Reviewerly/exHarmony huggingface dataset:
CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos

Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we consider a specific instance of such queries: queries that contain typos. We show that a small character level perturbation in queries (as caused by typos) highly impacts the effectiveness of dense retrievers. We then demonstrate that the root cause of this resides in the input tokenization strategy employed by BERT. In BERT, tokenization is performed using the BERT's WordPiece tokenizer and we show that a token with a typo will significantly change the token distributions obtained after tokenization. This distribution change translates to changes in the input embeddings passed to the BERT-based query encoder of dense retrievers. We then turn our attention to devising dense retriever methods that are robust to such queries with typos, while still being as performant as previous methods on queries without typos. For this, we use CharacterBERT as the backbone encoder and an efficient yet effective training method, called Self-Teaching (ST), that distills knowledge from queries without typos into the queries with typos. Experimental results show that CharacterBERT in combination with ST achieves significantly higher effectiveness on queries with typos compared to previous methods. Along with these results and the open-sourced implementation of the methods, we also provide a new passage retrieval dataset consisting of real-world queries with typos and associated relevance assessments on the MS MARCO corpus, thus supporting the research community in the investigation of effective and robust dense retrievers. Code, experimental results and dataset are made available at https://github.com/ielab/CharacterBERT-DR.
Exploring Uncertainty in Digital Scheduling, and The Wider Implications of Unrepresented Temporalities in HCI

Digital calendars and other technologies for social event planning leave little space to communicate uncertainty regarding time, place or the ability to attend an event. However, narratives of certainty can be detrimental and lead to the marginalisation of those who find it hard to cope with rigid and strictly paced schedules, such as people with health conditions or caring responsibilities. In this paper, we explore uncertainty as the starting point and leading principle behind digital scheduling tools. We present Haze, a speculative tool and user interface, designed to gain insights on participants' perceptions of uncertainty-based scheduling scenarios. We report on two qualitative studies (total of 21 participants), which indicate that a change in perspective towards uncertainty can challenge moral assumptions around certainty, increase temporal empathy, and indeed support those who are particularly affected by uncertainty. These findings help shift and expand the repertoire of temporality and discuss moral and social responsibilities for design and HCI.
Patch-O: Deformable Woven Patches for On-body Actuation

We present Patch-O, a novel deformable interface devised as a woven patch that enables diverse movement-based interactions adaptive to garments or on-skin wearing. Patch-O interfaces are uniquely detachable and relocatable soft actuation units that can be sewn or attached to clothing or skin at various locations. To optimize the morphing effect while preserving a slim form factor, we introduce a construction approach that integrates actuators at a structural level and varies the texture and stiffness of the woven substrate locally. We implement three basic actuation primitives, including bending, expanding, and shrinking, and experiment with aggregation parameters to exhaustively extend the design space. A final workshop study inviting textile practitioners to create personalized designs of Patch-O provides insights into the expressiveness of the approach for wearable interactions. We conclude with three applications inspired by users’ designs and showcase the aesthetic and functional usages enabled by the deformable woven patches.
The Eco-Technical Interface: Attuning to the Instrumental

Given the ongoing environmental crisis and recent calls within HCI to engage with its cascading effects on the more-than-human world, this paper introduces the concept of the eco-technical interface as a critical zone at which designers can surface and subvert issues of multispecies relations such as nonhuman instrumentalization. The eco-technical interface represents the sites at which human, nonhuman, and technological interfaces overlap, ranging from remote sensing for conservation to smart devices for precision agriculture to community science platforms for species identification. Here, we highlight the pervasiveness of the eco-technical interface as a set of sites for further HCI inquiry, engage with the politics and instrumentalizing tendencies at three particular sites, and demonstrate tactics for cultivating attunement to, reflexively accounting for, and subverting instrumentalization in multispecies encounter.
“Should I visit the clinic”: Analyzing WhatsApp-mediated Online Health Support for Expectant and New Mothers in Rural India

Limited interaction with professionals, infrastructural, and social constraints put barriers in providing holistic support to expectant and new mothers in low-resource settings. We examine the use of digital support groups facilitated through WhatsApp by a non-government organization in India. Complementing prior research, these digital peer support groups inform about an open public space created over a chat platform where rural communities and health professionals can engage. By qualitatively analyzing six months of interaction among 588 group members and collecting the experiences of the group moderators, we inform about how the support groups acted as an important source for compensating the gaps in the existing healthcare, providing reassurance support on routine health, explanation on test reports, validation and counseling support in ongoing treatments. We also derive implications for the future of digital support groups and the need for further research on the use of unplatformed design models in resource-constrained settings.
MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios

Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the feature extraction network is replaced by introducing the MobileNetv2 network to reduce the number of model parameters; then, part of the standard convolution is replaced by depthwise separable convolution in PAnet and the head network to further reduce the number of model parameters. Finally, by introducing an improved lightweight channel attention modul-Efficient Channel Attention (ECA)-to improve the feature expression ability during feature fusion. The Single-Stage Headless (SSH) context module is introduced to the small object detection branch to increase the receptive field. The experimental results show that the improved algorithm has an accuracy rate of 90.7% on the KITTI data set. Compared with YOLOv4, the parameters of the proposed MobileYOLO model are reduced by 52.11 M, the model size is reduced to one-fifth, and the detection speed is increased by 70%.
On greedy randomized average block Kaczmarz method for solving large linear systems

Inspired by the greedy randomized Kaczmarz method, we propose a probability criterion which can capture subvectors of the residual whose norms are relatively large. According to this probability criterion we select a submatrix randomly from the coefficient matrix, then average the projections of the current iteration vector onto each individual row of this chosen submatrix, constructing the greedy randomized average block Kaczmarz method for solving the consistent system of linear equations, which can be implemented in a distributed environment. When the size of each block is one, the probability criterion in the greedy randomized average block Kaczmarz method is a generalization of that in the greedy randomized Kaczmarz method. The greedy randomized Kaczmarz method is also a special case of the greedy randomized average block Kaczmarz method. Two kinds of extrapolated stepsizes for the greedy randomized average block Kaczmarz method are analyzed. The experimental results show the advantage of the greedy randomized average block Kaczmarz method over the greedy randomized Kaczmarz method and several existing randomized block Kaczmarz methods.
Overcoming Legacy Bias: Re-Designing Gesture Interactions in Virtual Reality With a San Community in Namibia

Recent improvements in hand-tracking technologies support novel applications and developments of gesture interactions in virtual reality (VR). Current implementations are mostly convention-based, originating in a Western technological context, thereby creating a legacy bias in gesture interaction implementations. With expanding application contexts and growing user groups and contexts, the design and selection of gestures need to be diversified. In this paper we present an exploration of natural gestures, followed by their implementation in a VR application and co-design of new gestures with a marginalized San community in Namibia. This study contributes to the still scarce empirical work in user-driven gesture design research, aiming to reduce legacy bias, on a methodological and technical level as well as through engaging non-WEIRD participants. Our findings confirm the applicability of our method, combined with Partner and Priming suggested by Morris et al., to the design of gestures inspired by natural interactions. We also consider the implementation of user-designed gestures to be necessary to asses usability, usefulness and technical issues in VR. Furthermore, the research directly advances the HCI agenda for diversity, through an ongoing research and design partnership with an indigenous community in Southern Africa, thereby challenging systemic bias and promoting design for the pluriverse.
User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering

Three of the most common approaches used in recommender systems are content-based filtering (matching users' preferences with products' characteristics), collaborative filtering (matching users with similar preferences), and demographic filtering (catering to users based on demographic characteristics). Do users' intuitions lead them to trust one of these approaches over others, independent of the actual operations of these different systems? Does their faith in one type or another depend on the quality of the recommendation, rather than how the recommendation appears to have been derived? We conducted an empirical study with a prototype of a movie recommender system to find out. A 3 (Ostensible Recommender Type: Content vs. Collaborative vs. Demographic Filtering) x 2 (Recommendation Quality: Good vs. Bad) experiment (N=226) investigated how users evaluate systems and attribute responsibility for the recommendations they receive. We found that users trust systems that use collaborative filtering more, regardless of the system's performance. They think that they themselves are responsible for good recommendations but that the system is responsible for bad recommendations (reflecting a self-serving bias). Theoretical insights, design implications and practical solutions for the cold start problem are discussed.
Spatial Negative Co-Location Pattern Directional Mining Algorithm with Join-Based Prevalence

It is usually difficult for prevalent negative co-location patterns to be mined and calculated. This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. Firstly, this paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases. Secondly, using this property, it is deduced that any prevalent negative co-location pattern with size n can be generated by connecting prevalent co-location with size 2 and with an n − 1 size candidate negative co-location pattern or an n − 1 size prevalent positive co-location pattern. Finally, the experiment results demonstrate that while other conditions are fixed, the proposed algorithm has an excellent efficiency level. The algorithm can eliminate the 90% useless negative co-location pattern maximumly and eliminate the useless 40% negative co-location pattern averagely.
Analysis of Bitcoin’s price spike in context of Elon Musk’s Twitter activity

