Reviewerly/exHarmony huggingface dataset:
A Loopback Network for Explainable Microvascular Invasion Classification

Microvascular invasion (MVI) is a critical factor for prognosis evaluation and cancer treatment. The current diagnosis of MVI relies on pathologists to manually find out cancerous cells from hundreds of blood vessels, which is time-consuming, tedious, and subjective. Recently, deep learning has achieved promising results in medical image analysis tasks. However, the unexplainability of black box models and the requirement of massive annotated samples limit the clinical application of deep learning based diagnostic methods. In this paper, aiming to develop an accurate, objective, and explainable diagnosis tool for MVI, we propose a Loopback Network (LoopNet) for classifying MVI efficiently. With the image-level category annotations of the collected Pathologic Vessel Image Dataset (PVID), LoopNet is devised to be composed binary classification branch and cell locating branch. The latter is devised to locate the area of cancerous cells, regular non-cancerous cells, and background. For healthy samples, the pseudo masks of cells supervise the cell locating branch to distinguish the area of regular non-cancerous cells and background. For each MVI sample, the cell locating branch predicts the mask of cancerous cells. Then the masked cancerous and non-cancerous areas of the same sample are input back to the binary classification branch separately. The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas. Experiment results show that the proposed LoopNet achieves 97.5% accuracy on MVI classification. Surprisingly, the proposed loopback mechanism not only enables LoopNet to predict the cancerous area but also facilitates the classification backbone to achieve better classification performance.
Learning from Unique Perspectives: User-aware Saliency Modeling

Everyone is unique. Given the same visual stimuli, people's attention is driven by both salient visual cues and their own inherent preferences. Knowledge of visual preferences not only facilitates understanding of fine-grained attention patterns of diverse users, but also has the potential of benefiting the development of customized applications. Nevertheless, existing saliency models typically limit their scope to attention as it applies to the general population and ignore the variability between users' behaviors. In this paper, we identify the critical roles of visual preferences in attention modeling, and for the first time study the problem of user-aware saliency modeling. Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model; (2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner; and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences. Experimental results on diverse stimuli, including naturalistic images and web pages, demonstrate the advantages of our method in capturing the distinct visual behaviors of different users and the general saliency of visual stimuli.
A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J- Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://github.com/ChanglongJiangGit/A2J-Transformer.
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids

Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions. The reliance on Multilayer Perceptrons (MLP), however, significantly limits speed in training and rendering. In this work, we propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without MLPs. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data such as color and semantic labels. To apply this representation to monocular scene reconstruction, we develop a scale calibration algorithm for fast geometric initialization from monocular depth priors. We apply differentiable volume rendering from this initialization to refine details with fast convergence. We also introduce efficient high-dimensional Continuous Random Fields (CRFs) to further exploit the semantic-geometry consistency between scene objects. Experiments show that our approach is 10× faster in training and 100× faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling

Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data. Rotation, as an important quantity in computer vision, graphics, and robotics, can exhibit many ambiguities when occlusion and symmetry occur and thus demands such probabilistic models. Though much progress has been made for NFs in Euclidean space, there are no effective normalizing flows without discontinuity or many-to-one mapping tailored for SO(3) manifold. Given the unique non-Euclidean properties of the rotation manifold, adapting the existing NFs to SO(3) manifold is non-trivial. In this paper, we propose a novel normalizing flow on SO (3) by combining a Mobius transformation-based coupling layer and a quaternion affine transformation. With our proposed rotation normalizing flows, one can not only effectively express arbitrary distributions on SO(3), but also conditionally build the target distribution given input observations. Extensive experiments show that our rotation normalizing flows significantly outperform the baselines on both unconditional and conditional tasks.
A Large-Scale Homography Benchmark

We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimation and image matching algorithms. The HEB dataset consists of 226 260 homographies and includes roughly 4M correspondences. The homographies link images that often undergo significant viewpoint and illumination changes. As applications of HEB, we perform a rigorous evaluation of a wide range of robust estimators and deep learning-based correspondence filtering methods, establishing the current state-of-the-art in robust homography estimation. We also evaluate the uncertainty of the SIFT orientations and scales w.r.t. the ground truth coming from the underlying homographies and provide codes for comparing uncertainty of custom detectors. The dataset is available at https://github.com/danini/homography-benchmark.
Toward RAW Object Detection: A New Benchmark and A New Model

