Search

This article reviews how empirical research of exploratory search is conducted. We investigated aspects of interdisciplinarity, study settings and evaluation methodologies from a systematically selected sample of 231 publications from 2010-2021, including a total of 172 articles with empirical studies. Our results show that exploratory search is highly interdisciplinary, with the most frequently occurring publication venues including high impact venues in information science, information systems and human-computer interaction. However, taken in aggregate, the breadth of study settings investigated was limited. We found that a majority of studies (77%) focused on evaluating novel retrieval systems as opposed to investigating users' search processes. Furthermore, a disproportionate number of studies were based on scientific literature search (20.7%), a majority of which only considered searching for Computer Science articles. Study participants were generally from convenience samples, with 75% of studies composed exclusively of students and other academics. The methodologies used for evaluation were mostly quantitative, but lacked consistency between studies and validated questionnaires were rarely used. In discussion, we offer a critical analysis of our findings and suggest potential improvements for future exploratory search studies.

r/antiwork is a Reddit community that focuses on the discussion of worker exploitation, labour rights and related left-wing political ideas (e.g. universal basic income). In late 2021, r/antiwork became the fastest growing community on Reddit, coinciding with what the mainstream media began referring to as the Great Resignation. This same media coverage was attributed with popularising the subreddit and, therefore, accelerating its growth. In this article, we explore how the r/antiwork community was affected by the exponential increase in subscribers and the media coverage that chronicled its rise. We investigate how subreddit activity changed over time, the behaviour of heavy and light users, and how the topical nature of the discourse evolved with the influx of new subscribers. We report that, despite the continuing rise of subscribers well into 2022, activity on the subreddit collapsed after January 25th 2022, when a moderator's Fox news interview was widely criticised. While many users never commented again, longer running trends of users' posting and commenting behaviour did not change. Finally, while many users expressed their discontent at the changing nature of the subreddit as it became more popular, we found no evidence of major shifts in the topical content of discussion over the period studied, with the exception of the introduction of topics related to seasonal events (e.g. holidays, such as Thanksgiving) and ongoing developments in the news (e.g. working from home and the curtailing of reproductive rights in the United States).

Applications designed for entertainment and other non-instrumental purposes are challenging to optimize because the relationships between system parameters and user experience can be unclear. Ideally, we would crowdsource these design questions, but existing approaches are geared towards evaluation or ranking discrete choices and not for optimizing over continuous parameter spaces. In addition, users are accustomed to informally expressing opinions about experiences as critiques (e.g. it's too cold, too spicy, too big), rather than giving precise feedback as an optimization algorithm would require. Unfortunately, it can be difficult to analyze qualitative feedback, especially in the context of quantitative modeling. In this article, we present collective criticism, a critiquing-based approach for modeling relationships between system parameters and subjective preferences. We transform critiques, such as "it was too easy/too challenging", into censored intervals and analyze them using interval regression. Collective criticism has several advantages over other approaches: "too much/too little"-style feedback is intuitive for users and allows us to build predictive models for the optimal parameterization of the variables being critiqued. We present two studies where we model: (i) aesthetic preferences for images generated with neural style transfer, and (ii) users' experiences of challenge in the video game Tetris. These studies demonstrate the flexibility of our approach, and show that it produces robust results that are straightforward to interpret and inline with users' stated preferences.

Detecting lexical semantic change in smaller data sets, e.g. in historical linguistics and digital humanities, is challenging due to a lack of statistical power. This issue is exacerbated by non-contextual embedding models that produce one embedding per word and, therefore, mask the variability present in the data. In this article, we propose an approach to estimate semantic shift by combining contextual word embeddings with permutation-based statistical tests. We use the false discovery rate procedure to address the large number of hypothesis tests being conducted simultaneously. We demonstrate the performance of this approach in simulation where it achieves consistently high precision by suppressing false positives. We additionally analyze real-world data from SemEval-2020 Task 1 and the Liverpool FC subreddit corpus. We show that by taking sample variation into account, we can improve the robustness of individual semantic shift estimates without degrading overall performance.

This paper introduces two recurrent neural network structures called Simple Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general structures for learning long term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gates to control information flow in the network, SGU and DSGU only use one multiplicative gate to control the flow of information. We show that this difference can accelerate the learning speed in tasks that require long dependency information. We also show that DSGU is more numerically stable than SGU. In addition, we also propose a standard way of representing inner structure of RNN called RNN Conventional Graph (RCG), which helps analyzing the relationship between input units and hidden units of RNN., Comment: This paper has been withdrawn by the author due to lacking of enough experiments

A content-based image retrieval system based on multinomial relevance feedback is proposed. The system relies on an interactive search paradigm where at each round a user is presented with k images and selects the one closest to their ideal target. Two approaches, one based on the Dirichlet distribution and one based the Beta distribution, are used to model the problem motivating an algorithm that trades exploration and exploitation in presenting the images in each round. Experimental results show that the new approach compares favourably with previous work.