Purpose The purpose of this paper is to explore the effect of Elon Musk’s Twitter bio change on January 29, 2021 on the discourse around Bitcoin (BTC) on Twitter and to understand how these changes relate to the changes in Bitcoin price around that time. Design/methodology/approach This study implements sentiment analysis and text mining on Twitter data to explore changes in public sentiments toward Bitcoin after Elon Musk’s Twitter bio change. Furthermore, it uses Bitcoin price data obtained from the Binance exchange to understand its relation with Twitter discussion. Findings Elon Musk’s bio change on Twitter on January 29 increased the tweet volume mentioning Bitcoin. This increase in tweets had a strong positive correlation with Bitcoin price and preceded the rise in Bitcoin price. Although the bio change had an apparent effect on the tweet volume, there was no considerable effect on the tweet sentiments, indicating that tweet sentiment is a poor predictor of Bitcoin price. Originality/value This paper proposes an understanding of how social media influencers, like Elon Musk, affect the discourse around Bitcoin and can, in turn, have an impact on Bitcoin price.
Analysis on the Selection of the Appropriate Batch Size in CNN Neural Network

Batch Size is an essential hyper-parameter in deep learning. Different chosen batch sizes may lead to various testing and training accuracies and different runtimes. Choosing an optimal batch size is crucial when training a neural network. The scientific purpose of this paper is to find an appropriate range of batch size people can use in a convolutional neural network. The study is conducted by changing the hyper-parameter batch size and observing the influences when training some commonly used convolutional neural networks (Mnist, Fashion Mnist and CIFAR-10). The experiment results suggest it is more likely to obtain the most accurate model when choosing the mini-batch size between 16 and 64. In addition, the experiments discuss the effect of different sizes of datasets, neural network depth, and whether the batch size is a power of 2 on the conclusions. Therefore, when training a CNN model, people could first choose a batch size of 32 and decrease it for accuracy or increase it for efficiency.
Keep the VRhythm going

The acoustic and visual experiences of musicians in the spaces they perform in are complex and organic in nature, entailing a continuous interaction with the environment. With this project, we leverage the power of Virtual Reality (VR) to support musicians in their creative practice by transporting them to novel sonic and visual worlds. For this, we developed a musician-centred VR system, featuring various acoustic and visual virtual environments, VR Rehearse & Perform, based on design requirements gathered with musicians and performance experts. To investigate how VR can be designed to support music-makers in their creative musical practice, we performed iterative tests with 19 musicians followed by semi-structured interviews. Our findings suggest that VR has the potential to support different aspects of the creative musical practice, such as rehearsing, performing and improvising. Our research provides insights and inspirations toward designing musician-centred VR experiences for various musical activities.
FabricatINK: Personal Fabrication of Bespoke Displays Using Electronic Ink from Upcycled E Readers

Abstract: FabricatINK explores the personal fabrication of irregularly-shaped low-power displays using electronic ink (E ink). E ink is a programmable bicolour material used in traditional form-factors such as E readers. It has potential for more versatile use within the scope of personal fabrication of custom-shaped displays, and it has the promise to be the pre-eminent material choice for this purpose. We appraise technical literature to identify properties of E ink, suited to fabrication. We identify a key roadblock, universal access to E ink as a material, and we deliver a method to circumvent this by upcycling broken electronics. We subsequently present a novel fabrication method for irregularly-shaped E ink displays. We demonstrate our fabrication process and E ink's versatility through ten prototypes showing different applications and use cases. By addressing E ink as a material for display fabrication, we uncover the potential for users to create custom-shaped truly bistable displays.
GPU-accelerated scalable solver with bit permutated cyclic-min algorithm for quadratic unconstrained binary optimization

A wide range of combinatorial optimization problems can be reduced to the Ising model, and equivalently the quadratic unconstrained binary optimization (QUBO) problem. Thus, in recent years, researchers have proposed to solve QUBO on FPGAs, GPUs, and special-purpose processors. The adaptive bulk search (ABS) is a previously-proposed framework for solving QUBO in parallel on multiple GPUs. In the ABS, a CPU host performs a GA-based global search while GPUs asynchronously perform many local searches in parallel. An original ABS adopts a simple local search algorithm called a cyclic-min algorithm, which does not use pseudo random numbers. However, the lack of randomness may cause a potential drawback of restricted bit-flipping operations in a local search. To avoid this drawback, this paper proposes a cyclic-min algorithm with randomly-generated multiple bit permutations, which enables a more effective local search with random number generation in CPUs (not in GPUs). Furthermore, this paper introduces a scalable implementation of the ABS with MPI and OpenMP. Our experimental results on TSUBAME3.0 show that the solution quality improves and the throughput linearly increases as the number of GPUs increases; with 256 GPUs, it evaluates 20.1×1012 solutions per second.
Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM

Aiming at the problems of over reliance on labor and low generalization of traditional emotion analysis methods based on dictionary and machine learning, an emotion analysis model of microblog comment text based on deep learning is proposed. Firstly, text is obtained through microblog crawler program. After data preprocessing, including data cleaning, Chinese word segmentation, removal of stop words, and so on, the Skip-gram model is used for word vector training on a large-scale unmarked corpus, and then the trained word vector is used as the text input of CNN-BiLSTM model, which combines Bidirectional Long-Short Term Memory (BiLSTM) neural network and Convolution Neural Network (CNN). Considering the historical context information and the subsequent context information, BiLSTM can better use the temporal relationship of text to learn sentence semantics. CNN can extract hidden features from the text and combine them. Finally, after Adamax optimization training, the emotion type of microblog comment text is output. The proposed model combines the learning advantages of BiLSTM and CNN. The overall accuracy of text emotion analysis has been greatly improved, with an accuracy of 0.94 and an improvement of 8.51% compared with the single CNN model.
AvatAR: An Immersive Analysis Environment for Human Motion Data Combining Interactive 3D Avatars and Trajectories

Analysis of human motion data can reveal valuable insights about the utilization of space and interaction of humans with their environment. To support this, we present AvatAR, an immersive analysis environment for the in-situ visualization of human motion data, that combines 3D trajectories with virtual avatars showing people's detailed movement and posture. Additionally, we describe how visualizations can be embedded directly into the environment, showing what a person looked at or what surfaces they touched, and how the avatar's body parts can be used to access and manipulate those visualizations. AvatAR combines an AR HMD with a tablet to provide both mid-air and touch interaction for system control, as well as an additional overview device to help users navigate the environment. We implemented a prototype and present several scenarios to show that AvatAR can enhance the analysis of human motion data by making data not only explorable, but experienceable.
Unmaking@CHI: Concretizing the Material and Epistemological Practices of Unmaking in HCI

Design is conventionally considered to be about making and creating new things. But what about the converse of that process – unmaking that which already exists? Researchers and designers have recently started to explore the concept of "unmaking" to actively think about important design issues like reuse, repair, and unintended socio-ecological impacts. They have also observed the importance of unmaking as a ubiquitous process in the world, and its relation to making in an ongoing dialectic that continually recreates our material and technological realms. Despite the increasing attention to unmaking, it remains largely under-investigated and under-theorized in HCI. The objectives of this workshop are therefore to (a) bring together a community of researchers and practitioners who are interested in exploring or showcasing the affordances of unmaking, (b) articulate the material and epistemological scopes of unmaking within HCI, and (c) reflect on frameworks, research approaches, and technical infrastructure for unmaking in HCI that can support its wider application in the field.
Varv: Reprogrammable Interactive Software as a Declarative Data Structure

Most modern applications are immutable and turn-key despite the acknowledged benefits of empowering users to modify their software. Writing extensible software remains challenging, even for expert programmers. Reprogramming or extending existing software is often laborious or wholly blocked, requiring sophisticated knowledge of application architecture or setting up a development environment. We present Varv, a programming model representing reprogrammable interactive software as a declarative data structure. Varv defines interactive applications as a set of concepts that consist of a schema and actions. Applications in Varv support incremental modification, allowing users to reprogram through addition and selectively suppress, modify, or add behavior. Users can define high-level concepts, creating an abstraction layer and effectively a domain-specific language for their application domain, emphasizing reuse and modification. We demonstrate the reprogramming and collaboration capabilities of Varv in two case studies and illustrate how the event engine allows for extensive tooling support.
Self-Determination Theory in HCI: Shaping a Research Agenda

Self-determination theory (SDT) has become one of the most frequently used and well-validated theories used in HCI research, modelling the relation of basic psychological needs, intrinsic motivation, positive experience and wellbeing. This makes it a prime candidate for a 'motor theme' driving more integrated, systematic, theory-guided research. However, its use in HCI has remained superficial and disjointed across various application domains like games, health and wellbeing, or learning. This workshop therefore convenes researchers across HCI to co-create a research agenda on how SDT-informed HCI research can maximise its progress in the coming years.