Reviewerly/exHarmony huggingface dataset:
Identifying Features that Enhance Older Adults’ Acceptance of Robots: A Mixed Methods Study

With global aging, robots are considered a promising solution for handling the shortage of aged care and companionships. However, these technologies would serve little purpose if their intended users do not accept them. While the socioemotional selectivity theory predicts that older adults would accept robots that offer emotionally meaningful relationships, selective optimization with compensation model predicts that older adults would accept robots that compensate for their functional losses.The present study aims to understand older adults' expectations for robots and to compare older adults' acceptance ratings for 2 existing robots: one of them is a more human-like and more service-oriented robot and the other one is a more animal-like and more companion-oriented robot.A mixed methods study was conducted with 33 healthy, community-dwelling Taiwanese older adults (age range: 59-82 years). Participants first completed a semi-structured interview regarding their ideal robot. After receiving information about the 2 existing robots, they then completed the Unified Theory of Acceptance and Use of Technology questionnaires to report their pre-implementation acceptance of the 2 robots.Interviews were transcribed for conventional content analysis with satisfactory inter-rater reliability. From the interview data, a collection of older adults' ideal robot characteristics emerged with highlights of humanlike qualities. From the questionnaire data, respondents showed a higher level of acceptance toward the more service-oriented robot than the more companion-oriented robot in terms of attitude, perceived adaptiveness, and perceived usefulness. From the mixed methods analyses, the finding that older adults had a higher level of positive attitude towards the more service-oriented robot than the more companion-oriented robot was predicted by higher expectation or preference for robots with more service-related functions.This study identified older adults' preference toward more functional and humanlike robots. Our findings provide practical suggestions for future robot designs that target the older population.
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The codes in the title are studied with respect to existence, enumeration and asymptotic performance. Their Gray images are shown to satisfy a modified Gilbert-Varshamov bound. Explicit counting formulas are derived. Examples of modest lengths are given where Gray images of LCD Z4-codes outperform the best known binary linear LCD codes.
Color Image Encryption Algorithm Based on DNA Coding and Double Chaos System

This paper combines DNA computing with double-chaos systems and proposes an algorithm for color image encryption at the bit level.First, we use Arnold algorithm to scramble the three components of the color image, and the number of iterations was determined by the average of the three components, which improved the scrambling effect of Arnold algorithm.Secondly, after a lot of experiments, we propose a double-chaos system composed of Lorenz chaotic mapping with variable parameters and fourth-order Rossler hyperchaotic mapping to generate three sets of chaotic sequences for diffusion operation.The double-chaos system compensates the pseudo-randomness of the two types of chaotic mappings, making chaotic sequences more difficult to predict.Then, we transform the chaotic component images and chaotic sequences into DNA sequences in accordance with eight DNA coding rules, and the coding rules are determined by plaintext information or generated chaotic sequences.We also perform addition, subtraction, and XOR operations on them.DNA computation can realize color image bit-level diffusion and reduce the computational cost.The plaintext information is embedded in the encryption process to achieve ''One-Time Pad''.Simulation experiments and detailed analysis are conducted with the proposed encryption scheme to prove that the algorithm has good security performance and can effectively resist all types of attacks, indicating that the proposed algorithm is competitive.
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks

Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.However, most existing studies suffer from the noise in the dependency trees, especially when they are automatically generated, so that intensively leveraging dependency information may introduce confusions to relation classification and necessary pruning is of great importance in this task.In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN).In this approach, an attention mechanism upon graph convolutional networks is applied to different contextual words in the dependency tree obtained from an offthe-shelf dependency parser, to distinguish the importance of different word dependencies.Consider that dependency types among words also contain important contextual guidance, which is potentially helpful for relation extraction, we also include the type information in A-GCN modeling.Experimental results on two English benchmark datasets demonstrate the effectiveness of our A-GCN, which outperforms previous studies and achieves state-ofthe-art performance on both datasets. 1
On quantum methods for machine learning problems part I: Quantum tools

This is a review of quantum methods for machine learning problems that consists of two parts.The first part, "quantum tools", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods.The second part, "quantum classification algorithms", introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization

Differentiable architecture search (DARTS) is a prevailing NAS solution to identify architectures. Based on the continuous relaxation of the architecture space, DARTS learns a differentiable architecture weight and largely reduces the search cost. However, its stability has been challenged for yielding deteriorating architectures as the search proceeds. We find that the precipitous validation loss landscape, which leads to a dramatic performance drop when distilling the final architecture, is an essential factor that causes instability. Based on this observation, we propose a perturbation-based regularization - SmoothDARTS (SDARTS), to smooth the loss landscape and improve the generalizability of DARTS-based methods. In particular, our new formulations stabilize DARTS-based methods by either random smoothing or adversarial attack. The search trajectory on NAS-Bench-1Shot1 demonstrates the effectiveness of our approach and due to the improved stability, we achieve performance gain across various search spaces on 4 datasets. Furthermore, we mathematically show that SDARTS implicitly regularizes the Hessian norm of the validation loss, which accounts for a smoother loss landscape and improved performance.
Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85-90%), specificity of 84% (79-87%), and AUC of 0.92 (0.90-0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.
Fundamental limits of quantum error mitigation

Abstract The inevitable accumulation of errors in near-future quantum devices represents a key obstacle in delivering practical quantum advantages, motivating the development of various quantum error-mitigation methods. Here, we derive fundamental bounds concerning how error-mitigation algorithms can reduce the computation error as a function of their sampling overhead. Our bounds place universal performance limits on a general error-mitigation protocol class. We use them to show (1) that the sampling overhead that ensures a certain computational accuracy for mitigating local depolarizing noise in layered circuits scales exponentially with the circuit depth for general error-mitigation protocols and (2) the optimality of probabilistic error cancellation among a wide class of strategies in mitigating the local dephasing noise on an arbitrary number of qubits. Our results provide a means to identify when a given quantum error-mitigation strategy is optimal and when there is potential room for improvement.