through online platforms. Our work highlights the tensions and invisible human labor in this maintenance work. Together, this work intends to foreground cultural infrastructure and discuss how HCI can support and contribute to the design and oft-invisible maintenance of cultural infrastructure.
Attention-Based Deep Multiple Instance Learning with Adaptive Instance Sampling

One challenge of training deep neural networks with gigapixel whole-slide images (WSIs) in computational pathology is the lack of annotation at pixel level or regional level due to the high cost and time-consuming labeling effort. Multiple instance learning (MIL) and its attention-based versions are typical weakly supervised learning methods, which allow us to use slide-level labels directly, without the need for pixel or region labels, thus reducing the cost of annotation. However, training a deep neural network with thousands of image regions (patches) per slide is computationally expensive, and it needs a lot of time for convergence. This paper proposes a fast adaptive attention-based deep MIL approach. This approach adaptively selects image regions that are highly predictive of outcome and ignores image regions with little or no information. We empirically show that our proposed approach outperforms the random sampling approach while it is faster than the standard attention-based MIL method (which uses all image regions for training).
‘ShishuShurokkha’: A Transformative Justice Approach for Combating Child Sexual Abuse in Bangladesh

The challenge of designing against child sexual abuse becomes more complicated in conservative societies where talking about sex is tabooed. Our mix-method study, comprised of an online survey, five FGDs, and 20 semi-structured interviews in Bangladesh, investigates the common nature, location, and time of the abuse, post-incident support, and possible combating strategies. Besides revealing important facts, our findings highlight the need of decentering the design from the victims (children and/or guardians) to the community. Hence, building on the theory of transformative justice, we prototyped and evaluated ‘ShishuShurokkha’ – an online tool that involves the whole community by allowing anonymous bystander reporting, visualizing case-maps, connecting with legal, medical, and social support, and raising awareness. The evaluation of ShishuShurokkha shows the promise for such a communal approach toward combating child sexual abuse, and highlights the needs for sincere involvement of the government, NGOs, the legal, educational, and religious services in this.
Software Architecture Refactoring Based on Data Integration and Interoperability Issues in PeduliLindungi

The pandemic situation caused by coronavirus (COVID-19) which has infected more than 200 countries around the world. Every country should have strategic planning and a roadmap regarding how to stop the spread of the virus. One of those is trying to build an application to be an aggregator and a tool supporting other strategic planning. In Indonesia, Ministry of Communication and Informatics (KEMKOMINFO) has the responsibility to build the application to stop the spread of the virus and trace every movement done by the user or people. An official application built by KEMKOMINFO is PeduliLindungi. PeduliLindungi is an application that helps the government in tracking to stop the spread of Coronavirus Disease (COVID-19). On the other hand, PeduliLindungi is more complex because it involves many entities. And of course, it will cause a high impact level of Data Integration and Interoperability (DII). So, in this paper researchers will try to get all issues of DII in the PeduliLindungi application and try to analyze the system architecture and subsequently give solution to refactor the architecture used by PeduliLindungi.
Multi-Modal Unsupervised Brain Image Registration Using Edge Maps

Diffeomorphic deformable multi-modal image registration is a challenging task which aims to bring images acquired by different modalities to the same coordinate space and at the same time to preserve the topology and the invertibility of the transformation. Recent research has focused on leveraging deep learning approaches for this task as these have been shown to achieve competitive registration accuracy while being computationally more efficient than traditional iterative registration methods. In this work, we propose a simple yet effective unsupervised deep learning-based multi-modal image registration approach that benefits from auxiliary information coming from the gradient magnitude of the image, i.e. the image edges, during the training. The intuition behind this is that image locations with a strong gradient are assumed to denote a transition of tissues, which are locations of high information value able to act as a geometry constraint. The task is similar to using segmentation maps to drive the training, but the edge maps are easier and faster to acquire and do not require annotations. We evaluate our approach in the context of registering multi-modal (T1w to T2w) magnetic resonance (MR) brain images of different subjects using three different loss functions that are said to assist multi-modal registration, showing that in all cases the auxiliary information leads to better results without compromising the runtime.
Switching Between Standard Pointing Methods with Current and Emerging Computer Form Factors

