Reviewerly/exHarmony huggingface dataset:
Variational Depth Networks: Uncertainty-Aware Monocular Self-supervised Depth Estimation

Using self-supervised learning, neural networks are trained to predict depth from a single image without requiring ground-truth annotations. However, they are susceptible to input ambiguities and it is therefore important to express the corresponding depth uncertainty. While there are a few truly monocular and self-supervised methods modelling uncertainty, none correlates well with errors in depth. To this end we present Variational Depth Networks (VDN): a probabilistic extension of the established monocular depth estimation framework, MonoDepth2, in which we leverage variational inference to learn a parametric, continuous distribution over depth, whose variance is interpreted as uncertainty. The utility of the obtained uncertainty is then assessed quantitatively in a 3D reconstruction task, using the ScanNet dataset, showing that the accuracy of the reconstructed 3D meshes highly correlates with the precision of the predicted distribution. Finally, we benchmark our results using 2D depth evaluation metrics on the KITTI dataset.
FUSION: Fully Unsupervised Test-Time Stain Adaptation via Fused Normalization Statistics

Staining reveals the micro-structure of the aspirate while creating histopathology slides. Stain variation, defined as a chromatic difference between the source and the target, is caused by varying characteristics during staining, resulting in a distribution shift and poor performance on the target. The goal of stain normalization is to match the target's chromatic distribution to that of the source. However, stain normalisation causes the underlying morphology to distort, resulting in an incorrect diagnosis. We propose FUSION, a new method for promoting stain-adaption by adjusting the model to the target in an unsupervised test-time scenario, eliminating the necessity for significant labelling at the target end. FUSION works by altering the target's batch-normalization statistics and fusing them with source statistics using a weighting factor. The algorithm reduces to one of two extremes based on the weighting factor. Despite the lack of training or supervision, FUSION surpasses existing equivalent algorithms for classification and dense predictions (segmentation), as demonstrated by comprehensive experiments on two public datasets.
Using Whole Slide Image Representations from Self-supervised Contrastive Learning for Melanoma Concordance Regression

Although melanoma occurs more rarely than several other skin cancers, patients' long term survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high discordance rate among pathologists when distinguishing between melanoma and benign melanocytic lesions. A tool that provides potential concordance information to healthcare providers could help inform diagnostic, prognostic, and therapeutic decision-making for challenging melanoma cases. We present a melanoma concordance regression deep learning model capable of predicting the concordance rate of invasive melanoma or melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features corresponding to melanoma concordance were learned in a self-supervised manner with the contrastive learning method, SimCLR. We trained a SimCLR feature extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens originating from four distinct pathology labs. We trained a separate melanoma concordance regression model on 990 specimens with available concordance ground truth annotations from three pathology labs and tested the model on 211 specimens. We achieved a Root Mean Squared Error (RMSE) of $$0.28\pm 0.01$$ on the test set. We also investigated the performance of using the predicted concordance rate as a malignancy classifier, and achieved a precision and recall of $$0.85\pm 0.05$$ and $$0.61\pm 0.06$$ , respectively, on the test set. These results are an important first step for building an artificial intelligence (AI) system capable of predicting the results of consulting a panel of experts and delivering a score based on the degree to which the experts would agree on a particular diagnosis. Such a system could be used to suggest additional testing or other action such as ordering additional stains or genetic tests.
Weakly Supervised Invariant Representation Learning via Disentangling Known and Unknown Nuisance Factors

Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them. However, those two goals are actually complementary to each other so that we propose a framework to accomplish both of them simultaneously. We introduce a weakly supervised signal to learn disentangled representation which consists of three splits containing predictive, known nuisance and unknown nuisance information respectively. Furthermore, we incorporate contrastive method to enforce representation invariance. Experiments shows that the proposed method outperforms state-of-the-art (SOTA) methods on four standard benchmarks and shows that the proposed method can have better adversarial defense ability comparing to other methods without adversarial training.
A Deep Wavelet Network for High-Resolution Microscopy Hyperspectral Image Reconstruction

