Reviewerly/exHarmony huggingface dataset:
Making a Medical Maker's Playbook: An Ethnographic Study of Safety-Critical Collective Design by Makers in Response to COVID-19

We present an ethnographic study of a maker community that conducted safety-driven medical making to deliver over 80,000 devices for use at medical facilities in response to the COVID-19 pandemic. To achieve this, the community had to balance their clinical value of safety with the maker value of broadened participation in design and production. We analyse their struggles and achievement through the artifacts they produced and the labors of key facilitators between diverse community members. Based on this analysis we provide insights into how medical maker communities, which are necessarily risk-averse and safety-oriented, can still support makers' grassroots efforts to care for their communities. Based on these findings, we recommend that design tools enable adaptation to a wider set of domains, rather than exclusively presenting information relevant to manufacturing. Further, we call for future work on the portability of designs across different types of printers which could enable broader participation in future maker efforts at this scale.
RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation

Differential evolution (DE) is widely used for global optimisation problems due to its simplicity and efficiency. L-SHADE is a state-of-the-art variant of DE algorithm that incorporates external archive, success-history-based parameter adaptation, and linear population size reduction. L-SHADE uses a current-to-pbest/1/bin strategy for mutation operator, while all individuals have the same probability to be selected. In this paper, we propose a novel L-SHADE algorithm, RWS-L-SHADE, based on a roulette wheel selection strategy so that better individuals have a higher priority and worse individuals are less likely to be selected. Our extensive experiments on the CEC-2017 benchmark functions and dimensionalities of 30, 50 and 100 indicate that RWS-L-SHADE outperforms L-SHADE.
Multi-Uav Trajectory Planning for 3d Visual Inspection of Complex Structures

This paper presents a new trajectory planning algorithm for three-dimensional autonomous UAV volume coverage and visual inspection. The algorithm is an extension of a state-of-the-art Heat Equation Driven Area Coverage (HEDAC) multi-agent area coverage algorithm for three-dimensional domains. With a given target exploration density field, the algorithm designs a potential field to achieve the minimization of the remaining density and generate trajectories using potential gradients to direct UAVs to the regions of higher potential, i.e., higher values of remaining density. Collisions between the agents and agents with domain boundaries are prevented by implementing the distance field and correcting the agent’s directional vector when the distance threshold is reached. A unit cube test case is considered to evaluate this trajectory planning strategy for volume coverage. The case demonstrates successful collision avoidance even inside a crowded domain with a hundred UAVs exploring it. For visual inspection applications, the algorithm is supplemented with camera direction control. A field containing the nearest distance from any point in the domain to the structure surface is designed. The gradient of this field is calculated to obtain the camera orientation throughout the trajectory. Three different test cases of varying complexities are considered to validate the proposed method for visual inspection. The simplest scenario is a synthetic portal-like structure inspected using three UAVs. The other two inspection scenarios are based on realistic structures where UAVs are commonly utilized: a wind turbine and a bridge. When deployed to a wind turbine inspection, two simulated UAVs traversing smooth spiral trajectories have successfully explored the entire turbine structure while cameras are directed to the curved surfaces of the turbine’s blades. In the bridge test case, commonly used as a test case in scientific publications focused on UAV inspection, an efficacious visual inspection of a complex structure is demonstrated by employing a single UAV and five UAVs. The proposed methodology is successful in planning the UAV visual inspection. It offers flexibility in various setup parameters and is applicable in real-world inspection tasks.
MLP-Pose: Human Pose Estimation by MLP-Mixer

Current human pose estimation methods mainly use multi-scale fusion fully convolutional networks to achieve impressive results. However, this fully convolutional network lacks the ability to capture the relationship between features. In this paper, we propose a human pose estimation method based on MLP-Mixer. In detail, using 1D heatmaps as the ground truth, the human pose estimation is transformed into a sequence prediction problem on the horizontal axis and the vertical axis, so that the MLP-Mixer can be directly used to capture the relationship between the features. In addition, the existing backbone lacks intra-layer fusing. In order to solve this problem, we propose an efficient intra-layer fusion module. Specifically, our proposed MLP-Pose can achieve 77. 0AP and 76. 2AP on the COCO validation and test-dev dataset respectively.
Transfer Learning Model Knowledge Across Multi-Sensors Locations Over Body Sensor Network