We investigate performance characteristics when switching between four pointing methods: absolute touch, absolute pen, relative mouse, and relative trackpad. The established “subtraction method” protocol used in mode-switching studies is extended to test pairs of methods and accommodate switch direction, multiple baselines, and controlling relative cursor position. A first experiment examines method switching on and around the horizontal surface of a tablet. Results find switching between pen and touch is fastest, and switching between relative and absolute methods incurs additional time penalty. A second experiment expands the investigation to an emerging foldable all-screen laptop form factor where switching also occurs on an angled surface and along a smoothly curved hinge. Results find switching between trackpad and touch is fastest, with all switching times generally higher. Our work contributes missing empirical evidence for switching performance using modern input methods, and our results can inform interaction design for current and emerging device form factors.
Katika: An End-to-End System for Authoring Amateur Explainer Motion Graphics Videos

Explainer motion graphics videos that use a combination of graphical elements and movement to convey a visual message are becoming increasingly popular among amateur creators in different domains. But, to author motion graphics videos, amateurs either have to face a steep learning curve with professional design tools or struggle with re-purposing slide-sharing tools that are easier to access but have limited animation capabilities. To simplify the process of motion graphics authoring, we present the design and implementation of Katika, an end-to-end system for creating shots based on a script, adding artworks and animation from a crowdsourced library, and editing the video using semi-automated transitions. Our observational study illustrates that participants (N=11) enjoyed using Katika and, within a one-hour session, managed to create an explainer motion graphics video. We identify opportunities for future HCI research to lower the barriers to entry and democratize the authoring of motion graphics videos.
Toward supporting quality alt text in computing publications

While researchers have examined alternative (alt) text for social media and news contexts, few have studied the status and challenges for authoring alt text of figures in computing-related publications. These figures are distinct, often conveying dense visual information, and may necessitate unique accessibility solutions. Accordingly, we explored how to support authors in creating alt text in computing publications---specifically in the field of human-computer interaction (HCI). We conducted two studies: (1) an analysis of 300 recently published figures at a general HCI conference (ACM CHI), and (2) interviews with 10 researchers in HCI and related fields who have varying levels of experience writing alt text. Our findings characterize the prevalence, quality, and patterns of recent figure alt text and captions. We further identify challenges authors encounter, describing their workflow barriers and confusions around how to compose alt text for complex figures. We conclude by outlining a research agenda on process, education, and tooling opportunities to improve alt text in computing-related publications.
Myers-Briggs personality classification from social media text using pre-trained language models

In Natural Language Processing, the use of pre-trained language models has been shown to obtain state-of-the-art results in many downstream tasks such as sentiment analysis, author identification and others. In this work, we address the use of these methods for personality classification from text. Focusing on the Myers-Briggs (MBTI) personality model, we describe a series of experiments in which the well-known Bidirectional Encoder Representations from Transformers (BERT) model is fine-tuned to perform MBTI classification. Our main findings suggest that the current approach significantly outperforms well-known text classification models based on bag-of-words and static word embeddings alike across multiple evaluation scenarios, and generally outperforms previous work in the field.
Structure-Aware Denoising for Real-world Noisy Point Clouds with Complex Structures

Point cloud denoising is a crucial and fundamental step in geometry processing, which has achieved significant progress in the last two decades. Denoising real-world noisy point clouds is a very challenging problem since it is hard to describe the complex real-world noise by simple distributions such as Gaussian distribution. Furthermore, existing methods may suffer from performance degradation when dealing with real-world noisy point clouds with complex structures, which contain not only sharp features (sharp edges, sharp corners, etc.) but also smooth features, fine features, etc. To solve the above-mentioned problems, we propose a novel structure-aware denoising approach by exploiting the prior information in both external clean point clouds and the given noisy point cloud. We first group nonlocal self-similarity (NSS) patches from a set of external clean point clouds. Then, we employ the Gaussian Mixture Model (GMM) learning algorithm to learn external NSS priors over patch groups. Next, the internal priors are learned from the given noisy point cloud in the same way to refine the prior model. We integrate both the learned external and internal priors into a set of orthogonal dictionaries to efficiently estimate point normals. Finally, we propose a feature-aware point updating method through adaptive neighborhood selection to reposition points to match the estimated normals. Extensive experiments show that our approach achieves favorable comprehensive performance compared with many popular or state-of-the-art methods in terms of both objective and visual perception. The source code can be found at https://zhiyongsu.github.io.
Unsupervised Ensemble Distillation for Multi-Organ Segmentation

