Reviewerly/exHarmony huggingface dataset:
Immunohistochemical Index Prediction of Breast Cancer by Using Hybrid Ultrasound Data

The immunohistochemical index is significant to help the selection of treatment strategy for breast cancer patients. Existing studies that focus on conventional ultrasound features and certain types of immunohistochemistry expressions are limited to correlation exploration, and only few studies have built predictive models. In this study, a Tri-Branch deep learning network is built for prediction of the immunohistochemical HER2 using the hybrid ultrasound data, instead of relying on the invasive and biopsy-based histopathological examination. Specifically, the deep learning model uses the cross-model attention and the interactive learning approaches to obtain the strong complementarity of hybrid data comprising B-mode US, contrast-enhanced ultrasound, and optical flow motion information to enhance accuracy of immunohistochemical HER2 prediction. The proposed prediction model was evaluated using hybrid ultrasound dataset from 335 breast cancer patients. The experimental results indicated that the Tri-Branch model had a high accuracy of 86.23% for HER2 status prediction, and the HER2 status prediction for patients with different pathology grades exhibited some meaningful clinical observations.
Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport

Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this letter, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.
Design and Implementation of a Fully-Actuated Integrated Aerial Platform Based on Geometric Model Predictive Control

Unlike individual unmanned aerial vehicles (UAVs), integrated aerial platforms (IAPs) containing multiple UAVs do not suffer from underactuation and can move omnidirectionally in six dimensions, providing a basis for constructing aerial manipulation platforms. Compared to single UAVs, multi-UAV IAPs are also advantageous in terms of payload and fault-tolerance capacity, making them promising candidates as platforms with integrated-response, observation, and strike capabilities. Herein, an IAP structure design containing three sub-UAVs connected in a star-like configuration is presented. This form of integration enables the IAP, as a whole, to simultaneously adjust its position and attitude in six dimensions. The dynamics of the overall system of the IAP are modeled. On this basis, an overall system controller is designed. To simplify control, based on stability of cascaded system, the rotational motion of the sub-UAVs is treated as a inner-loop subsystem, whereas the overall motion of the IAP is seen as a outer-loop subsystem. Because the configuration space of the sub-UAVs is non-Euclidean, a controller is designed for the outer-loop subsystem based on model predictive control on the manifold. Subsequently, the stability of the closed-loop system is demonstrated. Fieldbus technology is employed to design a real-time, scalable communication architecture for multiple sub-UAVs, followed by the development of a principle prototype of the multi-UAV IAP that consists of hardware and software systems. The effectiveness of the IAP design and control method is validated through simulation and real-world prototype-based tests. In the simulation and real-world tests, the proposed methodology can make the IAP system converge to the desired configuration at the presence of large initial configuration error. The same test scenario cannot be finished by a baseline PID controller. The advantage of the proposed control scheme in dealing with state and input constraints is shown via such tests.
Introduction: Towards Responsible Plant Data Linkage

Abstract This chapter provides a framing for this volume by reviewing the significance and the organisational, technical and social opportunities and challenges related to plant data linkage. We review what “responsible practice” means in relation to the plant environments being documented, the infrastructures used to circulate data, the institutions involved in data governance and the communities involved in plant data work. We show how, across these domains, responsible plant data linkage involves consideration of technical, legal, ethical and conceptual dimensions, thereby: (1) creating and maintaining digital infrastructures, technical standards and discussion venues focused on critical data reuse; (2) developing adequate legal and institutional frameworks that work transnationally; (3) identifying and implementing guidelines for what constitutes acceptable data use, together with systems to monitor and allocate responsibility for breaches and mistakes; and (4) considering the variety of views on what constitutes agricultural development in the first place and how plant research can sustainably, reliably and responsibly contribute to achieving food security. The production of sustainable, responsible and reliable agricultural solutions in the face of climatic and political change depends on the flourishing of transnational, interdisciplinary collaborations such as those represented in this volume.
An Underdeveloped Metaphor: The Mismatched Designs and Motivations of Digital Picture Interactions

Picture interactions are key to daily and long-term social connections between families and communities, especially through reminiscence. Across the nearly 200-year history of domestic photography, this social reminiscence has been accomplished largely through photo albums. However, in the now common digital setting, albums are pushed aside for the endless film roll metaphor. In this article, we explore this metaphor through the 20-year history of proposed digital picture interactions from Human–Computer Interaction research, and compare this to ongoing interactions with modern picture tools. Through these, we reveal that this prominent design metaphor does not create space for social reminiscence, but does fit the novel use of immediate sharing seen across social networking. Furthermore, the endless and ever-growing nature of digital film rolls are not meaningfully browsable for either intended use. We close by reconnecting to the past works that explore the broad potential of digital pictures.
Does the impact of private education on growth differ at different levels of credit market development?