With the growth of sensing technologies, the sensors are applied to diversified fields including health care, elderly protection, human activity abnormal detection, and surveillance. The advanced sensors embedded in mobile devices generate a large amount of valuable data. In recent years, to deal with the massive volumes of data, representation learning emerged as an alternative approach to extract the features without manual feature extraction. In this paper, we develop an unsupervised representation learning system for mining features across multiple sensors placed on different parts of the human body for recognizing human daily activities. The unsupervised representation learning approach allows models to learn the feature representation among a large number of unlabeled data samples collected from different parts of the human body. In order to demonstrate the feasibility of our system, extensive experiments on human daily activities recognition are carried out to evaluate the effectiveness of the learned representations.
Theoretical and empirical validation of software trustworthiness measure based on the decomposition of attributes

From the perspective of attribute decomposition, there are a variety of software trustworthiness metric models. However, little attention has been paid to using more rigorous methods and to performing theoretical validation. Axiomatic methods formalise the empirical understanding of software attributes through defining ideal metric properties. They can offer precise terms for the software attributes' quantification. We have utilised them to assess software trustworthiness on the basis of attribute decomposition, presented four properties, constructed a software trustworthiness measure (STMBDA for short). In this paper, we extend the set of properties, introduce two new properties, namely non-negativity and proportionality, and perfect substitutability and expectability. We verify the theoretical rationality of STMBDA by demonstrating that it conforms to the new property set and the empirical validity by evaluating the trustworthiness of 23 spacecraft software. The validation results show that STMBDA is able to effectively assess the spacecraft software trustworthiness and identify weaknesses in the development process.
Diagnosis of Liver Disease using Cost-Sensitive Support Vector Machine Classifier

In this paper, a reliable decision support system for the accurate diagnosis of liver disease is presented. A benchmark liver disease dataset was utilized to build the prediction model. However, the dataset in question is imbalanced, leading to a biased prediction towards the majority class. A standard error-driven Support Vector Machine (SVM) classifier fails completely to distinguish the diseased patients from the healthy ones due to the large class imbalance present. To alleviate the problem, the SVM classifier is modified into a cost-sensitive design where unequal costs are assigned to different classes. The minority class instances are strongly penalized compared to the majority class. This shifts the decision boundary away from the majority class, leading to a more balanced predictive performance. A grid search technique was utilized to identify the appropriate cost settings to achieve optimal performance. A g-mean score of 65.82%, with a sensitivity of 72.36% and specificity of 59.88% was achieved using our proposed approach. On the contrary, standard error-driven SVM classifier provided a g-mean score of zero. Thus, our proposed methodology has the potential to be an effective tool in the prediction of liver disease.
Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

This research proposes a method called enhanced collaborative and geometric multi-kernel learning (E-CGMKL) that can enhance the CGMKL algorithm which deals with multi-class classification problems with non-linear data distributions.CGMKL combines multiple kernel learning with softmax function using the framework of multi empirical kernel learning (MEKL) in which empirical kernel mapping (EKM) provides explicit feature construction in the high dimensional kernel space.CGMKL ensures the consistent output of samples across kernel spaces and minimizes the within-class distance to highlight geometric features of multiple classes.However, the kernels constructed by CGMKL do not have any explicit relationship among them and try to construct high dimensional feature representations independently from each other.This could be disadvantageous for learning on datasets with complex hidden structures.To overcome this limitation, E-CGMKL constructs kernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not only provide multiple feature representations but also inherit the compositional hierarchy of the hidden layers, which might be beneficial for enhancing the predictive performance of the CGMKL algorithm on complex data with natural hierarchical structures, for example, image data.Furthermore, our proposed scheme handles image data by constructing kernel spaces from a convolutional neural network (CNN).Considering the effectiveness of CNN architecture on image data, these kernel spaces provide a major advantage over the CGMKL algorithm which does not exploit the CNN architecture for constructing kernel spaces from image data.Additionally, outputs of hidden layers directly provide features for kernel spaces and unlike CGMKL, do not require an approximate MEKL framework.E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositional hierarchy of kernel spaces extracted from DNN hidden layers to enhance the 5100 CMC, 2022, vol.72, no.3 predictive performance of CGMKL significantly.The experimental results on various data sets demonstrate the superior performance of the E-CGMKL algorithm compared to other competing methods including the benchmark CGMKL.
Two-Branch Attention Network via Efficient Semantic Coupling for One-Shot Learning

