Reviewerly/exHarmony huggingface dataset:
CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer user preference from biased data with learning to debias, unfortunately, they mainly focus on the effect of one specific item attribute, e.g., position or modality which may affect users’ click probability on items. However, the comprehensive description for potential interactions between multiple items with various attributes, namely the context bias between items, may not be fully summarized. To that end, in this paper, we design a novel Context Bias aware Recommendation (CBR) model for describing and debiasing the context bias caused by comprehensive interactions between multiple items. Specifically, we first propose a content encoder and a bias encoder based on multi-head self-attention to embed the latent interactions between items. Then, we calculate the biased representation for users based on an attention network, which will be further utilized to infer the negative preference, i.e., the dislikes of users based on the items the user never clicked. Finally, the real user preference will be captured based on the negative preference to estimate the click prediction score. Extensive experiments on a real-world dataset demonstrate the competitiveness of our CBR framework compared with state-of-the-art baseline methods.
Influencing the security prioritisation of an agile software development project

Software security is a complex topic, and for development projects it can be challenging to assess what security is necessary and cost-effective. Agile Software Development (ASD) values self-management. Thus, teams and their Product Owners are expected to also manage software security prioritisation. In this paper we build on the notion that security experts who want to influence the priority given to security in ASD need to do this through interactions and support for teams rather than prescribing certain activities or priorities. But to do this effectively, there is a need to understand what hinders and supports teams in prioritising security. Based on a longitudinal case study, this article offers insight into the strategy used by one security professional in an SME to influence the priority of security in software development projects in the company. The main result is a model of influences on security prioritisation that can assist in understanding what supports or hinders the prioritisation of security in ASD, thus providing recommendations for security professionals. Two alternative strategies are outlined for software security in ASD – prescribed and emerging – where we hypothesise that an emerging approach can be more relevant for SMEs doing ASD, and that this can impact how such companies should consider software security maturity.
Hybrid Loss-Constrained Lightweight Convolutional Neural Networks for Cervical Cell Classification

Artificial intelligence (AI) technologies have resulted in remarkable achievements and conferred massive benefits to computer-aided systems in medical imaging. However, the worldwide usage of AI-based automation-assisted cervical cancer screening systems is hindered by computational cost and resource limitations. Thus, a highly economical and efficient model with enhanced classification ability is much more desirable. This paper proposes a hybrid loss function with label smoothing to improve the distinguishing power of lightweight convolutional neural networks (CNNs) for cervical cell classification. The results strengthen our confidence in hybrid loss-constrained lightweight CNNs, which can achieve satisfactory accuracy with much lower computational cost for the SIPakMeD dataset. In particular, ShufflenetV2 obtained a comparable classification result (96.18% in accuracy, 96.30% in precision, 96.23% in recall, and 99.08% in specificity) with only one-seventh of the memory usage, one-sixth of the number of parameters, and one-fiftieth of total flops compared with Densenet-121 (96.79% in accuracy). GhostNet achieved an improved classification result (96.39% accuracy, 96.42% precision, 96.39% recall, and 99.09% specificity) with one-half of the memory usage, one-quarter of the number of parameters, and one-fiftieth of total flops compared with Densenet-121 (96.79% in accuracy). The proposed lightweight CNNs are likely to lead to an easily-applicable and cost-efficient automation-assisted system for cervical cancer diagnosis and prevention.
Exploiting cross‐session information for knowledge‐aware session‐based recommendation via graph attention networks