Microscopy hyperspectral imaging (MHSI) integrates conventional imaging with spectroscopy to capture images through numbers of narrow spectral bands, and has attracted much attention in histopathology image analysis. However, due to the current hardware limitation, the observed microscopy hyperspectral images (MHSIs) has much lower spatial resolution than the coupled RGB images. High-resolution MHSI (HR-MHSI) reconstruction by fusing low-resolution MHSIs (LR-MHSIs) with high-resolution (HR) RGB images can compensate for the information loss. Unfortunately, radiometrical inconsistency between the two modalities often causes spectral and spatial distortions which degrade quality of the reconstructed images. To address these issues, we propose a Deep Wavelet Network (WaveNet) based on spectral grouping and feature adaptation for HR-MHSI reconstruction. For preservation of spectral information, the band-group-wise adaptation framework exploits intrinsic spectral correlation and decompose MHSI reconstruction in separate spectral groups. In spatial domain, we design the mutual adaptation block to jointly fuse the wavelet and convolution features by wavelet-attention for high-frequency information extraction. Specifically, to effectively inject spatial details from HR-RGB to LR-MHSI, radiometry-aware alignment is conducted between the two modalities. We compare WaveNet with several state-of-the-art methods by their performance on a public pathological MHSI dataset. Experimental results demonstrate its efficacy and reliability under different settings.
Power Awareness in Low Precision Neural Networks

Existing approaches for reducing DNN power consumption rely on quite general principles, including avoidance of multiplication operations and aggressive quantization of weights and activations. However, these methods do not consider the precise power consumed by each module in the network and are therefore not optimal. In this paper we develop accurate power consumption models for all arithmetic operations in the DNN, under various working conditions. We reveal several important factors that have been overlooked to date. Based on our analysis, we present PANN (power-aware neural network), a simple approach for approximating any full-precision network by a low-power fixed-precision variant. Our method can be applied to a pre-trained network and can also be used during training to achieve improved performance. Unlike previous methods, PANN incurs only a minor degradation in accuracy w.r.t. the full-precision version of the network and enables to seamlessly traverse the power-accuracy trade-off at deployment time.
BiTAT: Neural Network Binarization with Task-Dependent Aggregated Transformation

Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model. However, extreme quantization (1-bit weight/1-bit activations) of compactly-designed backbone architectures (e.g., MobileNets) often used for edge-device deployments results in severe performance degeneration. This paper proposes a novel Quantization-Aware Training (QAT) method that can effectively alleviate performance degeneration even with extreme quantization by focusing on the inter-weight dependencies, between the weights within each layer and across consecutive layers. To minimize the quantization impact of each weight on others, we perform an orthonormal transformation of the weights at each layer by training an input-dependent correlation matrix and importance vector, such that each weight is disentangled from the others. Then, we quantize the weights based on their importance to minimize the loss of the information from the original weights/activations. We further perform progressive layer-wise quantization from the bottom layer to the top, so that quantization at each layer reflects the quantized distributions of weights and activations at previous layers. We validate the effectiveness of our method on various benchmark datasets against strong neural quantization baselines, demonstrating that it alleviates the performance degeneration on ImageNet and successfully preserves the full-precision model performance on CIFAR-100 with compact backbone networks.
Multi-modal Sign Language Spotting by Multi/One-Shot Learning

The sign spotting task aims to identify whether and where an isolated sign of interest exists in a continuous sign language video. Recently, it has received substantial attention since it is a promising tool to annotate large-scale sign language data. Previous methods utilized multiple sources of available supervision information to localize the sign actions under the RGB domain. However, these methods overlook the complementary nature of different modalities, i.e., RGB, optical flow, and pose, which are beneficial to the sign spotting task. To this end, we propose a framework to merge multiple modalities for multiple-shot supervised learning. Furthermore, we explore the sign spotting task with the one-shot setting, which needs fewer annotations and has broader applications. To evaluate our approach, we participated in the Sign Spotting Challenge, organized by ECCV 2022. The competition contains two tracks, i.e., multiple-shot supervised learning (MSSL for track 1) and one-shot learning with weak labels (OSLWL for track 2). In track 1, our method achieves around 0.566 F1-score and is ranked 2nd. In track 2, we are ranked the 1st, with a 0.6 F1-score. These results demonstrate the effectiveness of our proposed method. We hope our solution will provide some insight for future research in the community.
Free form deformation and symmetry constraint‐based multi‐modal brain image registration using generative adversarial nets

Abstract Multi‐modal brain image registration has been widely applied to functional localisation, neurosurgery and computational anatomy. The existing registration methods based on the dense deformation fields involve too many parameters, which is not conducive to the exploration of correct spatial correspondence between the float and reference images. Meanwhile, the unidirectional registration may involve the deformation folding, which will result in the change of topology during registration. To address these issues, this work has presented an unsupervised image registration method using the free form deformation (FFD) and the symmetry constraint‐based generative adversarial networks (FSGAN). The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters, thereby producing two deformation fields. Meanwhile, the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously. Besides, the symmetry constraint is utilised to construct the loss function, thereby avoiding the deformation folding. Experiments on BrainWeb, high grade gliomas, IXI and LPBA40 show that compared with state‐of‐the‐art methods, the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value, target registration error and computational efficiency.
A Govern Chain—Integration of Government Function with Blockchain Technology

