Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Nuclei Segmentation Via Instance Learning

Weakly supervised nuclei segmentation is a critical problem for pathological image analysis and greatly benefits the community due to the significant reduction of labeling cost. Adopting point annotations, previous methods mostly rely on less expressive representations for nuclei instances and thus have difficulty in handling crowded nuclei. In this paper, we propose to decouple weakly supervised semantic and instance segmentation in order to enable more effective subtask learning and to promote instance-aware representation learning. To achieve this, we design a modular deep network with two branches: a semantic proposal network and an instance encoding network, which are trained in a two-stage manner with an instance-sensitive loss. Empirical results show that our approach achieves the state-of-the-art performance on two public benchmarks of pathological images from different types of organs. Our code is available at https://github.com/weizhenFrank/WeakNucleiSeg.
View-Disentangled Transformer for Brain Lesion Detection

Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to balance between the granularity of 3D context aggregation and the computational complexity. In this paper, we propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view, which approximately achieves the 3D correlation computing in an efficient way. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions. The experimental results show that our proposed view-disentangled transformer performs well for brain lesion detection on a challenging brain MRI dataset.
How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes

Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, which reduces the inter-annotator agreement. This causes noise for the classifier and might affect the user's perception of the classifier's performance. In our research, we investigated whether the classification difficulty of a data point influences how strongly a prediction mistake reduces the "perceived accuracy". In an experimental online study, 225 participants interacted with three fictive classifiers with equal accuracy (73%). The classifiers made prediction mistakes on three different types of data points (easy, difficult, impossible). After the interaction, participants judged the classifier's accuracy. We found that not all prediction mistakes reduced the perceived accuracy equally. Furthermore, the perceived accuracy differed significantly from the calculated accuracy. To conclude, accuracy and related measures seem unsuitable to represent how users perceive the performance of classifiers.
Haptic Fidelity Framework: Defining the Factors of Realistic Haptic Feedback for Virtual Reality

Providing haptic feedback in virtual reality to make the experience more realistic has become a strong focus of research in recent years. The resulting haptic feedback systems differ greatly in their technologies, feedback possibilities, and overall realism making it challenging to compare different systems. We propose the Haptic Fidelity Framework providing the means to describe, understand and compare haptic feedback systems. The framework locates a system in the spectrum of providing realistic or abstract haptic feedback using the Haptic Fidelity dimension. It comprises 14 criteria that either describe foundational or limiting factors. A second Versatility dimension captures the current trade-off between highly realistic but application-specific and more abstract but widely applicable feedback. To validate the framework, we compared the Haptic Fidelity score to the perceived feedback realism of evaluations from 38 papers and found a strong correlation suggesting the framework accurately describes the realism of haptic feedback.
Fault-tolerant SINS/HSB/DVL underwater integrated navigation system based on variational Bayesian robust adaptive Kalman filter and adaptive information sharing factor

To solve the problem that position information obtained by SINS/DVL integrated navigation system is divergent in the underwater navigation applications, this paper introduces hydroacoustic single beacon (HSB) into the underwater integrated system and proposes a novel SINS/HSB/DVL fault-tolerant federated variational Bayesian robust adaptive Kalman filter (FVBRAKF). In the FVBRAKF, the traditional KF is replaced by VBRAKF, which not only suppress the adverse influence of outliers based on Mahalanobis distance (MD) effectively, but also estimate the unknown measurement noise covariance based on variational Bayesian (VB) approximation adaptively. Meanwhile, a novel adaptive information sharing factor (ISF) method is proposed during the information fusion process to form an improved FVBRAKF（IFVBRAKF）, which can adjust the fusion weights in real-time according to the accuracy of local filter. The semi-physical simulation experiments for SINS/HSB/DVL underwater integrated navigation system based on the test data are carried out to verify the adaptive ability of the ISF and the robust adaptive ability of the method, respectively. Experimental results demonstrate that the proposed algorithm can not only improve the estimation accuracy, but also enhance the fault-tolerant performance.
Yo–Yo Machines