Session-based recommendation (SBR) aims to predict the next item based on anonymous behavior session, which has become increasingly essential in various online services. Prior efforts mainly focus on modeling user preference based on the current session. Although some of them have been proven effective, they fail to address two main challenges in SBR. First, SBR suffers more from the problem of data sparsity due to the very limited user–item interactions, and hence it cannot sufficiently capture complicated item dependency relationships. Second, most of the user-item interaction sequences may be with noisy preference signals due to the uncertainty of user's behaviors, and it is difficult to distill high-quality item for recommendation. In this study, we propose a novel SBR model that exploits Cross-session information for Knowledge-aware Session-based Recommendation (CKSR) to address these two issues. Specifically, cross-session graph and knowledge graph are combined to model a cross-session knowledge graph, based on which a knowledge-aware attention mechanism is performed to capture the complicated transition pattern among interacted items. Each session is then represented as the composition of the global preference and the current interest of that session. Moreover, we leverage the similar sessions for the target session to establish a similar session referral circle and apply an influence coupler to judge the significance of different session referrals. An attentive network is designed to distill session preferences from its unique session referral circle. It dynamically extracts high-quality item from noisy session. Experiments on two benchmark data sets demonstrate that CKSR outperforms the state-of-the-art methods consistently.
Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling

It is a challenging task to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among existing black-box attacks, transfer-based methods tend to overfit the substitute model on parameter settings. Decision-based methods have low query efficiency due to fixed sampling and greedy search strategy. To alleviate the above problems, we present a new framework for query-efficient black-box adversarial attack by bridging transfer-based and decision-based attacks. We reveal the relationship between current noise and variance of sampling, the monotonicity of noise compression, and the influence of transition function on the decision-based attack. Guided by the new framework, we propose a black-box adversarial attack named Customized Iteration and Sampling Attack (CISA). CISA estimates the distance from nearby decision boundary to set the stepsize, and uses a dual-direction iterative trajectory to find the intermediate adversarial example. Based on the intermediate adversarial example, CISA conducts customized sampling according to the noise sensitivity of each pixel to further compress noise, and relaxes the state transition function to achieve higher query efficiency. Extensive experiments demonstrate CISA's advantage in query efficiency of black-box adversarial attacks.
Deep CSI Compression for Massive MIMO: A Self-Information Model-Driven Neural Network

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.
The TAC Toolkit: Supporting Design for User Acceptance of Health Technologies from a Macro-Temporal Perspective

User acceptance is key for the successful uptake and use of health technologies, but also impacted by numerous factors not always easily accessible nor operationalised by designers in practice. This work seeks to facilitate the application of acceptance theory in design practice through the Technology Acceptance (TAC) toolkit: a novel theory-based design tool and method comprising 16 cards, 3 personas, 3 scenarios, a virtual think-space, and a website, which we evaluated through workshops conducted with 21 designers of health technologies. Findings showed that the toolkit revised and extended designers' knowledge of technology acceptance, fostered their appreciation, empathy and ethical values while designing for acceptance, and contributed towards shaping their future design practice. We discuss implications for considering user acceptance a dynamic, multi-stage process in design practice, and better supporting designers in imagining distant acceptance challenges. Finally, we examine the generative value of the TAC toolkit and its possible future evolution.
Predicting Determinants of Lifelong Learning Intention Using Gradient Boosting Machine (GBM) with Grid Search

The purpose of this study is to explore the factors that have the most decisive influence on actual learning intention that leads to participation in adult education. For developing the predictive model, we used tree-based machine learning, with the longitudinal big data (2017~2020) of Korean adults. Based on the gradient boosting machine (GBM) results, among the eleven variables used, the most influential variables in predicting the possibility of lifelong education participation were self-pay education expenses and then highest level of education completed. After the grid search, not only the importance of the two variables but also the overall figures including the false positive rate improved. In future studies, it will be possible to improve the performance of the machine learning model by adjusting the hyper-parameters that can be directly set by less computational methods.
Unpacking Intention and Behavior: Explaining Contact Tracing App Adoption and Hesitancy in the United States