Multi-organ segmentation is a fundamental task in medical image processing. This paper explores a novel privacy friendly setting to train a multi-organ segmentation model, i.e., learning directly from multiple pre-trained single-organ segmentation models. We formulate it into a special unsupervised ensemble distillation problem. To solve the problem, a multi-teacher knowledge distillation framework is proposed, which leverages the soft labels predicted by pre-trained teacher models to train a student model, i.e., the multi-organ segmentation model. Considering the difference of the teachers in task speciality, the pseudo labels for each organ come from the corresponding teacher, and those of the background region are from all teachers. To deal with the problem of unmatched dimensionality between the teacher models and the student model, an output transformation method is introduced. The entire learning process requires only access to pre-trained models and a reasonable set of unsupervised target data, achieving a good balance between the privacy protection and model performance. Experimental results on a widely adopted benchmark dataset have shown the promise of the proposed method.
Cell Instance Segmentation Using Z-Stacks in Digital Cytology

This work addresses cell instance segmentation in digital cytology slides. Automated segmentation of cells is an essential step toward automated cell analysis and pathology diagnosis, however cell instance segmentation remains a challenging task, especially for touching or overlapping cells. This work first introduces a new large dataset of overlapping urothelial cell Z-stacks, allowing the comparison and validation of modern algorithms for cell instance segmentation, improving previously proposed methods by a large margin. In addition, a modified backbone architecture is proposed to directly use Z-stacks as inputs, relieving from the necessity of using Extended Depth of Field projections of the volumes. Such backbone allows to reduce the total prediction time per sample by a factor up to 8, with no drop in segmentation performances. Dataset and code are available at gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation.
Regional Self-Attention Convolutional Neural Network for Facial Expression Recognition

Facial expression recognition (FER) has been a challenging task in the field of artificial intelligence. In this paper, we propose a novel model, named regional self-attention convolutional neural network (RSACNN), for FER. Different from the previous methods, RSACNN makes full use of the facial texture of expression salient region, so yields a robust feature representation for FER. The proposed model contains two novel parts: regional local multiple pattern (RLMP) based on the improved K-means algorithm and the regional self-attention module (RSAM). First, RLMP uses the improved K-means algorithm to dynamically cluster the pixels to ensure the robustness of texture features with expression salient variation. Besides, the texture description is enhanced by extending the binary pattern to the multiple patterns and integrating the information of gray difference between pixels in the region. Next, RSAM can adaptively form weights for each region through the self-attention mechanism, and use rank regularization loss (RRLoss) to constrain the weights of different regions. By jointly combining RLMP and RSAM, RSACNN can effectively enhance the feature representation of expression salient regions, so that the performance of expression recognition can be improved. Extensive experiments on public datasets, i.e. CK[Formula: see text], Oulu-CASIA, Fer2013 and SFEW, prove the superiority of our method over state-of-the-art approaches.
“Hartal (Strike) Happens Here Everyday”: Understanding Impact of Disruption on Education in Kashmir

Sustainable Development Goal 4 promotes inclusive and equitable quality education and lifelong learning opportunities for all. However, regions with ongoing socio-political conflict suffer disruption to education and learning. We situate our work in Kashmir, India, affected by socio-political conflict for more than three decades. We did multiple field visits and conducted 21 semi-structured interviews with parents, teachers, students, and members of a non-government organization that runs Community Learning Centers in Kashmir. Our findings present the barriers in education caused by disruption and the role of community learning centers in overcoming the barriers within these contextual constraints. Further, we discuss engaging researchers and policymakers to leverage human infrastructure, embedding uncertainty into the design, infrastructuring trust, and content usability to develop solutions to make education more accessible. Despite significant research in HCI and Education, research in this particular context is under-explored, and our work contributes to filling this gap.
A novel bundling learning paradigm for named entity recognition