Over the past few years, Convolutional Neural Networks (CNNs) have achieved remarkable advancement for the tasks of one-shot image classification. However, the lack of effective attention modeling has limited its performance. In this paper, we propose a Two-branch (Content-aware and Position-aware) Attention (CPA) Network via an Efficient Semantic Coupling module for attention modeling. Specifically, we harness content-aware attention to model the characteristic features (e.g., color, shape, texture) as well as position-aware attention to model the spatial position weights. In addition, we exploit support images to improve the learning of attention for the query images. Similarly, we also use query images to enhance the attention model of the support set. Furthermore, we design a local-global optimizing framework that further improves the recognition accuracy. The extensive experiments on four common datasets (miniImageNet, tieredImageNet, CUB-200-2011, CIFAR-FS) with three popular networks (DPGN, RelationNet and IFSL) demonstrate that our devised CPA module equipped with local-global Two-stream framework (CPAT) can achieve state-of-the-art performance, with a significant improvement in accuracy of 3.16% on CUB-200-2011 in particular.
An image-guided network for depth edge enhancement

Abstract With the rapid development of 3D coding and display technologies, numerous applications are emerging to target human immersive entertainments. To achieve a prime 3D visual experience, high accuracy depth maps play a crucial role. However, depth maps retrieved from most devices still suffer inaccuracies at object boundaries. Therefore, a depth enhancement system is usually needed to correct the error. Recent developments by applying deep learning to deep enhancement have shown their promising improvement. In this paper, we propose a deep depth enhancement network system that effectively corrects the inaccurate depth using color images as a guide. The proposed network contains both depth and image branches, where we combine a new set of features from the image branch with those from the depth branch. Experimental results show that the proposed system achieves a better depth correction performance than state of the art advanced networks. The ablation study reveals that the proposed loss functions in use of image information can enhance depth map accuracy effectively.
Weighted Loudspeaker Placement Method for Sound Field Reproduction

Sound field reproduction aims to reconstruct a desired sound field in a listening area by using multiple loudspeakers. To improve sound field reproduction performance, several loudspeaker placement methods have been proposed to select suitable loudspeaker positions. However, most conventional loudspeaker placement methods do not consider the distribution characteristic of the desired sound fields such as the distribution of directions from where the sound wave emanates, making these methods not optimal when the prior knowledge of the distribution characteristic of the desired sound fields is known. In this paper, a weighted loudspeaker placement method is proposed, which considers optimizing the objective function that depends on a set of desired sound fields with different weights. This paper formulates the optimization problem for weighted loudspeaker placement, presenting three alternative discretization methods to obtain a more practical optimization problem, after which the group greedy method is used to solve the optimization problem to find the near-optimal loudspeaker setup. To validate the proposed method, numerical experiments aiming to reconstruct the desired sound wave from a restricted range are conducted. The results indicate that the proposed method performs well in both narrowband and broadband cases, because it can use prior knowledge of the distribution characteristic of the desired sound fields.
Rethinking Adjacent Dependency in Session-Based Recommendations

Session-based recommendations (SBRs) recommend the next item for an anonymous user by modeling the dependencies between items in a session. Benefiting from the superiority of graph neural networks (GNN) in learning complex dependencies, GNN-based SBRs have become the main stream of SBRs in recent years. Most GNN-based SBRs are based on a strong assumption of adjacent dependency, which means any two adjacent items in a session are necessarily dependent here. However, based on our observation, the adjacency does not necessarily indicate dependency due to the uncertainty and complexity of user behaviours. Therefore, the aforementioned assumption does not always hold in the real-world cases and thus easily leads to two deficiencies: (1) the introduction of false dependencies between items which are adjacent in a session but are not really dependent, and (2) the missing of true dependencies between items which are not adjacent but are actually dependent. Such deficiencies significantly downgrade accurate dependency learning and thus reduce the recommendation performance. Aiming to address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes the topic information extracted from items’ reviews to refine dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms the state-of-the-art methods (The implementation is available at https://github.com/Nishikata97/RI-GNN. ).
Emotion-Aware Multimodal Pre-training for Image-Grounded Emotional Response Generation