Blockchain is a chain of blocks connected using encryption. Blockchains provide secure, indelible, and transparent transactions. There are decentralized applications of blockchain. Decentralized blockchains provide relief from single points of failure. Since the controls are also decentralized, the system is not dependent on any authority. There is a growing list of applications where the government is using blockchain technology in its interactions with the citizens. These applications are called Govern Chains. This chapter discusses the use of blockchains in public procurement, land registration, and electronic voting.
Optimized <scp>U‐Net</scp> convolutional neural network based breast cancer prediction for accuracy increment in big data

Summary Big data is data collected with huge dimensions and continuous exponential growth over time. In recent years, big data in health care has been commonly used to predict diseases. Breast cancer is one of the most common diseases and the secondary cause of death among women. Early diagnosis of breast cancer can prevent the risk of death. Few types of research have been done on breast cancer prediction on big data. However, the traditional prediction models have less efficient in terms of accuracy and error rate. The Optimized U‐Net Convolutional neural network (OU‐NetCNN) model is proposed in this paper to overcome these challenges. Hadoop is the storage system generated to store the datasets samples for big data. The data samples from two datasets, namely BreakHis and Kaggle (Breast Histopathology Images), are preserved in this storage system. The BreakHis data are considered for further processes like pre‐processing, segmentation, feature extraction, feature selection, and classification from the stored data samples. The noise is removed from the histopathological breast images in pre‐processing using the adaptive fast peer‐group filtering (AFPGF) approach. Then the morphological operations such as erosion and dilation are used to eradicate unwanted portions and the quality of the image is enhanced using the improved balance contrast enhancement method (IBCE). Next, edges of breast images are detected using an adaptive artificial ecosystem optimization (AAEO) algorithm‐based edge detection approach in the segmentation process. The features are extracted using the Spatial Gray Level Dependence Matrix (SGLDM) and the optimal features are selected by the modified selfish herd optimization (MSHO) algorithm. Finally, the selected features are fed into the proposed OU‐NetCNN model to classify the histopathology images as benign and malignant images. This hybridization minimizes the error rate, computational complexity and over fitting issues. The simulation analysis is performed in the PYTHON tool. Two datasets, namely BreakHis and Kaggle (Breast Histopathology Images), are considered. Some of the measures such as precision, sensitivity, accuracy and F‐measure are considered to evaluate the performance of the proposed model and compared with existing approaches.
Research on Federated Learning for Tactical Edge Intelligence

The gradual maturation of IoT and 5G technologies, as well as edge computing based on cloud-edge-end architecture and the rapid development of artificial intelligence, all provide a very good technical solution to the existing military technology change . At the same time, the information local warfare, equipment modularity, rapid deployment of the development. The network and application layers that connect battlefield information are associated with the efficient transmission of information as well as the effectiveness of information, so transformative improvements must be made to these two major components to suit current wartime and non-wartime tactical command requirements. Therefore, this paper will discuss the possibility of designing a bonding framework that can fully and effectively integrate the network and application layers by using federation learning techniques, and multimodal, multitasking customized edge federation features to expand the network layer and make the application layer intelligent.
Applying BBLT Incorporating Specific Domain Topic Summary Generation Algorithm to the Classification of Chinese Legal Cases

In response to the challenge that most existing case retrieval platforms can not effectively extract feature information of Chinese legal cases, and thus perform unsatisfactorily in terms of indicators such as relevance and accuracy of retrieval results. We propose to apply LBBT model incorporating domain-specific topic-based text summary generation algorithm to the classification of Chinese legal cases. In our proposed LBBT model, we use LDA to extract subject keywords for each type of legal documents separately, and then the TextRank algorithm is introduced to generate abstract for each legal document by combining the extracted subject words. BERT is used to vectorize the generated abstracts adopted as the inputs of BiLSTM to implement the task of classification on Chinese legal documents. The experimental result on the data set of 2500 single charge Chinese legal judgment documents obtained from CAIL2022 shows that our proposed LBBT model can effectively remove the redundant information in legal documents and improve the ability of LSTM to grasp the global key semantic information of long texts.
Web-Based Collaborative VR System Supporting VR Goggles for Radiation Therapy Setup Training