Multi-task learning (MTL) takes advantage of the information gained from multiple related NLP tasks in order to improve performance across these tasks. MTL-based models for named entity recognition (NER) have traditionally included relation extraction and (or) coreference resolution, which requires additional data annotations in NER corpora, whereas these annotations are often unavailable. Indeed, we generally model the NER task using either a sequence labeling-based or span-based approach. Motivated by MTL, we propose a novel Bundling Learning (BL) paradigm for the NER task, which is achieved by bundling sequence labeling-based and span-based NER models together, thus allowing us to model the task from both token- and span-level perspectives. In addition, BL does not require additional data annotations compared to MTL. In experiments on NER and RE tasks, it is shown that BL consistently improves the performance of the two tasks across several benchmark datasets. Detailed analyses further confirm the effectiveness of BL.
Artifact Identification in Digital Histopathology Images Using Few-Shot Learning

The advent of deep learning methods has led to breakthroughs in many digital histopathology image analysis tasks. However, automatic analysis is often impacted by the presence of various artifacts introduced during different tissue and slide processing stages. Therefore, it is desirable to have a generic artifacts identification algorithm to automatically exclude the artifacts regions in the downstream analysis. In this paper, considering the wide diversity of artifacts that present in histopathology images, and the difficulty to obtain a large amount of training data, we frame the artifacts identification task as a tile-based image classification problem and explore the feasibility of using a few-shot learning technique, specifically, prototypical network, for the task. We demonstrate that the use of prototypical network can effectively identify image tiles that contain various artifacts using a very small set of training images. The trained model is also able to generalize well to unseen artifacts. We validate the approach by applying it on both immunohistochemistry and H&E stained tissues images, showing that it is a more favorable approach compared to standard transfer learning for this application.
Seamless Visions, Seamful Realities: Anticipating Rural Infrastructural Fragility in Early Design of Digital Agriculture

Rural infrastructure is known to be more prone to breakdown than urban infrastructure. This paper explores how the fragility of rural infrastructure is reproduced through the process of engineering design. Building on values in design, we examine how eventual use is anticipated by engineering researchers building on emerging infrastructure for digital agriculture (DA). Our approach combines critically reflective technical systems-building with interviews with other practitioners to understand and address moments early in the design process where the eventual effects of DA systems may be being built-in. Our findings contrast researchers’ visions of seamless farming technologies with the seamful realities of their work to produce them. We trace how, when anticipating future use, the seams that researchers themselves experience disappear, other seams are hidden from view by institutional support, and seams end users may face are too distant to be in sight. We develop suggestions for the design of these technologies grounded in a more artful management of seamfulness and seamlessness during the process of design and development.
Why Did You/I Read but Not Reply? IM Users’ Unresponded-to Read-receipt Practices and Explanations of Them

We investigate instant-messaging (IM) users' sense-making and practices around read-receipts: a feature of IM apps for supporting the awareness of turn-taking, i.e., whether a message recipient has read a message. Using a grounded-theory approach, we highlight the importance of five contextual factors – situational, relational, interactional, conversational, and personal – that shape the variety of IM users' sense-making about read-receipts and strategies for utilizing them in different settings. This approach yields a 21-part typology comprising five types of senders' speculation about why their messages with read-receipts have not been answered; eight types of recipients' causes/reasons behind such non-response; and four types of senders' and recipients' subsequent strategies, respectively. Mismatches between senders' speculations about un-responded-to read-receipted messages (URRMs) and recipients' self-reported explanations are also discussed as sources of communicative friction. The findings reveal that, beyond indicating turn-taking, read-receipts have been leveraged as a strategic tool for various purposes in interpersonal relations.