Reviewerly/exHarmony huggingface dataset:
Long-Term Observation on Browser Fingerprinting: Users’ Trackability and Perspective

Abstract Browser fingerprinting as a tracking technique to recognize users based on their browsers’ unique features or behavior has been known for more than a decade. We present the results of a 3-year online study on browser fingerprinting with more than 1,300 users. This is the first study with ground truth on user level, which allows the assessment of trackability based on fingerprints of multiple browsers and devices per user. Based on our longitudinal observations of 88,000 measurements with over 300 considered browser features, we optimized feature sets for mobile and desktop devices. Further, we conducted two user surveys to determine the representativeness of our user sample based on users’ demographics and technical background, and to learn how users perceive browser fingerprinting and how they protect themselves.
Enabling Data-Driven API Design with Community Usage Data

APIs are becoming the fundamental building block of modern software and their usability is crucial to programming efficiency and software quality. Yet API designers find it hard to gather and interpret user feedback on their APIs. To close the gap, we interviewed 23 API designers from 6 companies and 11 open-source projects to understand their practices and needs. The primary way of gathering user feedback is through bug reports and peer reviews, as formal usability testing is prohibitively expensive to conduct in practice. Participants expressed a strong desire to gather real-world use cases and understand users' mental models, but there was a lack of tool support for such needs. In particular, participants were curious about where users got stuck, their workarounds, common mistakes, and unanticipated corner cases. We highlight several opportunities to address those unmet needs, including developing new mechanisms that systematically elicit users' mental models, building mining frameworks that identify recurring patterns beyond shallow statistics about API usage, and exploring alternative design choices made in similar libraries.
Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

Abstract In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.
Artificial Intelligence

Based on the principle of dialectic symmetry formulated by the philosophy of dialectic positivism, an interpretation of the concept of "artificial intelligence" is proposed. This principle assumes the existence of a hierarchy of information objects, similar to the hierarchy, which reflects different levels of organization of matter (mechanical, chemical, biological, social). The construction of a hierarchy of information objects - information processing systems - allows us to interpret in the same way the essence of both artificial intelligence and the intelligence that a human being is endowed with. These are information processing systems referring to the highest levels of the specified hierarchy.
GoTree: A Grammar of Tree Visualizations

We present GoTree, a declarative grammar allowing users to instantiate tree visualizations by specifying three aspects: visual elements, layout, and coordinate system. Within the set of all possible tree visualization techniques, we identify a subset of techniques that are both "unit-decomposable" and "axis-decomposable" (terms we define). For tree visualizations within this subset, GoTree gives the user flexible and fine-grained control over the parameters of the techniques, supporting both explicit and implicit tree visualizations. We developed Tree Illustrator, an interactive authoring tool based on GoTree grammar. Tree Illustrator allows users to create a considerable number of tree visualizations, including not only existing techniques but also undiscovered and hybrid visualizations. We demonstrate the expressiveness and generative power of GoTree with a gallery of examples and conduct a qualitative study to validate the usability of Tree Illustrator.
The 'invisible' materiality of information technology

It's difficult to see the ecological impact of IT when its benefits are so blindingly bright.
Technical Architectures for Automotive Systems

Driven by software, the automotive domain is living an unprecedented revolution with original equipment manufacturers increasingly becoming software companies. Vehicle electrical and electronic software architectures are considered means for addressing several concerns, which span from safety to security, through electrification and autonomy. Such architectures serve also as pivotal means for enabling communication between an original equipment manufacturer and suppliers (tier 1 and 2 companies) within the automotive ecosystem. In the automotive domain, software architectures include (at least) three different views of descending abstraction: functional, logical, and technical. In this work, we focus on the technical view with a two-folded contribution. On the one hand, we propose a feature model of technical architectures for automotive systems. On the other hand, starting from the elicited feature model, we present three technical reference architectures able to guide three generations of automotive systems. We evaluate the contribution of this work by means of a focus group validation session and short semi-structured interviews with automotive experts and practitioners.
Menstrual (Im)Mobilities and Safe Spaces

In cultural contexts where menstruation is a stigmatized health topic, daily management of menstrual hygiene comes with its set of challenges. Our research aims to identify and examine such challenges faced during menstruation in the urban environs of Delhi, India. Through participatory design activities and interviews conducted with 35 participants who identified as menstruating and female, and a survey with 139 responses, we investigate how participants deal with their periods on the go. We also examine participants' conceptualizations of safe spaces, where they are able to deal with their period on their own terms. Finally, we discuss how menstrual mobilities are being, and might be, supported through technology-based interventions for a third space, targeting the legibility, literacy, and legitimacy of surrounding environments.
Telewalk: Towards Free and Endless Walking in Room-Scale Virtual Reality