Face-to-face communication leads to better interactions between speakers than text-to-text conversations since the speakers can capture both textual and visual signals. Image-grounded emotional response generation (IgERG) tasks requires chatbots to generate a response with the understanding of both textual contexts and speakers’ emotions in visual signals. Pre-training models enhance many NLP and CV tasks and image-text pre-training also helps multimodal tasks. However, existing image-text pre-training methods typically pre-train on images by recognizing or modeling objects, but ignore the emotions expressed in the images. In this paper, we propose several pre-training tasks in a unified framework that not only captures emotions from images but also learns to incorporate the emotion into text generation. The pre-training involves single-modal learning to strengthen the ability to understand images and generate texts. It also involves cross-modal learning to enhance interactions between images and texts. The experiments verify our method in appropriateness, informativeness, and emotion consistency.
Interpersonal Conflicts During Code Review

Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Preprint: https://arxiv.org/abs/2201.05425 Data and material: https://doi.org/10.5281/zenodo.5848794
Extraction and Analysis of Regionally Specific Behavioral Facilitation Information in the Event of a Large-scale Disaster

During and after large-scale disasters, many people try to obtain disaster-related information from social networking services (SNSs) such as Twitter. This is true because SNSs have much useful information about a disaster. Such information includes prompting disaster-affected people to take action. Therefore, we have proposed a tweet extraction method to encourage disaster-affected people to take action on Twitter. We designate tweets that encourage disaster-affected people to take action as “behavioral facilitation tweets (BF tweets.” After analyzing behavioral facilitation tweets, we found that these tweets include local information that is more useful for disaster-affected people than tweets with no local information. For this study, we call such tweets “Region-specific Behavioral Facilitation tweets (RBF tweets)”: they have more specific disaster content and are more likely to promote action. As described herein, we propose an RBF tweet extraction method for use after a large-scale disaster. Furthermore, after we propose a method of classifying sources from which information is sent and targets to whom it is sent, we analyze them for classified RBF tweets.
A K-means Improved CTGAN Oversampling Method for Data Imbalance Problem

CTGAN is a tabular data synthesis method for privacy preservation, which is used in this paper for data imbalance problem. This paper proposes a method for dealing with imbalanced data sets that combines K-means clustering and CTGAN to address the imbalanced distribution of minority class examples that result from oversampling with CTGAN. By conducting experiments with the LightGBM algorithm on home loan and online shopping datasets, it is demonstrated that the CTGAN method achieves superior learning results in f1-score and G-mean metrics compared to the interpolation-based oversampling technique represented by SMOTE. The preceding results indicate that by applying the method described in this paper to handle an imbalanced dataset, one can obtain a dataset with more examples, a more uniform distribution, and less overfitting while still satisfying the original dataset's probability distribution.
Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?

The triplet loss with semi-hard negatives has become the de facto choice for image-caption retrieval (ICR) methods that are optimized from scratch. Recent progress in metric learning has given rise to new loss functions that outperform the triplet loss on tasks such as image retrieval and representation learning. We ask whether these findings generalize to the setting of ICR by comparing three loss functions on two ICR methods. We answer this question negatively: the triplet loss with semi-hard negative mining still outperforms newly introduced loss functions from metric learning on the ICR task. To gain a better understanding of these outcomes, we introduce an analysis method to compare loss functions by counting how many samples contribute to the gradient w.r.t. the query representation during optimization. We find that loss functions that result in lower evaluation scores on the ICR task, in general, take too many (non-informative) samples into account when computing a gradient w.r.t. the query representation, which results in sub-optimal performance. The triplet loss with semi-hard negatives is shown to outperform the other loss functions, as it only takes one (hard) negative into account when computing the gradient.