Reviewerly/exHarmony huggingface dataset:
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks

Language models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task’s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.
A visually secure image encryption scheme based on parallel compressive sensing

It is generally recognized that encrypting an original image into meaningless cipher image is an ideal method to protect image information. However, during transmission, the meaningless cipher image would draw attention and thus attract attacks. Recently, compressive sensing (CS) and carrier images have been utilized by Chai et al. to construct a novel image encryption scheme with visual security. However, in this scheme, some extra transmission is required for possible decryption besides the encrypted image. Moreover, the imperceptibility of the cipher image can be further improved and the recovered image quality would be severely degraded if unsuitable carrier images are selected. In this paper, we design a visually secure encryption scheme by using the parallel compressive sensing (PCS) counter mode and embedding technique. In order to achieve higher security level, Logistic-Tent system and 3-D Cat map are introduced to construct the measurement matrices and to disturb the order of the embedded information, respectively. Furthermore, experimental results demonstrate that the cipher image exhibits superior imperceptibility and the recovered image possesses more satisfactory quality, which is independent of the carrier image.
Multi-Person 3D Pose Estimation and Tracking in Sports

We present an approach to multi-person 3D pose estimation and tracking from multi-view video. Following independent 2D pose detection in each view, we: (1) correct errors in the output of the pose detector; (2) apply a fast greedy algorithm for associating 2D pose detections between camera views; and (3) use the associated poses to generate and track 3D skeletons. Previous methods for estimating skeletons of multiple people suffer long processing times or rely on appearance cues, reducing their applicability to sports. Our approach to associating poses between views works by seeking the best correspondences first in a greedy fashion, while reasoning about the cyclic nature of correspondences to constrain the search. The associated poses can be used to generate 3D skeletons, which we produce via robust triangulation. Our method can track 3D skeletons in the presence of missing detections, substantial occlusions, and large calibration error. We believe ours is the first method for full-body 3D pose estimation and tracking of multiple players in highly dynamic sports scenes. The proposed method achieves a significant improvement in speed over state-of-the-art methods.
Problematizing Replicable Design to Practice Respectful, Reciprocal, and Relational Co-designing with Indigenous People

Designing among Indigenous and non-Indigenous people is turbulent because we are all working within differing legacies of colonialism and entrenched systems of “othering.” When design enters this space through widely popular methods like the Double Diamond or Human-Centered Design (HCD) toolkits, it often carries legacies of its industrialized, Eurocentric origins. These origins emphasize problem-solving, replicable methods and outcomes, pursue simplicity and efficiency, and detach knowledge, people, and relationality from the sites of design’s embodiment. This risks perpetuating acts of colonialism, inadvertently displacing Indigenous practices, knowledges, and world views. Instead, we propose respectful, reciprocal, and relational approaches as an ontology of co-designing social innovation. This ontology requires a sensitivity to design’s location within multi-layered sites of power, knowledge, practices, cultural values, and precarious asymmetries as the condition of collaboration. We provide personal, reflexive stories as Māori, Pākehā, and Japanese designers negotiating the legacies of colonialism, laying bare our whole selves to show accountability and articulate pluralities of practices. In respecting design that is already rooted in local practices, we learn from these foundations and construct our practices in relation to them. For us, respect, reciprocity, and relationships are required dimensions of co-design as an engaged consciousness for Indigenous self-determination.
The Perceived Effectiveness of Blockchain for Digital Operational Risk Resilience in the European Union Insurance Market Sector

Due to the rise in the demand for information communication technologies (ICT), the need for operational risk resilience within the European insurance market sector has grown exponentially. This study aims to use the case of blockchain to evaluate whether the five characteristics determined from the literature to be required for effective digital risk resilience (specifically, integration, flexibility, reliability, relevance, and timeliness) have an impact on effectiveness in addressing the requirements of the European Union’s proposed Digital Operational Resilience Act (DORA). To achieve this, we developed a survey with 29 statements, which participants were required to answer using a five-point Likert scale. In total, 513 valid responses were received from participants. These were analyzed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Results show that in the case of blockchain, reliability, flexibility, and relevance were found to significantly relate to its effectiveness in addressing DORA’s requirements, but relationships of effectiveness with integration and timeliness were found to be insignificant. However, when the experience variable was added to the model as the moderator variable, we found that timeliness and relevance have a significant relationship with blockchain effectiveness, while integration, reliability, and flexibility do not.
Evaluating and Aggregating Feature-based Model Explanations

A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity.
Quantum Circuit Implementations of AES with Fewer Qubits

