Reviewerly/exHarmony huggingface dataset:
Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases

The establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs.Hematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses.The accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of "fixed" workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance.Most features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists' preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists' display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard "unlocked" vendor-agnostic displays for clinical digital pathology workflow rather than purchasing "locked" and more expensive displays that are part of a digital pathology system.
A new control scheme for an aerodynamic-surface-free tilt-rotor convertible UAV

Abstract This paper is concerned with the design and control of a tilt-rotor UAV (TRUAV), with the purpose of simplifying the mechanical structure and transition handling. Previous works on TRUAVs control consider a different controller for each flight mode. Furthermore, two sets of actuators are used: propellers in the VTOL mode, and aerodynamic surfaces in the fixed-wing mode. In this work, a new design that does not contain any control surfaces is proposed. A new control strategy is also presented to accommodate this particularity. Unlike previous works, this strategy uses a single controller to handle both flight phases, making the transition between the two phases no longer an issue. Furthermore, such a characteristic makes the drone capable of following any flight trajectory, which is vital for applications such as the tracking of a ground target. Simulations, conducted on the full nonlinear model of the famous Zagi flying wing drone, showed the effectiveness of the proposed control strategy in tracking a typical trajectory profile with a smooth transition from VTOL to fixed-wing mode.
Penerapan Metode Design Thinking Pada Rubbish Picker Application

Rubbish is a residual material from human daily needs. Rubbish has become a terrible problem throughout Indonesia. Poor waste management can lead to the destruction of waste which has great potential to cause health, environmental and social problems. Rubbish that accumulates and is not managed properly becomes a source of disease transmission and gives off a stinking and pungent odor. This study aims to design a ‘Rubbish Picker App’ aimed at housewives, food distributor companies so that they can help manage accumulated waste in a clean and timely manner. The method used for this research is the Design Thinking Method. From the results of the prototype testing that has been given, there were responses in the form of 75% of respondents agreeing that this designed application can assist in waste management. And 25% of other respondents responded neutrally. Therefore, it is hoped that this application can be further developed to become an even more perfect application.
Support vector machine in ultrahigh-dimensional feature space

AbstractClassification and feature selection play an important role in knowledge discovery in high-dimensional data. Although penalized Support Vector Machine (SVM) is among the most powerful methods for classification and automatic feature selection in high-dimensional feature space, it is not directly applicable to ultrahigh-dimensional cases, wherein the number of features far exceeds the sample size. In this paper, we suggest an efficient two-step method for simultaneous classification and identifying important features in the setting of ultrahigh-dimensional models. Specifically, we first develop an independence screening procedure to reduce the dimensionality of the feature space to a moderate scale, and then penalized support vector machine is applied to the dimension-reduced feature space to select important features further and estimate the coefficients, via a (penalized) model fit. Implementation of the suggested two-step method is not limited by the dimensionality of the models and entails much less computational cost. Numerical examples and a real data analysis are used to demonstrate the finite sample performance of our proposal.Keywords: Classificationdimension reductionhigh-dimensional datascreeningsparsity AcknowledgementsWe would like to thank the anonymous reviewers and associate editor for their valuable comments and suggestions which significantly improved the presentation of the paper and led to more details.Disclosure statementNo potential conflict of interest was reported by the author(s).
Augmented Reality in Industrial Manufacturing - Identification of Application Areas for AR-Based Quality Control/Assembly Based on Technology Suitability

Augmented Reality (AR) has started to be gradually utilized in industrial manufacturing. However, the gap between novel stage and industrial AR-based applications must be comprehensively solved. In this study, the task technology fit (TTF) and method of time measurement (MTM) are applied and put together to create a comprehensive map demonstrating the relationship between manufacturing tasks and AR-solution features. In addition, this map will support exploring the suitability of AR-based solutions and identifying the application areas of AR for industrial manufacturing, primarily focused on quality control, metrology and assembly tasks. By considering both the viewpoint of developers/users and the scientific principles underlying manufacturing tasks in this comprehensive map, the usability and effectiveness of the final AR solution are also ensured at an early stage of AR-based application development.As a result, this paper provides a useful system to utilize AR capabilities for more complex-multistep tasks in a standardized way. Thus, more potential development and improvement of AR-based solutions for quality 4.0, virtual metrology, and complex assemblies in an industrial manufacturing context could be holistically established and built.
Exploiting Temporal–Spatial Feature Correlations for Sequential Spacecraft Depth Completion

The recently proposed spacecraft three-dimensional (3D) structure recovery method based on optical images and LIDAR has enhanced the working distance of a spacecraft’s 3D perception system. However, the existing methods ignore the richness of temporal features and fail to capture the temporal coherence of consecutive frames. This paper proposes a sequential spacecraft depth completion network (S2DCNet) for generating accurate and temporally consistent depth prediction results, and it can fully exploit temporal–spatial coherence in sequential frames. Specifically, two parallel convolution neural network (CNN) branches were first adopted to extract the features latent in different inputs. The gray image features and the depth features were hierarchically encapsulated into unified feature representations through fusion modules. In the decoding stage, the convolutional long short-term memory (ConvLSTM) networks were embedded with the multi-scale scheme to capture the feature spatial–temporal distribution variation, which could reflect the past state and generate more accurate and temporally consistent depth maps. In addition, a large-scale dataset was constructed, and the experiments revealed the outstanding performance of the proposed S2DCNet, achieving a mean absolute error of 0.192 m within the region of interest.
Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution

Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are designed to restore high-frequency image details and enhance imaging resolution through the use of rapid and lightweight network architectures. Existing SISR methodologies face the challenge of striking a balance between performance and computational costs, which hinders the practical application of SISR methods. In response to this challenge, the present study introduces a lightweight network known as the Spatial and Channel Aggregation Network (SCAN), designed to excel in image super-resolution (SR) tasks. SCAN is the first SISR method to employ large-kernel convolutions combined with feature reduction operations. This design enables the network to focus more on challenging intermediate-level information extraction, leading to improved performance and efficiency of the network. Additionally, an innovative 9 × 9 large kernel convolution was introduced to further expand the receptive field. The proposed SCAN method outperforms state-of-the-art lightweight SISR methods on benchmark datasets with a 0.13 dB improvement in peak signal-to-noise ratio (PSNR) and a 0.0013 increase in structural similarity (SSIM). Moreover, on remote sensing datasets, SCAN achieves a 0.4 dB improvement in PSNR and a 0.0033 increase in SSIM.
Foreground Segmentation-Based Density Grading Networks for Crowd Counting

Estimating object counts within a single image or video frame represents a challenging yet pivotal task in the field of computer vision. Its increasing significance arises from its versatile applications across various domains, including public safety and urban planning. Among the various object counting tasks, crowd counting is particularly notable for its critical role in social security and urban planning. However, intricate backgrounds in images often lead to misidentifications, wherein the complex background is mistaken as the foreground, thereby inflating forecasting errors. Additionally, the uneven distribution of crowd density within the foreground further exacerbates predictive errors of the network. This paper introduces a novel architecture with a three-branch structure aimed at synergistically incorporating hierarchical foreground information and global scale information into density map estimation, thereby achieving more precise counting results. Hierarchical foreground information guides the network to perform distinct operations on regions with varying densities, while global scale information evaluates the overall density level of the image and adjusts the model's global predictions accordingly. We also systematically investigate and compare three potential locations for integrating hierarchical foreground information into the density estimation network, ultimately determining the most effective placement.Through extensive comparative experiments across three datasets, we demonstrate the superior performance of our proposed method.
Application of Reading Skills to Children with Special Needs through NaraMedia at SMPN 8 Tamban

This study aims to describe the application of accompanying teachers in developing reading skills for children with needs special at SMPN 8 Tamban. This qualitative research study uses guidelines for interviews, observation, and documentation. The subject of the study is two students in Class VII. The data collection techniques are interviews, observation, tests, and documentation. The research results show that the teacher's efforts in developing reading skills with a manner special and guided through NaraMedia. The application of reading skills to children with special needs as a whole by teachers has used simple or short utterances (consisting of one or two words). The teacher's approach to the three children slowly made them know, understand, and understand when reading.
Resampling-Detection-Network-Based Robust Image Watermarking against Scaling and Cutting

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness.
Design premise and diary study exploring felt senses as data for self-reflection

AbstractWhile mobile and wearable applications leverage biosensing and self-tracking technologies to promote healthy lifestyles through data-based self-reflection, there have been criticisms that they perpetuate the dualistic view that separates mind and body. We examine theoretical premises and design approaches for self-reflection using personal data, and compare them with self-reflection accounts collected from a diary study, in which participants are encouraged to reflect through Focusing on their felt sensations. Based on the analysis of how participants notice, express, question, and respond to their felt senses, we investigate how self-awareness and self-knowledge can be derived from elusive felt sensations and expand the scope of personal data and design for self-reflection. Our findings reveal the gap between theory and practice to design for self-reflection at the limits of biosensing and self-tracking applications, and lead to alternative design propositions for harnessing human senses as personal data for self-reflection.Keywords: self-reflectionself-knowledgepersonal datafelt sensedesign for self-reflection Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsHeekyoung JungHeekyoung Jung is an Associate Professor of Interaction Design in the School of Design at University of Cincinnati. She earned her BS and MS in Industrial Design at Korea Advanced Institute of Science and Technology and completed her PhD in Human-Computer Interaction Design at Indiana University Bloomington. She specializes in User Interface, Interaction and Experience Design of information and product systems, and conducts practice-based, reflective, and exploratory design studies to understand and augment human experience with emerging technologies. She has worked on industry and academic collaborative projects to design information and communication systems for workplace wellbeing at warehouse and airport, patient data registry and visualization, and mobile and wearable self-care.
Blind Quality Assessment of Images Containing Objects of Interest