Natural navigation in VR is challenging due to spatial limitations. While Teleportation enables navigation within very small physical spaces and without causing motion sickness symptoms, it may reduce the feeling of presence and spacial awareness. Redirected walking (RDW), in contrast, allows users to naturally walk while staying inside a finite, but still very large, physical space. We present Telewalk, a novel locomotion approach that combines curvature and translation gains known from RDW research in a perceivable way. This combination enables Telewalk to be applied even within a physical space of 3m x 3m. Utilizing the head rotation as input device enables directional changes without any physical turns to keep the user always on an optimal circular path inside the real world while freely walking inside the virtual one. In a user study we found that even though motion sickness susceptible participants reported respective symptoms, Telewalk did result in stronger feelings of presence and immersion and was seen as more natural then Teleportation.
Exploring The Future of Data-Driven Product Design

Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner.
Design Study "Lite" Methodology: Expediting Design Studies and Enabling the Synergy of Visualization Pedagogy and Social Good

Design studies are frequently used to conduct problem-driven visualization research by working with real-world domain experts. In visualization pedagogy, design studies are often introduced but rarely practiced due to their large time requirements. This limits students to a classroom curriculum, often involving projects that may not have implications beyond the classroom. Thus we present the Design Study "Lite" Methodology, a novel framework for implementing design studies with novice students in 14 weeks. We utilized the Design Study "Lite" Methodology in conjunction with Service-Learning to teach five Data Visualization courses and demonstrate that it benefits not only the students but also the community through service to non-profit partners. In this paper, we provide a detailed breakdown of the methodology and how Service-Learning can be incorporated with it. We also include an extensive reflection on the methodology and provide recommendations for future applications of the framework for teaching visualization courses and research.
Reflections from the Classroom and Beyond: Imagining a Decolonized HCI Education

As the field of Human-Computer Interaction (HCI) increasingly engages with matters of social change for the Global South, more students from this region — seeking to use HCI for impacting their countries - emigrate to HCI programs in the Global North. In turn, this challenges the assumed targeted audience, intentionality, and pedagogical approaches of traditional HCI educational structures. Drawing from Latin American decolonial thinking, we reflect on our experiences as Latin American students seeking a graduate education in the United States. From there, we discuss paths for HCI educators and students to engage with the co-creation of pluriversal learning spaces that resist universal notions of language, class content, and knowledge production. As we build a more inclusive research community, these discussions become critical for imagining an HCI education aware of its social and political dimensions.
Generating Audio-Visual Slideshows from Text Articles Using Word Concreteness

We present a system that automatically transforms text articles into audio-visual slideshows by leveraging the notion of word concreteness, which measures how strongly a word or phrase is related to some perceptible concept. In a formative study we learn that people not only prefer such audio-visual slideshows but find that the content is easier to understand compared to text articles or text articles augmented with images. We use word concreteness to select search terms and find images relevant to the text. Then, based on the distribution of concrete words and the grammatical structure of an article, we time-align selected images with audio narration obtained through text-to-speech to produce audio-visual slideshows. In a user evaluation we find that our concreteness-based algorithm selects images that are highly relevant to the text. The quality of our slideshows is comparable to slideshows produced manually using standard video editing tools, and people strongly prefer our slideshows to those generated using a simple keyword-search based approach.
PICOSS: Python Interface for the Classification of Seismic Signals

Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known classification used for initial validation of the machine outcome. Here, we present PICOSS (Python Interface for the Classification of Seismic Signals), a modular data-curator platform for volcano-seismic data analysis, including detection, segmentation and classification. PICOSS has exportability and standardization at its core; users can select automatic or manual workflows to select and label seismic data from a comprehensive suite of tools, including deep neural networks. The modular implementation of PICOSS includes a portable and intuitive graphical user interface to facilitate essential data labelling tasks for large-scale volcano seismic studies.
Be Water: Technologies in the Leaderless Anti-ELAB Movement in Hong Kong

