Reviewerly/exHarmony huggingface dataset:
Supporting Serendipity

Qualitative inductive methods are widely used in CSCW and HCI research for their ability to generatively discover deep and contextualized insights, but these inherently manual and human-resource-intensive processes are often infeasible for analyzing large corpora. Researchers have been increasingly interested in ways to apply qualitative methods to "big" data problems, hoping to achieve more generalizable results from larger amounts of data while preserving the depth and richness of qualitative methods. In this paper, we describe a study of qualitative researchers' work practices and their challenges, with an eye towards whether this is an appropriate domain for human-AI collaboration and what successful collaborations might entail. Our findings characterize participants' diverse methodological practices and nuanced collaboration dynamics, and identify areas where they might benefit from AI-based tools. While participants highlight the messiness and uncertainty of qualitative inductive analysis, they still want full agency over the process and believe that AI should not interfere. Our study provides a deep investigation of task delegability in human-AI collaboration in the context of qualitative analysis, and offers directions for the design of AI assistance that honor serendipity, human agency, and ambiguity.
Combining Photogrammetry and Photometric Stereo to Achieve Precise and Complete 3D Reconstruction

Image-based 3D reconstruction has been employed in industrial metrology for micro-measurements and quality control purposes. However, generating a highly-detailed and reliable 3D reconstruction of non-collaborative surfaces is still an open issue. In this paper, a method for generating an accurate 3D reconstruction of non-collaborative surfaces through a combination of photogrammetry and photometric stereo is presented. On one side, the geometric information derived with photogrammetry is used in areas where its 3D measurements are reliable. On the other hand, the high spatial resolution capability of photometric stereo is exploited to acquire a finely detailed topography of the surface. Finally, three different approaches are proposed to fuse both geometric information and high frequency details. The proposed method is tested on six different non-collaborative objects with different surface characteristics. To evaluate the accuracy of the proposed method, a comprehensive cloud-to-cloud comparison between reference data and 3D points derived from the proposed fusion methods is provided. The experiments demonstrated that, despite correcting global deformation up to an average RMSE of less than 0.1 mm, the proposed method recovers the surface topography at the same high resolution as the photometric stereo.
The Cognitive Basis for the Design of a Mammography Interpretation Tutor

The purpose of this paper is to present a cognitively-based and empirically-derived approach for the design of the RadTutor, a prototype computerized tutor to train radiology residents in diagnosing mammograms exhibiting breast diseases. A multitude of computer-based radiology training environments have recently been developed with the objective of supporting the acquisition of radiological expertise. In general, however, these systems have failed in several aspects including a failure to incorporate theoretical perspectives and empirical findings to the design of these systems. This paper outlines the conceptual framework for the development of the prototype which includes: (1) a discussion of the objectives and goals of the radiology residency training program, (2) a review and critique of existing computer-based radiology training environments, (3) a synthesis of an expert-novice study aimed at attaining a cognitive model of problem solving in mammogram interpretation (Azevedo, 1997), (4) a description of the results of analyses of authentic radiology resident teaching rounds, and (5) deriving instructional principles for the design of the mammography tutor.
Fast and compact planar embeddings

There are many representations of planar graphs, but few are as elegant as Turán's (1984): it is simple and practical, uses only 4 bits per edge, can handle self-loops and multi-edges, and can store any specified embedding. Its main disadvantage has been that "it does not allow efficient searching" (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Turán's representation such that it supports fast navigation while retaining simplicity. As a consequence of the inherited simplicity, we offer the first efficient parallel construction of a compact encoding of a planar graph embedding. Our experimental results show that the resulting representation uses about 6 bits per edge in practice, supports basic navigation operations within a few microseconds, and can be built sequentially at a rate below 1 microsecond per edge, featuring a linear speedup with a parallel efficiency around 50% for large datasets.
Securing Cloud Data Under Key Exposure

Recent news reveal a powerful attacker which breaks data confidentiality by acquiring cryptographic keys, by means of coercion or backdoors in cryptographic software. Once the encryption key is exposed, the only viable measure to preserve data confidentiality is to limit the attacker's access to the ciphertext. This may be achieved, for example, by spreading ciphertext blocks across servers in multiple administrative domains-thus assuming that the adversary cannot compromise all of them. Nevertheless, if data is encrypted with existing schemes, an adversary equipped with the encryption key, can still compromise a single server and decrypt the ciphertext blocks stored therein. In this paper, we study data confidentiality against an adversary which knows the encryption key and has access to a large fraction of the ciphertext blocks. To this end, we propose Bastion, a novel and efficient scheme that guarantees data confidentiality even if the encryption key is leaked and the adversary has access to almost all ciphertext blocks. We analyze the security of Bastion, and we evaluate its performance by means of a prototype implementation. We also discuss practical insights with respect to the integration of Bastion in commercial dispersed storage systems. Our evaluation results suggest that Bastion is well-suited for integration in existing systems since it incurs less than 5 percent overhead compared to existing semantically secure encryption modes.
Sensor-Based Proximity Detection in the Face of Active Adversaries

Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a context-manipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniques: features-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases.
Exploratory Visual Sequence Mining Based on Pattern-Growth

Sequential pattern mining finds applications in numerous diverging fields. Due to the problem's combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user's perspective. Second, as datasets grow, mining large number of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a "transparent box" execution model. We propose a novel approach to interactive visual sequence mining that allows the user to guide the execution of a pattern-growth algorithm at suitable points through a powerful visual interface. Our approach (1) introduces the possibility of using local constraints during the mining process, (2) allows stepwise visualization of patterns being mined, and (3) enables the user to steer the mining algorithm towards directions of interest. The use of local constraints significantly improves users' capability to progressively refine the search space without the need to restart computations. We exemplify our approach using two event sequence datasets; one composed of web page visits and another composed of individuals' activity sequences.
Video Storytelling: Textual Summaries for Events

Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this paper, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a residual bidirectional recurrent neural network to leverage contextual information from past and future. The multimodal embedding is then used to retrieve sentences for video clips. Second, we propose a Narrator model to select clips that are representative of the underlying storyline. The Narrator is formulated as a reinforcement learning agent, which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the video story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines and show that our method achieves better performance, in terms of quantitative measures and user study.
Structure-aware Fisheye Views for Efficient Large Graph Exploration

Traditional fisheye views for exploring large graphs introduce substantial distortions that often lead to a decreased readability of paths and other interesting structures. To overcome these problems, we propose a framework for structure-aware fisheye views. Using edge orientations as constraints for graph layout optimization allows us not only to reduce spatial and temporal distortions during fisheye zooms, but also to improve the readability of the graph structure. Furthermore, the framework enables us to optimize fisheye lenses towards specific tasks and design a family of new lenses: polyfocal, cluster, and path lenses. A GPU implementation lets us process large graphs with up to 15,000 nodes at interactive rates. A comprehensive evaluation, a user study, and two case studies demonstrate that our structure-aware fisheye views improve layout readability and user performance.
GANs for generating EFT models

We initiate a way of generating effective field theories (EFT) models by the computer, satisfying both experimental and theoretical constraints. We use Generative Adversarial Networks (GAN) and display generated instances which go beyond the examples known to the machine during training. As a starting point, we apply this idea to the generation of supersymmetric field theories with a single field. We find cases where the number of minima in the generated scalar potential is different from values found in the training data. We comment on potential further applications of this framework.
An Efficient Optimization Method for Improving Generalization Performance of Fuzzy Neural Networks

Fuzzy neural networks (FNNs), with suitable structures, have been demonstrated to be an effective tool in approximating nonlinearity between input and output variables. However, it is time-consuming to construct an FNN with appropriate number of fuzzy rules to ensure its generalization ability. To solve this problem, an efficient optimization technique is introduced in this paper. First, a self-adaptive structural optimal algorithm (SASOA) is developed to minimize the structural risk of an FNN, leading to an improved generalization performance. Second, with the proposed SASOA, the fuzzy rules of SASOA-based FNN (SASOA-FNN) are generated or pruned systematically. This SASOA-FNN is able to organize the structure and adjust the parameters simultaneously in the learning process. Third, the convergence of SASOA-FNN is proved in the cases with fixed and updated structures, and the guidelines for selecting the parameters are given. Finally, experimental studies of the proposed SASOA-FNN have been performed on several nonlinear systems to verify the effectiveness. The comparison with other existing methods has been made, and it demonstrates that the proposed SASOA-FNN is of better performance.
Coherence measure: Logarithmic coherence number

We introduce a measure of coherence which is extended from the coherence rank via the standard convex roof construction; we call it the logarithmic coherence number. This approach is parallel to the Schmidt measure in entanglement theory. We study some interesting properties of the logarithmic coherence number and show that this quantifier can be considered as a proper coherence measure. We find that the logarithmic coherence number can be calculated exactly for a large class of mixed states. We also discuss the relationships between the logarithmic coherence number and other coherence quantifiers, e.g., the relative entropy of coherence, the ${l}_{1}$-norm coherence, and average fidelity coherence. We give the relationship between coherence and entanglement in a bipartite system, and our results can be generalized to multipartite settings. Finally, we give that the creation of entanglement with bipartite incoherent operations is bounded by the logarithmic coherence number of the initial system during the process.
Stochastic Graphlet Embedding

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (parts, interest points, and segments) and edges characterize the relationships between these primitives. However, these nonvectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of-explicit/implicit-graph vectorization and embedding. This embedding process should be resilient to intraclass graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have a positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
Tackling Sparse Rewards in Real-Time Games with Statistical Forward Planning Methods