COVID-19 has demonstrated the importance of digital contact tracing apps in reducing the spread of disease. Despite people widely expressing interest in using contact tracing apps, actual installation rates have been low in many parts of the world. Prior studies suggest that decisions to use these apps are largely shaped by pandemic beliefs, social influences, perceived benefits and harms, and other factors. However, there is a gap in understanding what factors motivate intention, but not subsequent behavior of actual adoption. Reporting on a survey of 290 U.S. residents, we disentangle the intention-behavior gap by investigating factors associated with installing a contact tracing app from those associated with intending to install, but not actually installing. Our results suggest that social norms can be leveraged to span the intention-behavior gap, and that a privacy paradox may influence people's adoption decisions. We present recommendations for technologies that enlist individuals to address collective challenges.
Advit: Vision Transformer On Multi-Modality Pet Images For Alzheimer Disease Diagnosis

We present a new model trained on multi-modalities of Positron Emission Tomography images (PET-AV45 and PET-FDG) for Alzheimer’s Disease (AD) diagnosis. Unlike the conventional methods using multi-modal 3D/2D CNN architecture, our design replaces the Convolutional Neural Net-work (CNN) by Vision Transformer (ViT). Considering the high computation cost of 3D images, we firstly employ a 3D-to-2D operation to project the 3D PET images into 2D fusion images. Then, we forward the fused multi-modal 2D images to a parallel ViT model for feature extraction, followed by classification for AD diagnosis. For evaluation, we use PET images from ADNI. The proposed model outperforms several strong baseline models in our experiments and achieves 0.91 accuracy and 0.95 AUC.
Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

Breast cancer has now overtaken lung cancer as the world's most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909 images, and a breast cancer histopathology dataset, containing 3771 images of four classes, were used. The proposed method achieves a multi-class average accuracy of 94.20%, with a classification training time of 226 s in four classes of breast cancer, and a multi-class average accuracy of 90.10%, with a classification training time of 147 s in eight classes of breast cancer. The experimental outcomes show that the proposed method achieves the highest multi-class average accuracy for breast cancer classification, and hence, the proposed method can effectively be applied for early detection and classification of breast cancer to assist the pathologists in early and accurate diagnosis of breast cancer.
Vibing Together: Dance Experiences in Social Virtual Reality

Dancing is a universal human activity, and also a domain of enduring significance in Human-Computer Interaction (HCI) research. However, there has been limited investigation into how computing supports the experiences of recreational dancers. Concurrently, a diverse and sizeable dance community has been emerging in VRChat. Little is known about these dancers’ experiences, motivations, and practices. Yet shedding light into these could inform both VR technology development and the design of systems that better support embodied and complex social interactions. To bridge this gap, we interviewed participants active in the VRChat dance scene. Through thematic analysis, we identified six central facets of their experiences related to freedom, community, dance as an individual experience, dance as a shared experience, dance as a performance, and self-expression and -exploration. Based on these findings, we discuss emerging tensions and highlight beneficial impacts of dancing in VR as well as problems that still await resolving.
Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science

Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative research practices, we describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding, discussing, and disclosing the discretionary choices and subjectivity inherent to data science work. We first introduce the concepts of model positionality and computational reflexivity that can help data scientists to reflect on and communicate the social and cultural context of a model's development and use, the data annotators and their annotations, and the data scientists themselves. We then describe the unique challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions. Finally, we demonstrate these techniques in a case study of the development of classifiers for toxic commenting in online communities.
Canoe VR: An Immersive Exergame to Support Cognitive and Physical Exercises of Older Adults

Cognitive and physical exercises are important factors to support a healthy life, especially considering demographics change. Virtual reality (VR) exergames have great potential to support these activities in a more motivating way. However, regular usage of VR exergames by the older population is still limited. To address this issue, we designed and implemented a VR exergame: Canoe VR. We applied several prototyping sessions with older players and also report the results from interviews with physiotherapists. The results suggest that Canoe VR was very well received and can be used by older players as an additional fitness tool. We discuss the implications of extending a fitness routine with a VR exergame and using the game with players of different abilities.
Understanding and Designing Avatar Biosignal Visualizations for Social Virtual Reality Entertainment

