Reviewerly/exHarmony huggingface dataset:
Deep Learning Identifies HAT1 as a Morphological Regulator in Esophageal Squamous Carcinoma Cells through Controlling Cell Senescence

Histopathology is a critical approach for diagnostic tasks and precision treatment. However, histopathological deep learning tools for auto‐identification remain poorly developed. Meanwhile, the interpretation of the computer vision attention into a cellular process is less efficient in a systematic way. Herein, it is identified that histone acetyltransferase 1 (HAT1) is an aging‐associated gene in the esophagus epithelium by machine learning. An interpretable deep learning model is developed to distinguish morphological changes with varied HAT1 expressions in esophageal squamous carcinoma cells (ESCC). The gradient‐weighted class activation mapping and prediction score analysis reveal that the computer's vision focuses on the nuclear sizes of ESCC. The hypothesized phenotype is verified in HAT1‐knockdown ESCCs. Finally, HAT1 regulating cell senescence by affecting the H3K27 acetylation and E2F transcription factor 7 (E2F7) expression is shown. Herein, the feasibility and benefits of applying histopathological deep learning assistance systems in routine practice scenarios and connecting phenotype and genotype for further genetic research are suggested.
Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System

This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships.
Enhancing Visualization and Explainability of Computer Vision Models with Local Interpretable Model-Agnostic Explanations (LIME)

It is important that humans understand why machine learning models behave the way they do, especially in the field of computer vision. Having methods for visualizing which regions of an image are responsible for classifying or detecting objects can be a very useful resource. One popular algorithm for doing so is Local Interpretable Model-agnostic Explanations (LIME). We introduce Sub-model Stabilized and Sub grid Superimposed LIME (SubLIME), a technique for enhancing the stability of LIME-based visualizations as well as increasing the resolution of those explanations using a superimposition technique. Demonstrations are shown on the MNIST handwritten digit data set as well as a real-world data set for object detection in overhead infrared imagery.
Efficient Semi-Supervised Adversarial Training without Guessing Labels

Adversarial training has been proved to be the most effective defensive strategy to protect models from adversarial attacks. In the practical application scenario of adversarial training, besides labeled data, we also face an enormous amount of unlabeled data. However, existing adversarial training methods are naturally targeting supervised learning problems. To adapt to semi-supervised learning problems, they need to estimate labels for unlabeled data in advance, which inevitably degenerates the performance of the learned model due to the bias on the estimation of labels for unlabeled data. To mitigate this issue, in this paper, we propose a new semi-supervised adversarial training framework via maximizing AUCs which is also a minimax problem but treats the unlabeled samples as both positive and negative ones, which allows us to avoid guessing labels for unlabeled data. Quite naturally, the minimax problem can be solved via a traditional adversarial training algorithm by extending singly stochastic gradients to triply stochastic gradients, to adapt to the three (i.e. positive, negative, and unlabeled) data sources. To further accelerate the training procedure, we transform the minimax adversarial training problem into an equivalent minimization one based on the kernel perspective. For the minimization problem, we discuss scalable and efficient algorithms not only for deep neural networks but also for kernel support vector machines. Extensive experimental results show that our algorithms not only achieve better generalization performance against various adversarial attacks, but also enjoy efficiency and scalability when considered from the kernel perspective.
Coherent plug-and-play artifact removal: Physics-based deep learning for imaging through aberrations

Current image-correction frameworks for sensors that employ optically coherent detection attempt to estimate phase errors in the data, like those caused by aberrations, and simultaneously reconstruct digitally enhanced images. In practice, these frameworks struggle to account for the effects of speckle. To address this gap, we develop a novel image-correction framework referred to as Coherent Plug-and-Play Artifact Removal (CPnP-AR), which couples a neural-network despeckler with a physics-based measurement model. We also develop the experimental protocol needed to quantitatively evaluate performance relative to multiple state-of-the-art frameworks. The results show that CPnP-AR produces higher-quality images and more accurate estimates of the phase errors for a wide range of objects, specifically without the need for object-dependent parameter tuning. This improvement in overall robustness is a key step towards employing this novel image-correction framework for numerous applications of interest.
The Kernelized Taylor Diagram