This paper proposes a new web-based collaborative VR system for the radiation therapy setup training that solved the following problems: in the authors’ previous system, the laser representation was not the same as the real one and the replay function was not enough. In addition, the system configuration was complicated because in the previous system, two sets of a goggle with a smartphone were used as output devices, and a game pad and a hand tracking device were used as input devices. Contrarily, the system configuration of the new version proposed in this paper has become simpler by only supporting VR goggles such as Meta Quest 2 with their hand tracking functionality. Medical students need to take the radiation therapy setup training before practicing such therapies. Because the radiation therapy devices are very expensive and dangerous, we need a training system without using the real devices. Evaluation by medical students proved that the new system proposed in this paper is useful for such the training.
GAMBARAN SANITASI OBJEK WISATA GEDUNG GONGGONG DAN TAMAN LAMAN BOENDA KOTA TANJUNGPINANG PROVINSI KEPULAUAN RIAU TAHUN 2022

ABSTRACGonggong Building and Boenda Gardens are two symbols that represent uniqueness of Tanjungpinang City. The Gonggong Building and Boenda Laman Park had a number of issues, according to the researchers' early findings, there were still water puddles in various locations, especially during the rainy season, and buildings that began to experience damage. This study aims to determine the description of the sanitation of the Gonggong Building &amp; Taman Laman Boenda tourist attraction, Tanjungpinang City, Riau Islands Province in 2022. The study was carried out in January 2022-July 2022. The objects of this study were environmental conditions, conditions of sanitation facilities, and conditions of buildings. The data of each variable were analyzed descriptively referring to the regulations of DITJEN P2PM and PLP No. 47 of 1999, presented in the form of tables and narratives. The results showed that the condition of the physical environment (80%) and the condition of the sanitation facilities (76.82%) had met the sanitation requirements of a tourist attraction, while the condition of the building (56.6%) did not meet the sanitation requirements of a tourist attraction because the total score was less than the minimum. 60% of the total score obtained. Based on this study, it was concluded that of the 3 variables examined, only 2 variables met the sanitation requirements of the tourist attraction in accordance with the regulations of the Directorate General of P2PM &amp; PLP No. 47 of 1999. hoped that Gonggong Building and Boenda Gardens can improve sanitation conditions including physical environmental conditions, sanitation facilities, and especially the condition of building facilities in order to meet the requirements in accordance with regulations.Keywords : Tourism Object, Public SanitationReferences : 28 References (1996-2020)1. : Student of Sanitation Study Program2. : Supervisor of Scientific Writing I3. : Supervisor of Scientific Writing II
Vision-Based Intelligent Grasping System for Moving Object

The grasping of moving objects is a widely studied application direction of manipulator. Because of the continuous change of the object position and shape, it is difficult to detect and track it. In this article, we propose an object detection and tracking algorithm based on the cascade of YOLOv4 and DSST. The algorithm can detect the object automatically and then track it steadily. It turns out that the algorithm realized accurate detection, real-time tracking and exact grasping of moving objects, and solved the problems of target loss in the tracking process. Through this algorithm and stereo vision method, we can further obtain the 3-D position of our target. Using Aubo-i5 manipulator and Intel RealSense D435 RGBD depth camera, we build a vision-based grasping system for moving object with the above algorithm, which achieved good real-time performance in the practical grasping experiments, and completed the accurate grasping of moving objects.
Overview of Online Track Planning for Cruise Missiles

Cruise missiles are the “pioneer officers” in high-tech warfare, and they are responsible for the important task of precision strikes against key enemy targets. This paper describes the framework structure of the cruise missile online trajectory planning problem, analyzes its problem model and constraint system, compares the research situation of this problem in recent years at home and abroad, and summarizes its optimization methods, pointing out its advantages and shortcomings. Finally, the development direction of the cruise missile online trajectory planning research field is proposed.
Method of Learning Dynamic Bayesian Network Parameter Based on DEQPK Algorithm

For the problem of DBN parameter learning from small sample data, a differential evolution based on qualitative prior knowledge (DEQPK) is proposed. Firstly, the feasible region defined by the parameter qualitative constrains is sampled by the Monte Carlo to obtain the qualitative prior knowledge (QPK); secondly, the search space is reduced according to the QPK, and then the DE algorithm is used for parameter learning; finally, the QPK and the results of the DE algorithm are fused to acquire the real parameters. In the simulation experiment, three algorithms are used for parameter learning. The results show that the DEQPK is the most precise as well as the least time-consuming. At the same time, the parameters learned by the DEQPK algorithm is substituted into DBN, and the situations of battlefield targets are assessed.