Yo–Yo Machines are playful communication devices designed to help people feel socially connected while physically separated. We designed them to reach as many people as possible, both to make a positive impact during the COVID-19 pandemic and to assess a self-build approach to circulating research products and the appeal of peripheral and expressive communication devices. A portfolio of four distinct designs, based on over 30 years of research, were made available for people to make by following simple online instructions (yoyomachines.io). Each involves connecting a pair of identical devices over the internet to allow simple communication at a distance. This paper describes our motivation for the project, previous work in the area, the design of the devices, supporting website and publicity, and how users have made and used Yo-Yo Machines. Finally, we reflect on what we learned about peripheral and expressive communication devices and implications for the self-build approach.
DSTUNet: UNet with Efficient Dense SWIN Transformer Pathway for Medical Image Segmentation

Automatic medical image segmentation has achieved impressive results with the development of Deep Learning. However, although convolutional neural network, especially the U-shape network, has shown the superiority of method in many segmentation tasks, it can not model long-range dependency well and will be limited by the information recession due to the downsampling operation. Some recent Transformer-based works only used multi-head self attention mechanism in the main autoencoder architecture to enhance the long-range dependency on the single scale, and it failed to compensate for the information loss. In this paper, we propose a novel UNet with densely connected Swin Transformer blocks as efficient skip pathway, namely DSTUNet, for medical image segmentation. Specifically, each Dense Swin Transformer Block is composed of several Swin Transformer layers to make better use of the shift-window self attention mechanism at different scales to enhance the multi-scale long-range dependency. Moreover, the dense connection among Swin Transformer layers is introduced to boost the flow of feature information and minimize the information recession. Experiments have been conducted on multi-organ and cardiac segmentation tasks, and the results demonstrate that our method is able to achieve superior segmentation compared to the existing state-of-the-art approaches.
TunePad Playbooks

This paper describes the design of an online learning platform that empowers musical creation and performance with Python code. For this platform we have developed an innovative computational notebook paradigm that we call TunePad playbooks. While playbooks borrow ideas from popular computational notebooks like Jupyter, we have designed them from the ground up to support creative musical expression including live performances. After discussing our design principles and features, we share findings from a series of artifact-centered interviews conducted with experienced TunePad users. Our results show how systems like ours might flexibly support a variety of creative workflows, while suggesting opportunities for future work in this area.
A Speculative Ethics for Designing with Bodily Fluids

This essay performs a speculative ethics in designing with a researcher's own bodily fluids. This is through the creation of "performative texts", which are autoethnographic accounts of past experiences in which written words perform through visual and spatial compositions alongside verbal readings aloud. I present three performative texts about moments of discomfort in designing with milk from my own breastfeeding relationship. They are to reflect upon felt experiences of potential harm and to understand social and material relations of care. From these I offer three possibilities for how HCI might consider the ethics of first-person research in attending to more-than-human entanglements: unsafe spaces, situated escapes, and censored inclusion. These possibilities and the approach of performative texts contribute to research for more sustainable futures by exploring the decentering of humans through an intimate engagement with the self.
Semisupervised Graph Neural Networks for Graph Classification

Graph classification aims to predict the label associated with a graph and is an important graph analytic task with widespread applications. Recently, graph neural networks (GNNs) have achieved state-of-the-art results on purely supervised graph classification by virtue of the powerful representation ability of neural networks. However, almost all of them ignore the fact that graph classification usually lacks reasonably sufficient labeled data in practical scenarios due to the inherent labeling difficulty caused by the high complexity of graph data. The existing semisupervised GNNs typically focus on the task of node classification and are incapable to deal with graph classification. To tackle the challenging but practically useful scenario, we propose a novel and general semisupervised GNN framework for graph classification, which takes full advantage of a slight amount of labeled graphs and abundant unlabeled graph data. In our framework, we train two GNNs as complementary views for collaboratively learning high-quality classifiers using both labeled and unlabeled graphs. To further exploit the view itself, we constantly select pseudo-labeled graph examples with high confidence from its own view for enlarging the labeled graph dataset and enhancing predictions on graphs. Furthermore, the proposed framework is investigated on two specific implementation regimes with a few labeled graphs and the extremely few labeled graphs, respectively. Extensive experimental results demonstrate the effectiveness of our proposed semisupervised GNN framework for graph classification on several benchmark datasets.
User Perceptions of Extraversion in Chatbots after Repeated Use