Abstract This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to visualize similarities between populations. However, the Taylor diagram has several limitations such as not capturing non-linear relationships and sensitivity to outliers. To address such limitations, we propose the kernelized Taylor diagram. Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions. The kernelized Taylor diagram relates the maximum mean discrepancy and the kernel mean embedding in a single diagram, a construction that, to the best of our knowledge, have not been devised prior to this work. We believe that the kernelized Taylor diagram can be a valuable tool in data visualization.
Open-Set Object Detection Using Classification-Free Object Proposal and Instance-Level Contrastive Learning

Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this letter, we present Openset RCNN to address the challenging OSOD. To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories. To identify unknown objects in the second subtask, we propose to represent them using the complementary region of known categories in a latent space which is accomplished by a prototype learning network (PLN). PLN performs instance-level contrastive learning to encode proposals to a latent space and builds a compact region centering with a prototype for each known category. Further, we note that the detection performance of unknown objects can not be unbiasedly evaluated on the situation that commonly used object detection datasets are not fully annotated. Thus, a new benchmark is introduced by reorganizing GraspNet-1billion, a robotic grasp pose detection dataset with complete annotation. Extensive experiments demonstrate the merits of our method. We finally show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments.
Remote-sensing image data fusion processing technology based on multi-level fuzzy judgment

Remote sensing image technology is of great significance for dynamic management and monitoring of ground buildings. In order to improve the data fusion ability of remote sensing image of ground buildings, a data fusion method of remote sensing image of ground buildings based on multi-level fuzzy evaluation is proposed. This method constructs a remote sensing image acquisition model of ground buildings, and uses image enhancement methods to realize the gray information analysis and image enhancement of the remote sensing image rate of ground buildings. Finally, combining the remote sensing image data fusion method and the fuzzy region reconstruction method, it reconstructs the pixels of the dynamically changed ground buildings. The simulation results show that the remote sensing image data fusion accuracy of ground buildings is good, and the remote sensing feature extraction accuracy of ground buildings is high. The dynamic real-time monitoring of remote sensing image of ground buildings is realized.
Weaklier Supervised: Semi-automatic Scribble Generation Applied to Semantic Segmentation

With many applications regarding semantic segmentation arising, along with the advent of the Deep Semantic Segmentation Networks, the need for large labeled datasets has also largely increased. But labeling thousands of images can be very expensive and time-consuming. Approaches such as weak and semi supervision try do deal with this problem, but the first cannot deal with large datasets and the latter is hard to deal with semantic segmentation. Therefore, in this work we propose a combination of both to create a novel pipeline of weak supervision, with focus in satellite imagery, capable of dealing with large datasets. We propose a pipeline to automatically generate scribbles in images, requiring that the user only label 10% of the images in a given dataset, while a classifier deal with the remaining images. Along with that, we also propose a simple semantic segmentation pipeline, that uses only images with scribbles to train a network. Results show that performance is lower, but similar to a fully supervised pipeline.
Affective Analysis of Explainable Artificial Intelligence in the Development of Trust in AI Systems