We propose some quantum circuit implementations of AES with the following improvements. Firstly, we propose some quantum circuits of the AES S-box and S-box $$^{-1}$$ , which require fewer qubits than prior work. Secondly, we reduce the number of qubits in the zig-zag method by introducing the S-box $$^{-1}$$ operation in our quantum circuits of AES. Thirdly, we present a method to reduce the number of qubits in the key schedule of AES. While the previous quantum circuits of AES-128, AES-192, and AES-256 need at least 864, 896, and 1232 qubits respectively, our quantum circuit implementations of AES-128, AES-192, and AES-256 only require 512, 640, and 768 qubits respectively, where the number of qubits is reduced by more than 30%.
Iterative Residual CNNs for Burst Photography Applications

Modern inexpensive imaging sensors suffer from inherent hardware constraints which often result in captured images of poor quality. Among the most common ways to deal with such limitations is to rely on burst photography, which nowadays acts as the backbone of all modern smartphone imaging applications. In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model. This, in turn, allows us to restore a single image of higher quality from a sequence of low-quality images as the solution of an optimization problem. Inspired by an extension of the gradient descent method that can handle non-smooth functions, namely the proximal gradient descent, and modern deep learning techniques, we propose a convolutional iterative network with a transparent architecture. Our network uses a burst of low-quality image frames and is able to produce an output of higher image quality recovering fine details which are not distinguishable in any of the original burst frames. We focus both on the burst photography pipeline as a whole, i.e., burst demosaicking and denoising, as well as on the traditional Gaussian denoising task. The developed method demonstrates consistent state-of-the art performance across the two tasks and as opposed to other recent deep learning approaches does not have any inherent restrictions either to the number of frames or their ordering.
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding

Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of generating proposals and inflexibility in determining fine-grained boundaries, and the latter ones usually attempt to decide the start and end timestamps directly, which brings about much difficulty and inaccuracy. In this paper, we convert this task into a multi-step decision problem and propose a novel Cascaded Prediction Network (CPN) to generate the grounding result in a coarse-to-fine manner. Concretely, we first encode video and query into the same latent space and fuse them into integrated representations. Afterwards, we construct a segment-tree-based structure and make predictions via decision navigation and signal decomposition in a cascaded way. We evaluate our proposed method on three large-scale publicly available benchmarks, namely ActivityNet Caption, Charades-STA and TACoS, where our CPN surpasses the performance of the state-of-the-art methods.
Feature Map Distillation of Thin Nets for Low-Resolution Object Recognition

Intelligent video surveillance is an important computer vision application in natural environments. Since detected objects under surveillance are usually low-resolution and noisy, their accurate recognition represents a huge challenge. Knowledge distillation is an effective method to deal with it, but existing related work usually focuses on reducing the channel count of a student network, not feature map size. As a result, they cannot transfer "privilege information" hidden in feature maps of a wide and deep teacher network into a thin and shallow student one, leading to the latter's poor performance. To address this issue, we propose a Feature Map Distillation (FMD) framework under which the feature map size of teacher and student networks is different. FMD consists of two main components: Feature Decoder Distillation (FDD) and Feature Map Consistency-enforcement (FMC). FDD reconstructs the shallow texture features of a thin student network to approximate the corresponding samples in a teacher network, which allows the high-resolution ones to directly guide the learning of the shallow features of the student network. FMC makes the size and direction of each deep feature map consistent between student and teacher networks, which constrains each pair of feature maps to produce the same feature distribution. FDD and FMC allow a thin student network to learn rich "privilege information" in feature maps of a wide teacher network. The overall performance of FMD is verified in multiple recognition tasks by comparing it with state-of-the-art knowledge distillation methods on low-resolution and noisy objects.
Ideal Quantum Nondemolition Readout of a Flux Qubit without Purcell Limitations

Quantum technologies, including those based on superconducting quantum circuits, require high-fidelity high-speed detection of the quantum state of a qubit. Standard quantum nondemolition readout of a superconducting qubit is based on its dispersive coupling to a resonator, but this method suffers from the Purcell effects: Purcell decay, critical photon number, and qubit-dependent Kerr nonlinearity. The authors propose a method in which both speed and fidelity of flux-qubit readout can avoid all three Purcell limitations.
Introduction, evaluation and application of an energy balance model for photovoltaic modules

Here we introduce a new energy balance model that accurately simulates the complete diurnal dynamics of photovoltaic (PV) thermal behavior with routinely available meteorological input. The model is evaluated extensively against observed module surface temperatures (day and nighttime), electrical output, and sensible heat flux measurements. It is demonstrated that different tracking systems have a significant effect on module temperatures and sensible heat fluxes by modulating the total radiation received on the PV surface. A model intercomparison study indicates significant improvements in the representation of module temperatures compared to an earlier study, the commercial software PVsyst and the Python package PVLIB. A sensitivity study demonstrates a considerable effect of the PV conversion efficiency and longwave emissivity on sensible heat fluxes emitted by the module. The model is available as a stand-alone program (UCRC-Solar) written in Python and planned to be implemented in mesoscale meteorological models to study the geophysical impacts of PV arrays at larger spatial and longer temporal scales.
Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations

Recent advances in whole-slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence-based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well-defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large-scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real-world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
On Solving Word Equations Using SAT

We present Woorpje, a string solver for bounded word equations (i.e., equations where the length of each variable is upper bounded by a given integer). Our algorithm works by reformulating the satisfiability of bounded word equations as a reachability problem for nondeterministic finite automata, and then carefully encoding this as a propositional satisfiability problem, which we then solve using the well-known Glucose SAT-solver. This approach has the advantage of allowing for the natural inclusion of additional linear length constraints. Our solver obtains reliable and competitive results and, remarkably, discovered several cases where state-of-the-art solvers exhibit a faulty behaviour.
Comparing High-Dimensional Partitions with the Co-clustering Adjusted Rand Index

We consider the simultaneous clustering of rows and columns of a matrix and more particularly the ability to measure the agreement between two co-clustering partitions. The new criterion we developed is based on the Adjusted Rand Index and is called the Co-clustering Adjusted Rand Index named CARI. We also suggest new improvements to existing criteria such as the classification error which counts the proportion of misclassified cells and the Extended Normalized Mutual Information criterion which is a generalization of the criterion based on mutual information in the case of classic classifications. We study these criteria with regard to some desired properties deriving from the co-clustering context. Experiments on simulated and real observed data are proposed to compare the behavior of these criteria.
Detecting and predicting visually induced motion sickness with physiological measures in combination with machine learning techniques

Visually induced motion sickness (VIMS) is a common sensation when using visual displays such as smartphones or Virtual Reality. In the present study, we investigated whether Machine Learning (ML) techniques in combination with physiological measures (ECG, EDA, EGG, respiration, body and skin temperature, and body movements) could be used to detect and predict the severity of VIMS in real-time, minute-by-minute. A total of 43 healthy younger adults (25 female) were exposed to a 15-minute VIMS-inducing video. VIMS severity was subjectively measured during the video using the Fast Motion Sickness Scale (FMS) as well as before and after the video using the Simulator Sickness Questionnaire (SSQ). Thirty-one participants (72%) experienced VIMS in the present study. Results showed that changes in facial skin temperature and body movement had the strongest relationship with VIMS. On a minute-by-minute basis, ML models revealed a medium correlation between the physiological measures and the FMS scores. An acceptable classification score distinguishing between sick and non-sick participants was found. Our findings suggest that physiological measures may be useful for measuring VIMS, but they are not a reliable standalone method to detect or predict VIMS severity in real-time.
An Improved Calibration Technique for MEMS Accelerometer-Based Inclinometers

Micro-electro-mechanical system (MEMS) accelerometer-based inclinometers are widely used to measure deformations of civil structures. To further improve the measurement accuracy, a new calibration technique was proposed in this paper. First, a single-parameter calibration model was constructed to obtain accurate angles. Then, an image-processing-based method was designed to obtain the key parameter for the calibration model. An ADXL355 accelerometer-based inclinometer was calibrated to evaluate the feasibility of the technique. In this validation experiment, the technique was proven to be reliable and robust. Finally, to evaluate the performance of the technique, the calibrated MEMS inclinometer was used to measure the deflections of a scale beam model. The experimental results demonstrate that the proposed technique can yield accurate deformation measurements for MEMS inclinometers.
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions

Despite the ubiquity of the Gaussian process regression model, few theoretical results are available that account for the fact that parameters of the covariance kernel typically need to be estimated from the data set. This article provides one of the first theoretical analyses in the context of Gaussian process regression with a noiseless data set. Specifically, we consider the scenario where the scale parameter of a Sobolev kernel (such as a Matérn kernel) is estimated by maximum likelihood. We show that the maximum likelihood estimation of the scale parameter alone provides significant adaptation against misspecification of the Gaussian process model in the sense that the model can become “slowly” overconfident at worst, regardless of the difference between the smoothness of the data-generating function and that expected by the model. The analysis is based on a combination of techniques from nonparametric regression and scattered data interpolation. Empirical results are provided in support of the theoretical findings.
A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

eXplainable Artificial Intelligence (XAI) aims to provide intelligible explanations to users. XAI algorithms such as SHAP, LIME and Scoped Rules compute feature importance for machine learning predictions. Although XAI has attracted much research attention, applying XAI techniques in healthcare to inform clinical decision making is challenging. In this paper, we provide a comparison of explanations given by XAI methods as a tertiary extension in analysing complex Electronic Health Records (EHRs). With a large-scale EHR dataset, we compare features of EHRs in terms of their prediction importance estimated by XAI models. Our experimental results show that the studied XAI methods circumstantially generate different top features; their aberrations in shared feature importance merit further exploration from domain-experts to evaluate human trust towards XAI.
Inventory Classification Using Multi-Level Association Rule Mining

Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.