Whilst imbuing robots and voice assistants with personality has been found to positively impact user experience, little is known about user perceptions of personality in purely text-based chatbots. In a within-subjects study, we asked N=34 participants to interact with three chatbots with different levels of Extraversion (extraverted, average, introverted), each over the course of four days. We systematically varied the chatbots' responses to manipulate Extraversion based on work in the psycholinguistics of human behaviour. Our results show that participants perceived the extraverted and average chatbots as such, whereas verbal cues transferred from human behaviour were insufficient to create an introverted chatbot. Whilst most participants preferred interacting with the extraverted chatbot, participants engaged significantly more with the introverted chatbot as indicated by the users' average number of written words. We discuss implications for researchers and practitioners on how to design chatbot personalities that can adapt to user preferences.
How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior

Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells.In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expression from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological features in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expression. Interestingly, we found the genes with higher fold change can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attention scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSIs.
Few-shot Website Fingerprinting attack with Meta-Bias Learning

Website fingerprinting (WF) attack aims to identify which website a user is visiting from the traffic data patterns. Whilst existing methods assume many training samples, we investigate a more realistic and scalable few-shot WF attack with only a few labeled training samples per website. To solve this problem, we introduce a novel Meta-Bias Learning (MBL) method for few-shot WF learning. Taking the meta-learning strategy, MBL simulates and optimizes the target tasks. Moreover, a new model parameter factorization idea is introduced for facilitating meta-training with superior task adaptation. Expensive experiments show that our MBL outperforms significantly existing hand-crafted feature and deep learning based alternatives in both closed-world and open-world attack scenarios, at the absence and presence of defense.
Adversarial attacks and defenses in Speaker Recognition Systems: A survey

Speaker recognition has become very popular in many application scenarios, such as smart homes and smart assistants, due to ease of use for remote control and economic-friendly features. The rapid development of SRSs is inseparable from the advancement of machine learning, especially neural networks. However, previous work has shown that machine learning models are vulnerable to adversarial attacks in the image domain, which inspired researchers to explore adversarial attacks and defenses in Speaker Recognition Systems (SRS). Unfortunately, existing literature lacks a thorough review of this topic. In this paper, we fill this gap by performing a comprehensive survey on adversarial attacks and defenses in SRSs. We first introduce the basics of SRSs and concepts related to adversarial attacks. Then, we propose two sets of criteria to evaluate the performance of attack methods and defense methods in SRSs, respectively. After that, we provide taxonomies of existing attack methods and defense methods, and further review them by employing our proposed criteria. Finally, based on our review, we find some open issues and further specify a number of future directions to motivate the research of SRSs security.
Representation learning with deep sparse auto-encoder for multi-task learning

We demonstrate an effective framework to achieve a better performance based on Deep Sparse auto-encoder for Multi-task Learning, called DSML for short. To learn the reconstructed and higher-level features on cross-domain instances for multiple tasks, we combine the labeled and unlabeled data from all tasks to reconstruct the feature representations. Furthermore, we propose the model of Stacked Reconstruction Independence Component Analysis (SRICA for short) for the optimization of feature representations with a large amount of unlabeled data, which can effectively address the redundancy of image data. Our proposed SRICA model is developed from RICA and is based on deep sparse auto-encoder. In addition, we adopt a Semi-Supervised Learning method (SSL for short) based on model parameter regularization to build a unified model for multi-task learning. There are several advantages in our proposed framework as follows: 1) The proposed SRICA makes full use of a large amount of unlabeled data from all tasks. It is used to pursue an optimal sparsity feature representation, which can overcome the over-fitting problem effectively. 2) The deep architecture used in our SRICA model is applied for higher-level and better representation learning, which is designed to train on patches for sphering the input data. 3) Training parameters in our proposed framework has lower computational cost compared to other common deep learning methods such as stacked denoising auto-encoders. Extensive experiments on several real image datasets demonstrate our proposed framework outperforms the state-of-the-art methods.
OVRlap: Perceiving Multiple Locations Simultaneously to Improve Interaction in VR