The rise of Explainable Artificial Intelligence (XAI) has been a game changer for the growth of Artificial Intelligence (AI) powered systems. By providing human-level explanations, it systematically solves the most significant issue that AI faces: the black-box paradox realized from the complex hidden layers of deep and machine learning that powers it. Fundamentally, it allows users to learn how the AI operates and comes to decisions, thus enabling cognitive calibration of trust and subsequent reliance on the system. This conclusion has been supported by various research under different contexts and has motivated the development of newer XAI techniques. However, as human-computer interaction and social science studies suggest, these findings might be limited as the emotional component, which is also established from the interaction, was not considered. Emotions have long been determined to play a dominant role in decision-making as they can rapidly and unconsciously be infused in judgments. This insinuates an idea that XAI might facilitate trust calibration not solely because of the cognitive information it provides but of the emotions developed on the explanations. Considering this idea has not been explored, this study aims to examine the effects of emotions associated with the interaction with XAI towards trust, reliance, and explanation satisfaction. One hundred twenty-three participants were invited to partake in an online experiment anchored in an image classification testbed. The premise was that they were hired to classify different species of animals and plants, with an XAI-equipped image classification AI available to give them recommendations. At the end of each trial, they were tasked to rate their emotions upon interaction with the XAI, trust in the system, and satisfaction with the explanation. Reliance was measured based on whether they accepted the recommendations of AI. Results show that users who felt surprisingly happy and trusting emotions reported high trust, reliance, and satisfaction. On the other hand, users that developed fearfully dismayed and anxiously suspicious emotions have a significant negative relationship with satisfaction. Essentially, as supported by the post-interview, the study surfaced three critical findings on the affective functionality of XAI. First, emotions developed are mainly attributed to the design and overall composition rather than the information it carries. Second, trust and reliance can only be developed from positive emotions. Users might not trust and rely on an AI system even if it has a meaningful explanation if it develops negative emotions to the user. Third, explanation satisfaction can be triggered by both positive and negative emotions. The former is mainly from the presentation of XAI, while the latter is because of understanding the limitation of the AI.
Development of 3D Building Model Using Augmented Reality

Augmented reality (AR) is one of the interactive user interface technology which enhances the real physical world through digital elements. From industrial solutions to tourism, marketing and education, AR has become one of the most widely used technologies in recent years. Over the decade, the field of architecture has been evolving from the traditional practices. The visualization of architectural blueprints has always been a perplexing task for the general public and architects. Therefore, the developed AR application provides a 360-degree view of custom made 3D building models over the blueprints when scanned through an Android device. This makes it a cost-effective business tool for architects to show their clients what their future buildings will look like. This application involves the use of Marker-based AR Technology with Unity Real-Time Development Platform software and Vuforia.
The Impact of Virtual Reality Technology on Jordan’s Learning Environment and Medical Informatics among Physicians

The purpose of this study is to look into the components that affect the behavioral intention of Jordanians to use Virtual Reality Technology in the Learning Environment (VRTLE). For the educational and entertainment industries, virtual reality presents both obstacles and opportunities. The goal of this study is to determine the factors that influence VRTLE acceptance and to propose how such technology may be integrated into the educational setting of students and universities. A total of 60 students from private universities took part in the research. Among the participants, 63.3% were females and 36.7% were males. The modified UTAUT model used in this study only looked at the effects of four independent variables and two external factors, namely, acceptability (ACC) and usability (USA). Acceptability and usability have a substantial impact on student acceptance of VRTLE, according to the findings. Furthermore, as moderators, there is a major effect on both Awareness and Experience. This paper contributes significantly to the UTUAT model. It also tackles educational concerns about the transition to a virtual reality learning environment. As a result, it would be useful to look into other factors as well. As a result, a comprehensive empirical study should be designed to allow for the assessment of the effects of other variables as well.
End-to-End Voice Conversion with Information Perturbation

The ideal goal of voice conversion is to convert the source speaker’s speech to sound naturally like the target speaker while maintaining the linguistic content and the prosody of the source speech. However, current approaches are insufficient to achieve comprehensive source prosody transfer and target speaker timbre preservation in the converted speech, and the quality of the converted speech is also unsatisfied due to the mismatch between the acoustic model and the vocoder. In this paper, we leverage the recent advances in information perturbation and propose a fully end-to-end approach to conduct high-quality voice conversion. We first adopt information perturbation to remove speaker-related information in the source speech to disentangle speaker timbre and linguistic content and thus the linguistic information is subsequently modeled by a content encoder. To better transfer the prosody of the source speech to the target, we particularly introduce a speaker-related pitch encoder which can maintain the general pitch pattern of the source speaker while flexibly modifying the pitch intensity of the generated speech. Finally, one-shot voice conversion is set up through continuous speaker space modeling. Experimental results indicate that the proposed end-to-end approach significantly outperforms the state-of-the-art models in terms of intelligibility, naturalness, and speaker similarity.
Contrastive Learning for Simulation-to-Real Domain Adaptation of LiDAR Data