Users giving relevance feedback in exploratory search are often uncertain about the correctness of their feedback, which may result in noisy or even erroneous feedback. Additionally, the search intent of the user may be volatile as the user is constantly learning and reformulating her search hypotheses during the search. This may lead to a noticeable concept drift in the feedback. We formulate a Bayesian regression model for predicting the accuracy of each individual user feedback and thus find outliers in the feedback data set. Additionally, we introduce a timeline interface that visualizes the feedback history to the user and gives her suggestions on which past feedback is likely in need of adjustment. This interface also allows the user to adjust the feedback accuracy inferences made by the model. Simulation experiments demonstrate that the performance of the new user model outperforms a simpler baseline and that the performance approaches that of an oracle, given a small amount of additional user interaction. A user study shows that the proposed modelling technique, combined with the timeline interface, makes it easier for the users to notice and correct mistakes in their feedback, and to discover new items.

In order to achieve low Noise-Equivalent-Powers (NEP < 1 aW Hz$^{-1/2}$) Transition Edge Sensors (TES) require high levels of thermal isolation between the superconducting bilayer and the heat bath. We propose that short micro-machined acoustic interferometers can be used for low-noise thermal isolation, avoiding many of the difficulties inherent in conventional, long- legged TES designs. In this paper, we present a detailed elastic wave model of interferometric phononic legs, and demonstrate the successful fabrication of TESs that are thermally isolated by interferometric structures.

We have fabricated Transition Edge Sensors (TESs) whose thermal characteristics are completely characterised by few-mode ballistic phonon exchange with the heat bath. These TESs have extremely small amorphous SiNx support legs: 0.2 um thick, 0.7 to 1.0 um wide and 1.0 to 4.0 um long. We show, using classical elastic wave theory, that it is only necessary to know the geometry and bulk elastic constants of the material to calculate the thermal conductance and fluctuation noise. Our devices operate in the few-mode regime, between 5 and 7 modes per leg, and have noise equivalent powers (NEPs) of 1.2 aW Hz-1/2. The NEP is dominated by the thermal fluctuation noise in the legs, which itself is dominated by phonon shot-noise. Thus TESs have been demonstrated whose thermal characteristics are fully accounted for by an elastic noise-wave model. Our current devices, and second-generation devices based on patterned phononic filters, can be used to produce optically compact, mechanically robust, highly sensitive TES imaging arrays, circumventing many of the problems inherent in conventional long-legged designs., Comment: 10 pages, 9 figures, 1 table

CLOVER is an experiment which aims to detect the signature of gravitational waves from inflation by measuring the B-mode polarization of the cosmic microwave background. CLOVER consists of three telescopes operating at 97, 150, and 220 GHz. The 97-GHz telescope has 160 feedhorns in its focal plane while the 150 and 220-GHz telescopes have 256 horns each. The horns are arranged in a hexagonal array and feed a polarimeter which uses finline-coupled TES bolometers as detectors. To detect the two polarizations the 97-GHz telescope has 320 detectors while the 150 and 220-GHz telescopes have 512 detectors each. To achieve the target NEPs (1.5, 2.5, and 4.5x10^-17 W/rtHz) the detectors are cooled to 100 mK for the 97 and 150-GHz polarimeters and 230 mK for the 220-GHz polarimeter. Each detector is fabricated as a single chip to ensure a 100% operational focal plane. The detectors are contained in linear modules made of copper which form split-block waveguides. The detector modules contain 16 or 20 detectors each for compatibility with the hexagonal arrays of horns in the telescopes' focal planes. Each detector module contains a time-division SQUID multiplexer to read out the detectors. Further amplification of the multiplexed signals is provided by SQUID series arrays. The first prototype detectors for CLOVER operate with a bath temperature of 230 mK and are used to validate the detector design as well as the polarimeter technology. We describe the design of the CLOVER detectors, detector blocks, and readout, and present preliminary measurements of the prototype detectors performance., Comment: 4 pages, 6 figures; to appear in the Proceedings of the 17th International Symposium on Space Terahertz Technology, held 10-12 May 2006 in Paris

CLOVER is an experiment which aims to detect the signature of gravitational waves from inflation by measuring the B-mode polarization of the cosmic microwave background. CLOVER consists of three telescopes operating at 97, 150, and 220 GHz. The 97-GHz telescope has 160 horns in its focal plane while the 150 and 220-GHz telescopes have 256 horns each. The horns are arranged in a hexagonal array and feed a polarimeter which uses finline-coupled TES bolometers as detectors. To detect the two polarizations the 97-GHz telescope has 320 detectors while the 150 and 220-GHz telescopes have 512 detectors each. To achieve the required NEPs the detectors are cooled to 100 mK for the 97 and 150-GHz polarimeters and 230 mK for the 220-GHz polarimeter. Each detector is fabricated as a single chip to guarantee fully functioning focal planes. The detectors are contained in linear modules made of copper which form split-block waveguides. The detector modules contain 16 or 20 detectors each for compatibility with the hexagonal arrays of horns in the telescopes' focal planes. Each detector module contains a time-division SQUID multiplexer to read out the detectors. Further amplification of the multiplexed signals is provided by SQUID series arrays. The first prototype detectors for CLOVER operate with a bath temperature of 230 mK and are used to validate the detector design as well as the polarimeter technology. We describe the design of the CLOVER detectors, detector blocks, and readout, and give an update on the detector development., Comment: 9 pages, 7 figures, SPIE Orlando 24-31 May 2006, Conference 6275