We examine a leaderless social movement characterized by participants' autonomy and the absence of leaders and organizations. We conducted a participant observation study of the Anti-ELAB movement in Hong Kong. Focusing on the organization of a protest march, we collected thousands of lines of discourse in the LIHKG Forum and the Telegram instant messaging system. Our grounded theory analysis revealed hundreds of groups acting within a symbiotic network. Participants promoted an ethos of empowering individual participants and groups to act autonomously. At the same time, participants' extensive use of hyperlinks and polls orchestrated a coherent social movement. We discuss how this novel formation can mediate successful leaderless movements.
A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering

Effective methods are required to be developed that can deal with the multi faceted nature of the multiview data.We design a factorization-based loss function-based method to simultaneously learn two components encoding the consensus and complementary information present in multi-view data by using the Coupled Matrix Factorization (CMF) and Non-negative Matrix Factorization (NMF).We propose a novel optimal manifold for multi-view data which is the most consensed manifold embedded in the high-dimensional multi-view data.A new complementary enhancing term is added in the loss function to enhance the complementary information inherent in each view.An extensive experiment with diverse datasets, benchmarking the state-of-the-art multi-view clustering methods, has demonstrated the effectiveness of the proposed method in obtaining accurate clustering solution.
Towards Fine-Grained Human Pose Transfer With Detail Replenishing Network

Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for production quality and user engagement. However, existing HPT methods often suffer from three fundamental issues: detail deficiency, content ambiguity and style inconsistency, which severely degrade the visual quality and realism of generated images. Aiming towards real-world applications, we develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment. Concretely, we analyze the potential design flaws of existing methods via an illustrative example, and establish the core FHPT methodology by combing the idea of content synthesis and feature transfer together in a mutually-guided fashion. Thereafter, we substantiate the proposed methodology with a Detail Replenishing Network (DRN) and a corresponding coarse-to-fine model training scheme. Moreover, we build up a complete suite of fine-grained evaluation protocols to address the challenges of FHPT in a comprehensive manner, including semantic analysis, structural detection and perceptual quality assessment. Extensive experiments on the DeepFashion benchmark dataset have verified the power of proposed benchmark against start-of-the-art works, with 12\%-14\% gain on top-10 retrieval recall, 5\% higher joint localization accuracy, and near 40\% gain on face identity preservation. Moreover, the evaluation results offer further insights to the subject matter, which could inspire many promising future works along this direction.
Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection

This paper focuses on the construction of strong local features and the effective fusion of image and LiDAR data for 3D object detection. We adopt different modalities of LiDAR data to generate rich features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3D anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LiDAR data by introducing an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network.
Inducing and Mitigating Stereotype Threat Through Gendered Virtual Body-Swap Illusions

A psychological phenomenon termed "stereotype threat" has been shown to contribute to women's underperformance and underrepresentation in math and science fields. Within the virtual reality literature, a recent study utilized gendered body-swap illusions (i.e., women in male virtual bodies) to mitigate the effects of stereotype threat among a sample of female participants. The present research provides a much needed replication of this intervention, as well as a critical extension of virtual reality research on the Proteus Effect to test whether stereotype threat can be induced among male participants immersed in a female virtual body. Results supported both the replication and extension hypotheses; female participants embodied in male avatars were buffered from stereotype threat whereas male participants embodied in female avatars suffered from stereotype threat. Avatar gender also influenced participants' math confidence and awareness of the negative societal stereotype regarding women's math ability.
Video Monitoring Queries

Recent advances in video processing utilizing deep learning primitives achieved breakthroughs in fundamental problems in video analysis such as frame classification and object detection enabling an array of new applications. In this paper we study the problem of interactive declarative query processing on video streams. In particular we introduce a set of approximate filters to speed up queries that involve objects of specific type (e.g., cars, trucks, etc.) on video frames with associated spatial relationships among them (e.g., car left of truck). The resulting filters are able to assess quickly if the query predicates are true to proceed with further analysis of the frame or otherwise not consider the frame further avoiding costly object detection operations. We propose two classes of filters IC and OD, that adapt principles from deep image classification and object detection. The filters utilize extensible deep neural architectures and are easy to deploy and utilize. In addition, we propose statistical query processing techniques to process aggregate queries involving objects with spatial constraints on video streams and demonstrate experimentally the resulting increased accuracy on the resulting aggregate estimation. Combined these techniques constitute a robust set of video monitoring query processing techniques. We demonstrate that the application of the techniques proposed in conjunction with declarative queries on video streams can dramatically increase the frame processing rate and speed up query processing by at least two orders of magnitude. We present the results of a thorough experimental study utilizing benchmark video data sets at scale demonstrating the performance benefits and the practical relevance of our proposals.