The accuracy of supervised deep learning algorithms is heavily dependent on the availability of annotated data and, in many cases, labeling this data accurately involves a very large outlay. Consequently, simulated data becomes an enticing option, since this data can be parameterized to resemble a real environment. However, the domain shift cannot be disregarded. To tackle this problem, we present a method which formulates an cloud-to-cloud translation as an image-to-image task from simulated to real scenarios. Our approach is capable of learning to extract the best features from the geometry of the environment, encode the information into a voxelized representation and generate a version similar to the one captured by a real sensor for complete scenarios. Our results on the CARLA to SemanticKITTI translation demonstrate that our method is able to provide adequate samples that help improve the accuracy, for selected categories of the SemanticKITTI validation set, of a semantic segmentation network trained only on real data.
The influence of target layout and target graphic type on searching performance based on eye-tracking technology

With the development of various intelligent technologies, the application of interactive interfaces is becoming more and more widespread, and the related researches conducted for interactive interfaces are also increasing. The purpose of this study was to explore the influence of icon layout location, icon graphic type, and icon layout method on users’ searching performance in interactive interfaces through eye-tracking technology. Participants were asked to perform search tasks for the search target (facet icon or linear icon) on each image. Thus, each trial consisted of a search task on a given image. In total, each participant had 36 trials to complete. Searching time, fixation duration, and fixation count were collected to evaluate the searching performance of participants. Results showed that when faced with familiar icons, whether the graphic type of icons was facet or linear did not affect the user’s experience, but when other factors of the interaction interface changed, facet icons provided a more stable experience for users. And compared to the rectangular layout, the circular layout method provided a more stable experience for users when the location of icons in the interactive interface changed, but icons located in the top half of the interactive interface were easier to find than those located in the bottom half, regardless of whether the layout was circular or rectangular. These results could be used in the layout and icon design of the interactive interfaces to facilitate their optimization.
Communication of Results of Educational Policies: Impact Levels of Educational Policies in the Digital Society

The levels of adherence to results in the implementation of public policies within educational communities can vary greatly depending on several factors: program coverage, funding level, the level of understanding of program goals, the duration of implementation, and the dissemination of results. In today’s digital society, the most relevant factor is precisely the communication of results, even when the way in which these are reached is overlooked. As a result, non-causal, high-impact relationships are installed in the collective consciousness. This article presents the results of a study that aims to measure the level of impact of the implementation of a public policy developed over two years in educational establishments in the Los Ríos Region of Chile, and it looks into the level of adherence to results three years after its implementation. The results explain that the differentiating factor is the type of dissemination of results, in direct correlation to the digital media used and the digital culture of the establishment, which allows to previously project the conditions of possibility for a type of adherence, even though we need larger scale measurements to determine with certainty this point of causality.
Lightweight Disconnector State Detection Model Based on Improved yolov5s

Aiming at the problems of complex environmental factors in substations, low accuracy, poor robustness and large amount of detection network parameters, an improved yolov5s lightweight network model is proposed for the location and state detection of six types of disconnector. Firstly, aiming at the problem of sample imbalance, use the albumentations library to perform data enhancement on the disconnector image; Secondly, the lightweight network MobileNetV3 is used for backbone feature extraction, and the Coordinate Attention (CA) mechanism is introduced to improve its inverted residual structure to more accurately locate the target; Change the PANet of the neck to BiFPN, a bidirectional feature pyramid, to enhance the feature transfer to solve the problem of the target being occluded; Finally, the weight coefficient $\alpha$ is introduced to improve the frame loss function and optimize the convergence effect of the model. The experimental results show that the improved network model obtained through iterative training can overcome environmental factors and accurately identify the different states of various disconnectors. The average detection accuracy (mAP@0.5) can reach 97.2%, which is 3.4% higher than the original model.