One of the issues general AI game players are required to deal with is the different reward systems in the variety of games they are expected to be able to play at a high level. Some games may present plentiful rewards which the agents can use to guide their search for the best solution, whereas others feature sparse reward landscapes that provide little information to the agents. The work presented in this paper focuses on the latter case, which most agents struggle with. Thus, modifications are proposed for two algorithms, Monte Carlo Tree Search and Rolling Horizon Evolutionary Algorithms, aiming at improving performance in this type of games while maintaining overall win rate across those where rewards are plentiful. Results show that longer rollouts and individual lengths, either fixed or responsive to changes in fitness landscape features, lead to a boost of performance in the games during testing without being detrimental to non-sparse reward scenarios.
Interactive Visualization of 3D Histopathology in Native Resolution

We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue.Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging.As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice.Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology.Visualization of 3D histology data does pose difficult challenges in several aspects.The full-color datasets are dense and large in scale, on the order of 100,000 × 100,000× 100 voxels.This entails serious demands on both rendering performance and user experience design.Despite this, our developed application supports interactive study of 3D histology datasets at native resolution.Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members.Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.
Superconducting Quantum Annealing Architecture with LC Resonators

We propose a novel architecture for superconducting circuits to improve the efficiency of a quantum annealing system. To increase the capability of a circuit, it is desirable for a qubit to be coupled not only with adjacent qubits but also with other qubits located far away. We introduce a circuit that uses a lumped element resonator coupled each with one qubit. The resonator-qubit pairs are coupled by rf-superconducting quantum interference device (SQUID) based couplers. These pairs make a large quantum system for quantum annealer. This system could prepare the problem Hamiltonian and tune the parameters for quantum annealing procedure.
Incorporating Domain Knowledge into Natural Language Inference on Clinical Texts

Making inference on clinical texts is a task which has not been fully studied. With the newly released, expert annotated MedNLI dataset, this task is being boosted. Compared with open domain data, clinical texts present unique linguistic phenomena, e.g., a large number of medical terms and abbreviations, different written forms for the same medical concept, which make inference much harder. Incorporating domain-specific knowledge is a way to eliminate this problem, in this paper, we assemble a new incorporating medical concept definitions module on the classic enhanced sequential inference model (ESIM), which first extracts the most relevant medical concept for each word, if it exists, then encodes the definition of this medical concept with a bidirectional long short-term network (BiLSTM) to obtain domain-specific definition representations, and attends these definition representations over vanilla word embeddings. The empirical evaluations are conducted to demonstrate that our model improves the prediction performance and achieves a high level of accuracy on the MedNLI dataset. Specifically, the knowledge enhanced word representations contribute significantly to entailment class.
Data-driven reconstruction of nonlinear dynamics from sparse observation

We present a data-driven model to reconstruct nonlinear dynamics from a very sparse times series data, which relies on the strength of the echo state network (ESN) in learning nonlinear representation of data. With an assumption of the universal function approximation capability of ESN, it is shown that the reconstruction problem can be formulated as a fixed-point problem, in which the trajectory of the dynamical system is a fixed point of the ESN. An under-relaxed fixed-point iteration is proposed to reconstruct the nonlinear dynamics from a sparse observation. The proposed fixed-point ESN is tested against both univariate and multivariate chaotic dynamical systems by randomly removing up to 95% of the data. It is shown that the fixed-point ESN is able to reconstruct the complex dynamics from only 5∼10% of the data. For a relatively simple non-chaotic dynamical system, the numerical experiments on a forced van der Pol oscillator show that it is possible to reconstruct the nonlinear dynamics from only 1∼2% of the data.
RaKUn: Rank-based Keyword Extraction via Unsupervised Learning and Meta Vertex Aggregation

Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and rank keywords. Introducing meta vertices (aggregates of existing vertices) and systematic redundancy filters, the proposed method performs on par with state-of-the-art for the keyword extraction task on 14 diverse datasets. The proposed method is unsupervised, interpretable and can also be used for document visualization.
A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT)

The essential step of surrogating algorithms is phase randomizing the Fourier transform while preserving the original spectrum amplitude before computing the inverse Fourier transform. In this paper, we propose a new method which considers the graph Fourier transform. In this manner, much more flexibility is gained to define properties of the original graph signal which are to be preserved in the surrogates. The complex case is considered to allow unconstrained phase randomization in the transformed domain, hence we define a Hermitian Laplacian matrix that models the graph topology, whose eigenvectors form the basis of a complex graph Fourier transform. We have shown that the Hermitian Laplacian matrix may have negative eigenvalues. We also show in the paper that preserving the graph spectrum amplitude implies several invariances that can be controlled by the selected Hermitian Laplacian matrix. The interest of surrogating graph signals has been illustrated in the context of scarcity of instances in classifier training.