Abstract Using an overlapping generations model, we show that the impact of private financing of education on growth depends on credit market development, being positive when credit markets are adequately developed but negative if sufficiently low levels of credit market development occur alongside relatively high private financing intensities. Employing cross‐country data, we find that reduced‐form growth relationships are statistically significant and robust under various controls and samples. We also lay out conditions under which economies with missing credit markets are dynamically efficient and outperform, in terms of growth, economies with complete credit markets. The latter may explain large cross‐country differences in savings and growth, while facilitating the evaluation of policies on financing education.
Local Eyebrow Feature Attention Network for Masked Face Recognition

During the COVID-19 coronavirus epidemic, wearing masks has become increasingly popular. Traditional occlusion face recognition algorithms are almost ineffective for such heavy mask occlusion. Therefore, it is urgent to improve the recognition performance of the existing face recognition technology on masked faces. Due to the limited visible feature points of the masked face image relative to the normal face image, we have to exploit the identification potential of eyebrow (referring to eyes and brows) features. This article proposes a local eyebrow feature attention network for masked face recognition, which consists of feature extraction, eyebrow region pooling, and feature fusion. To highlight the eyebrow region, we first use the eyebrow region pooling to separate the local features of eyebrows from the learned overall facial features. We then make full use of the symmetry of left and right eyebrows to emphasize their discriminant ability, due to the inadequate fine information of the low-resolution eyebrows. In particular, in view of the symmetrical similarity between eyebrow pairs and the subordinate relationship between facial components and the whole, we propose a feature fusion model based on graph convolutional network (GCN) to learn the feature association structure of eye features, brow features, and global facial features. We construct the benchmark datasets for masked face recognition to validate our approach, including real-world masked face recognition dataset (RMFRD) and synthetic masked face recognition dataset (SMFRD). Extensive experimental results on both public datasets and our built masked face datasets show that our approach significantly outperforms the state-of-the-arts.
OCPHN: Outfit Compatibility Prediction with Hypergraph Networks

With the rapid development of the online shopping, the pursuit of outfit compatibility has become a basic requirement for an increasing number of customers. The existing work on outfit compatibility prediction largely focuses on modeling pairwise item compatibility without considering modeling the whole outfit directly. To address the problem, in this paper, we propose a novel hypergraph-based compatibility modeling scheme named OCPHN, which is able to better model complex relationships among outfits. In OCPHN, we represent the outfit as a hypergraph, where each hypernode represents a category and each hyperedge represents the interactions between multiple categories (i.e., they appear in the same outfit). To better predict outfit compatibility, the hypergraph is transformed into a simple graph, and the message propagation mechanism in the graph convolution network is used to aggregate the neighbours’ information on the node and update the node representations. Furthermore, with learned node representations, an attention mechanism is introduced to compute the outfit compatibility score. Using a benchmark dataset, the experimental results show that the proposed method is an improvement over the strangest baselines in terms of accuracy by about 3% and 1% in the fill-in-the-blank and compatibility prediction tasks, respectively.
A Hybrid Semantic Networks Construction Framework for Engineering Design

Abstract This paper proposes a novel framework for building semantic networks from a seed design statement using Recursive Object Modeling (ROM), Word2Vec language modeling, and vector semantic-based method. Semantic Scholar API was used to retrieve abstracts of scientific papers to build ROM-based Semantic Networks to address the design problem implied in the seed design statement, following Environment Analysis from Environment-Based Design (EBD) methodology. The proposed framework was applied to construct the semantic network for a project to design aircraft braking systems, which demonstrates the framework's efficiency. The presented research makes two major contributions: a ROM-based phrase extractor and a domain-specific language model, which is trained on the automatically collected literature abstracts. Using a manually created and assessed truth set containing 100 pairs of abstract-key phrases, the phrase extractor was evaluated by benchmarking it with two existing off-the-shelf key phrase extraction algorithms: TextRank and Rake. The ROM-based phrase extractor extracted most key phrases from target domains and showed higher precision, recall, and F-1 scores than other methods. Meanwhile, the trained project-specific language model was evaluated using the NASA thesaurus. We randomly sampled 457 pairs of connected domain-specific terms related to aircraft braking and landing knowledge. Our Skip-gram model was compared with Google's pre-trained word2vec model and a baseline word2vec model. The results demonstrated that our language model could detect the most pairs of concepts from the NASA thesaurus. The generated semantic network can be applied to design information retrieval, computer-aided design idea generation, cross-domain communication support system, and designer training tool.
Server temperature prediction using deep neural networks to assist thermal-aware scheduling