Visualizing biosignals can be important for social Virtual Reality (VR), where avatar non-verbal cues are missing. While several biosignal representations exist, designing effective visualizations and understanding user perceptions within social VR entertainment remains unclear. We adopt a mixed-methods approach to design biosignals for social VR entertainment. Using survey (N=54), context-mapping (N=6), and co-design (N=6) methods, we derive four visualizations. We then ran a within-subjects study (N=32) in a virtual jazz-bar to investigate how heart rate (HR) and breathing rate (BR) visualizations, and signal rate, influence perceived avatar arousal, user distraction, and preferences. Findings show that skeuomorphic visualizations for both biosignals allow differentiable arousal inference; skeuomorphic and particles were least distracting for HR, whereas all were similarly distracting for BR; biosignal perceptions often depend on avatar relations, entertainment type, and emotion inference of avatars versus spaces. We contribute HR and BR visualizations, and considerations for designing social VR entertainment biosignal visualizations.
Much Realistic, Such Wow! A Systematic Literature Review of Realism in Digital Games

Researchers reference realism in digital games without sufficient specificity. Without clarity about the dimensions of realism, we cannot assess how and when to aim for a higher degree of realism, when lower realism suffices, or when purposeful unrealism is ideal for a game and can benefit player experience (PX). To address this conceptual gap, we conducted a systematic review using thematic synthesis to distinguish between types of realism currently found in the digital games literature. We contribute qualitative themes that showcase contradictory design goals of realism/unrealism. From these themes, we created a framework (i.e., a hierarchical taxonomy and mapping) of realism dimensions in digital games as a conceptual foundation. Our themes and framework enable a workable specificity for designing or analyzing types of realism, equip future work to explore effects of specific realism types on PX, and offer a starting point for similar efforts in non-game applications.
Ability is in the eye of the beholder: How context and individual factors shape consumer perceptions of digital assistant ability

As consumers and companies continue to invest in voice-based digital assistant technologies, a better understanding of how consumers evaluate these technologies is needed. Anecdotal evidence suggests that digital assistants often fall short of consumer expectations, with digital assistant ability playing a key role in consumers’ evaluation of these technologies. It is unclear, however, how contextual and individual factors shape digital assistant ability perceptions. To better understand consumer perceptions of digital assistant ability, we propose a novel measurement of the construct and then explore the use of digital assistants in the context of task automation versus augmentation. Across three studies and six samples, we find that (1) our new measure of digital assistant ability influences intended consumer outcomes and (2) ability assessments are dependent upon both the use context (i.e., automation versus augmentation; disclosure of automation) and individual characteristics (i.e., consumer mood state and consumer preference for human interaction). Theoretical and managerial implications of these findings are discussed.
Through a white lens: Black victimhood, visibility, and whiteness in the Black Lives Matter movement on TikTok

In this paper, we explore how highly visible users in the context of #BlackLivesMatter on TikTok shape the narrative around Black victims of police brutality, the understanding of these narratives by others, and the potential consequences of these portrayals for the movement at large. To examine these dimensions, we analysed the 100 most circulated TikTok videos and associated comments depicting victims of police brutality using the #BlackLivesMatter hashtag through multimodal critical discourse analysis. We identified how users attempted to increase visibility of their content, and how this was supported or criticised by commenters depending on the perceived motives of these efforts. Furthermore, we showcased how influencers raised awareness of the movement with little personal effort or risk, sometimes appearing to leverage the movement for self-exposure. Our analysis showed that many of the most liked videos were made by white content creators who, in their videos, seemed to be addressing an imagined white audience. While these efforts portrayed the movement favourably, the content creators remain outsiders who have not themselves been in harm's way of police brutality. While there were exceptions that promoted the perspectives of marginalised communities, and while the white narratives were consistently supportive of the movement, they also work to displace focus on racial (in)justice away from those directly affected by it, that is, away from Black people's own experiences of police brutality. We discuss these findings in relation to questions about digital representations of Black victimhood, digital visibility and practices of whiteness, on TikTok and beyond.