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results. In this study, we propose an end-to-end model, named DETR-IQA (detection transformer image quality assessment), which extends the capability to perform object detection and blind image quality assessment (IQA) simultaneously by adding IQA heads comprising simple multi-layer perceptrons at the top of the DETRs (detection transformers) decoder. Using IQA heads, DETR-IQA carried out blind IQAs based on the weighted fusion of the distortion degree of the region of objects of interest and the other regions of the image; the predicted quality score of images containing objects of interest was generally greater than that of images without objects of interest. Currently, the subjective quality score of all public datasets is in accordance with the distortion of images and does not consider objects of interest. We manually extracted the images in which the five predefined classes of objects were the main contents of the largest authentic distortion dataset, KonIQ-10k, which was used as the experimental dataset. The experimental results show that with slight degradation in object detection performance and simple IQA heads, the values of PLCC and SRCC were 0.785 and 0.727, respectively, and exceeded those of some deep learning-based IQA models that are specially designed for only performing IQA. With the negligible increase in the computation and complexity of object detection and without a decrease in inference speeds, DETR-IQA can perform object detection and IQA via multi-tasking and substantially reduce the workload.
RTAD: A Real-Time Animal Object Detection Model Based on a Large Selective Kernel and Channel Pruning

Animal resources are significant to human survival and development and the ecosystem balance. Automated multi-animal object detection is critical in animal research and conservation and ecosystem monitoring. The objective is to design a model that mitigates the challenges posed by the large number of parameters and computations in existing animal object detection methods. We developed a backbone network with enhanced representative capabilities to pursue this goal. This network combines the foundational structure of the Transformer model with the Large Selective Kernel (LSK) module, known for its wide receptive field. To further reduce the number of parameters and computations, we incorporated a channel pruning technique based on Fisher information to eliminate channels of lower importance. With the help of the advantages of the above designs, a real-time animal object detection model based on a Large Selective Kernel and channel pruning (RTAD) was built. The model was evaluated using a public animal dataset, AP-10K, which included 50 annotated categories. The results demonstrated that our model has almost half the parameters of YOLOv8-s yet surpasses it by 6.2 AP. Our model provides a new solution for real-time animal object detection.
Multi-Attention Infused Integrated Facial Attribute Editing Model: Enhancing the Robustness of Facial Attribute Manipulation

Facial attribute editing refers to the task of modifying facial images by altering specific target facial attributes. Existing approaches typically rely on the combination of generative adversarial networks and encoder–decoder architectures to tackle this problem. However, current methods may exhibit limited accuracy when dealing with certain attributes. The primary objective of this research is to enhance facial image modification based on user-specified target facial attributes, such as hair color, beard removal, or gender transformation. During the editing process, it is crucial to selectively modify only the regions relevant to the target attributes while preserving the details of other unrelated facial attributes. This ensures that the editing results appear more natural and realistic. This study introduces a novel approach called MAGAN (Combining GRU Structure and Additive Attention with AGU—Adaptive Gated Units). Moreover, a discriminative attention mechanism is introduced to automatically identify key regions in the input images that are relevant to facial attributes. This mechanism concentrates attention on these regions, enhancing the model’s ability to accurately capture and analyze subtle facial attribute features. The method incorporates external attention within the convolutional layers of the encoder–decoder architecture, facilitating the modeling of linear complexity across image regions and implicitly considering correlations among all data samples. By employing discriminative attention in the discriminator, the model achieves more precise attribute editing. To evaluate the effectiveness of MAGAN, experiments were conducted on the CelebA dataset. The average precision of facial attribute generation in images edited by our model stands at 91.83%. PSNR and SSIM for reconstructed images are 32.52 and 0.957, respectively. In comparison with existing methodologies (AttGAN, STGAN, MUGAN), noteworthy enhancements have been achieved in the domain of facial attribute manipulation.
Opto-Electronic Hybrid Network Based on Scattering Layers

Owing to the disparity between the computing power and hardware development in electronic neural networks, optical diffraction networks have emerged as crucial technologies for various applications, including target recognition, because of their high speed, low power consumption, and large bandwidth. However, traditional optical diffraction networks and electronic neural networks are limited by long training durations and hardware requirements for complex applications. To overcome these constraints, this paper proposes an innovative opto-electronic hybrid system that combines optical diffraction networks with electronic neural networks. Using scattering layers to replace the diffraction layers in traditional optical diffraction networks, this hybrid system circumvents the challenging training process associated with diffraction layers. Spectral outputs of the optical diffraction network were processed using a simple backpropagation neural network, forming an opto-electronic hybrid network exhibiting exceptional performance with minimal data. For three-class target recognition, this network attains a classification accuracy of 93.3% within a substantially short training time of 9.2 s using only 100 data samples (training: 70 and testing: 30). Furthermore, it demonstrates exceptional insensitivity to position errors in scattering elements, enhancing its robustness. Therefore, the proposed opto-electronic hybrid network presents substantial application prospects in the fields of machine vision, face recognition, and remote sensing.