Thermal-Aware (TA) scheduling is a primitive thermal-management tool to avoid hotspots and attain a better thermal profile inside data centers. However, TA scheduling depends on the accuracy of server temperature calculation for making reliable scheduling decisions. Existing TA simulation frameworks rely on the CRAC, RC, and thermodynamics model to calculate the temperature of a server. However, these models are not very efficient computationally while calculating the temperature of computing nodes. Hence, there is a need for an efficient and lightweight temperature prediction model to fill this gap. Moreover, existing TA allocation and migration schemes neglect the workload heterogeneity regarding execution time (length) of user tasks in batch workloads. Ignoring task heterogeneity may lead to higher ambient temperature on its neighboring servers—assigning a hotter job with a longer duration to a server with higher ambient effect results in significantly higher cooling expenses. Our contribution in this paper is twofold: (1) firstly, we design and train a deep neural network to predict the temperature of servers; our proposed DNN model achieves 96.11% prediction accuracy, and (2) secondly, we propose a TA algorithm for job allocation and migration. Specifically, we consider the length and thermal profile of user tasks and servers during allocation and migration. We compare the proposed strategy against existing TA scheduling designs such as TA Scheduling Algorithm (TASA) and TA Control Strategy (TACS) using simulations. Results demonstrate that the proposed TA approach significantly reduces the overall energy consumption by up to 12.03% and 8.28% compared to the TASA and TACS, respectively.
AB-GEP: Adversarial bandit gene expression programming for symbolic regression

Gene expression programming (GEP) is a commonly used approach for solving symbolic regression (SR) problems. However, GEP often falls into a local optimum. GEP also randomly searches for results and could revisit low value search spaces, which impacts its performance. To overcome the two problems, we propose a new algorithm using an adversarial bandit (AB) technique to enhance GEP and the new algorithm is named AB-GEP. AB-GEP segments the mathematical expression space into many subspaces. It then leverages a new search space selection method, AvgExp3, to enhance the population jump between subspaces. This prevents the algorithm from falling into a local optimum. AvgExp3 dynamically estimates a subspace by the rewards generated in the search space. The dynamical reward estimation makes AB-GEP more adaptive to reward changes and it also provides guidance for AB-GEP to choose a subspace that could contain the correct results. This study proves that AvgExp3 is an unbiased estimation of the average rewards of subspace, and its variance is lower than the standard Exp3 method. The evaluation on two benchmark datasets shows that AB-GEP can maintain better population diversity and obtain better results than three traditional GEPs, GEP, SL-GEP, and SPJ-GEP. AB-GEP ranks top in 50% of the 32 benchmarks, while one of the traditional GEPs only gets no more than 20% of the best results. We release our code at https://github.com/kgae-cup/ab-gep .
Exploiting high-order behaviour patterns for cross-domain sequential recommendation

The cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF) to alleviate the sparsity issue of user-interaction data. Specifically, we design time-aware and relation-aware graph attention mechanisms with collaborative filtering to exploit high-order behaviour patterns of users for promising results in both domains. Time-aware Graph Attention mechanism (TGAT) is designed to learn the inter-domain sequence-level representation of items. Relationship-aware Graph Attention mechanism (RGAT) is proposed to learn collaborative items' and users' feature representations. Moreover, to simultaneously improve the recommendation performance in the two domains, a Cross-domain Feature Bidirectional Transfer module (CFBT) is proposed, transferring user's common sharing features in both domains and retaining user's domain-specific features in a specific domain. Finally, cross-domain and sequential information jointly recommend the next items that users like. We conduct extensive experiments on two real-world datasets that show that CsrGCF outperforms several state-of-the-art baselines in terms of Recall and MRR. These demonstrate the necessity of exploiting high-order behaviour patterns of users for a cross-domain sequential recommendation. Meanwhile, retaining domain-specific features is an important step in the process of cross-domain feature bidirectional transferring.
Image encryption algorithm based on semi-tensor product theory