We introduce OVRlap, a VR interaction technique that lets the user perceive multiple places at the same time from a first-person perspective. OVRlap achieves this by overlapping viewpoints. At any time, only one viewpoint is active, meaning that the user may interact with objects therein. Objects seen from the active viewpoint are opaque, whereas objects seen from passive viewpoints are transparent. This allows users to perceive multiple locations at once and easily switch to the one in which they want to interact. We compare OVRlap and a single-viewpoint technique in a study where 20 participants complete object-collection and monitoring tasks. We find that in both tasks, participants are significantly faster and move their head significantly less with OVRlap. We propose how the technique might be improved through automated switching of the active viewpoint and intelligent viewpoint rendering.
ImpactVest: Rendering Spatio-Temporal Multilevel Impact Force Feedback on Body in VR

Rendering instant and intense impact feedback on users’ hands, limbs and head to enhance realism in virtual reality (VR) has been proposed in previous works, but impact on the body is still less discussed. With the body’s large surface area to utilize, numerous impact patterns can be rendered in versatile VR applications, e.g., being shot, blasted, punched or slashed on body in VR games. Herein we propose ImpactVest to render spatio-temporal multilevel impact force feedback on body. By independently controlling nine impactors in a 3 × 3 layout using elastic force, impact is generated at different levels, positions and time sequences for versatile spatial and temporal combinations. We conducted a just-noticeable difference (JND) study to understand users’ impact level distinguishability on the body. A time interval threshold study was then performed to ascertain what time interval thresholds between two impact stimuli should be used to distinguish from simultaneous impact, a continuous impact stroke and two discrete impact stimuli. Based on the results, we conducted a VR experience study to verify that impact feedback from ImpactVest enhances VR realism.
Integrating Religion, Faith, and Spirituality in HCI

HCI scholarship has not yet fully engaged with faith, religion, and spirituality, even though billions of people around the world are associated with such traditions and belief systems. While a few papers and workshops at CHI have focused on particular religions, broader discussions around religion, interfaith relationships, and computing have been absent from mainstream HCI design concerns. In this workshop, we propose to bring together HCI scholars and practitioners, whose work is associated with various faiths, religions, and spiritual practices to start this important conversation, with a focus on three questions: (a) does secularization in computing marginalize faith-based values? and if so, how? (b) how can HCI design address the unique needs and values of faith-based communities? and (c) how can scholarship and practice in HCI benefit from the integration of faith, religion, and spirituality? We hope to form an HCI community of scholars and practitioners focused on the intersection of faith/spirituality/religion and computing.
A Novel Approach for Semantic Extractive Text Summarization

Text summarization is a technique for shortening down or exacting a long text or document. It becomes critical when someone needs a quick and accurate summary of very long content. Manual text summarization can be expensive and time-consuming. While summarizing, some important content, such as information, concepts, and features of the document, can be lost; therefore, the retention ratio, which contains informative sentences, is lost, and if more information is added, then lengthy texts can be produced, increasing the compression ratio. Therefore, there is a tradeoff between two ratios (compression and retention). The model preserves or collects all the informative sentences by taking only the long sentences and removing the short sentences with less of a compression ratio. It tries to balance the retention ratio by avoiding text redundancies and also filters irrelevant information from the text by removing outliers. It generates sentences in chronological order as the sentences are mentioned in the original document. It also uses a heuristic approach for selecting the best cluster or group, which contains more meaningful sentences that are present in the topmost sentences of the summary. Our proposed model extractive summarizer overcomes these deficiencies and tries to balance between compression and retention ratios.