An image encryption algorithm is proposed by combining chaotic system with semi-tensor product theory. First, the four-way diffusion is performed on the plaintext image with a random interpolation. To further destroy or eliminate the image structure features, the image pixels are split into the octal plane and confused by Josephs scrambling with a variable step. Finally, the ciphertext image is obtained by performing a semi-tensor product operation on an encryption matrix. In this algorithm, the selection of the positions in the chaotic sequence is highly correlated with the plaintext image, which ensures the security of the algorithm without affecting the encryption efficiency.
Sketch-Based Design of Foundation Paper Pieceable Quilts

Foundation paper piecing is a widely used quilt-making technique in which fabric pieces are sewn onto a paper guide to facilitate construction. But, designing paper pieceable quilt patterns is challenging because the sewing process imposes constraints on both the geometry and sewing order of the fabric pieces. Based on a formative study with expert quilt designers, we develop a novel sketch-based tool for designing such quilt patterns. Our tool lets designers sketch a partial design as a set of edges, which may intersect but do not have to form closed polygons, and our tool automatically completes it into a fully paper pieceable pattern. We contribute a new sketch-completion algorithm that extends the input sketched edges into a planar mesh composed of closed polygonal faces representing fabric pieces, determines a paper pieceable sewing order for the faces, and breaks complicated sketches into independently paper pieceable sections when necessary. A partial input design often admits multiple visually different completions. Thus, our tool lets designers specify completion heuristics, which are based on current quilt design practices, to control the appearance of the completed quilt. Initial user evaluations with novice and expert quilt designers suggest that our tool fits within current design workflows and greatly facilitates designing foundation paper pieceable quilts by allowing users to focus on the visual design rather than tedious constraint checks.
Multiclass classification of hyperspectral remote sensed data using QSVC

Quantum Machine Learning (QML) is a branch of quantum computing that combines classical machine learning with the principles of quantum mechanics. It is emerging as an alternative to classical machine learning which exploits the quantum mechanical properties of entanglement and superposition to express the hidden patterns in the data. This reduces computational resources also the time required for processing. This research work is a comparative study, which compares the overall performance of Classical multi-class Support Vector Classifiers (SVC) with Quantum multi-class Support Vector Classifiers (QSVC). In this work, we used benchmark Hyperspectral Remotely Sensed datasets namely, Pavia University and Salinas-A on IBM gate-based Quantum Computer(QC). Here, in QSVC, kernel is generated by QC, and Qiskit’s Support Vector Classifier is used for classification. Classification of the pixels into their respective classes was experimented using two techniques, One vs One (OVO) and One v/s Rest (OVR). Quantum kernels are very expressive when compared to their classical counterparts and can learn complex data more efficiently. The overall accuracy of classification by QSVC is comparable to that of the classical SVC. We summarize our research by saying that QSVC performs better than SVC.
Spatial Audio Designer

The Web Audio API is an underutilized technology that provides a potential for rich interactive control over sound generation and rendering. Our team made use of the API in combination with Web3D technologies to create a spatial audio design tool for digital audiovisual creators. Our primary design challenge was creating an interface for visualizing and manipulating sound design in 3D space. We wanted our interface to be learnable and usable for our target user groups: digital music creators, digital audiovisual 3D artists, and physical audiovisual installation artists who wish to develop ideas in a virtual space. From user interviews, we learned that users needed a detailed visual 3D space as a starting point to populate with sound, as well as fine control over positioning of sound sources. The prototype web app can be used by digital and physical artists to create novel virtual audiovisual experiences, or to model a physical audiovisual installation to share and test with others. More work needs to be done to add direct spatial controls for sound fields and to make the app more natural to use. We asked artists of varying technical skill to use the app and re-create a reference scene, and measured how accurate their re-creation is.
Project Primrose: Reflective Light-Diffuser Modules for Non-Emissive Flexible Display Systems

Recent advances in smart materials have enabled displays to move beyond planar surfaces into the fabric of everyday life. We propose reflective light-diffuser modules for non-emissive flexible display systems. Our system leverages reflective-backed polymer-dispersed liquid crystal (PDLC), an electroactive material commonly used in smart window applications. This low-power non-emissive material can be cut to any shape, and dynamically diffuses light. We present the design & fabrication of two exemplar artifacts, a canvas and a handbag, that use the reflective light-diffuser modules. We also describe our content authoring pipeline and interaction modalities. We hope this work inspires future designers of flexible displays.