In many computer vision applications (e.g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare. In this paper, we aim to achieve object detection on RAW sensor data, which naturally saves the HDR information from image sensors without extra equipment costs. We build a novel RAW sensor dataset, named ROD, for Deep Neural Networks (DNNs)-based object detection algorithms to be applied to HDR data. The ROD dataset contains a large amount of annotated instances of day and night driving scenes in 24-bit dynamic range. Based on the dataset, we first investigate the impact of dynamic range for DNNs-based detectors and demonstrate the importance of dynamic range adjustment for detection on RAW sensor data. Then, we propose a simple and effective adjustment method for object detection on HDR RAW sensor data, which is image adaptive and jointly optimized with the downstream detector in an end-to-end scheme. Extensive experiments demonstrate that the performance of detection on RAW sensor data is significantly superior to standard dynamic range (SDR) data in different situations. Moreover, we analyze the influence of texture information and pixel distribution of input data on the performance of the DNNs-based detector. Code and dataset will be available at https://gitee.com//mindspore/models/tree/master/research/cv/RAOD.
YOLOv4 Pedestrian Target Detection Based on Embedded Platform

Due to the dense number of pedestrians in complex environments and the predominance of small moving targets, the YOLOv4 algorithm has low detection accuracy and the large number of model parameters cannot be deployed on mobile devices with limited resource computing power. In this paper, we propose a YOLOv4 pedestrian target detection method based on the embedded platform Jetson Nano, using the ONNX (Open Neural Network Exchange) and TensorRT algorithms to optimize the YOLOv4 network model for inference; The trained network model is deployed on the Jetson Nano platform to build a target detection system; Using an SSD (Solid State Drive) with SATA3 interface to add a Swap function, which can ameliorate the storage structure of the Jetson Nano platform. The finally experimental results show that YOLOv4 pedestrian detection based on the embedded platform Jetson Nano achieves 94.39% mAP on the VOC extraction dataset, and the inference time is shorter, the IO throughput of the system is improved, and the robustness is enhanced.
Pseudonymisation of personal data as a technical and organisational measure for using data in the cloud

Today, social media, information management systems and smart devices are just a few examples of technologies that process large amounts of data every day. Although the General Data Protection Regulation requires data controllers to ensure that technical and organisational measures are in place, the focus of the Regulation is on data processing, such as the collection, storage and processing of personal data. Given that a very large part of the data of smart devices is nowadays stored directly in the cloud, it is necessary to analyse how to store and process this data in the cloud, which most often can also identify a specific person to meet the requirements of the Regulation, or to store it directly in a form that does not have to meet some of the data protection requirements. The work focussed on the storage and processing of personal data in the cloud and analysed whether the pseudonymisation of personal data can effectively eliminate the reference to the data subject, since such an elimination would imply that the cloud can be used without restrictions as well as in accordance with the requirements of data protection. Scientific articles, legislation, guidelines and other sources were analysed. The paper describes the possibilities of pseudonymisation as such, and also provides useful insights into storing personal data in pseudonymised form.
Conceptual and terminological apparatus of research into the pedagogical concept of education of future teachers in the conditions of digitalization of education

algorithm for their implementation.In the process of scientific research, the author identified the role of the advisor in the education process, which is to provide advice on choosing optional educational components of higher education, choosing a place of various internships (industrial, undergraduate or pedagogical) and work after graduation.It is quite interesting for the author to find out the functions of the facilitator.In the process of education, the functions of the facilitator are aimed at encouraging, motivating and stimulating students (pupils, students, research and teaching staff) to independently conduct benchmarking and ensure effective communication in a group or team to achieve intellectual comfort for each participant.The author also briefly describes the new educational program, which is curriculum, and the main types of education: formal and informal.According to the algorithm of studying the research problem, the table of definitions of the concept of "digitalization" is given and the concept of "digitalization of education" is specified.In the context of scientific research, it was found that the key concept of "pedagogical concept of education of future teachers in the digitalization of education" should be understood as the avant-garde idea of pedagogical science, which is implemented through the priority direction of the modernization of the educational process, the goal of which is the all-round flourishing of the personality of future teachers in the conditions of the large-scale process of introduction, integration and use of digital technologies and digitized information.
Effective and Privacy Protecting Cross Domain Deduplication in Cloud

In the recent years, the big data world has developed a cloud with strong storage management that can verify data integrity and maintain one data duplicate. The data deduplication (DD) issue has been solved by the development of numerous cloud auditing storage techniques, but these methods are weak and unable to withstand brute force attacks. In this paper, the proposed technique explore a three-tier cross-domain architecture and suggest a fast and private huge data deduplication in cloud storage. EPCDD achieves data availability as well as privacy preservation while resisting brute-force attacks. In order to provide better privacy protections than previous systems, this method take accountability into consideration. In terms of compute, communication and storage overheads and then it show that EPCDD performs better than currently used competitive strategies. However, since users and data owners may not be confident in cloud storage providers, data will likely be encrypted before outsourcing. Since different users encrypt identical data in different ways, deduplication efforts are complicated. As a result, to examine the performance of the proposed work is utilizing the java software.
A Blockchain Framework of Improved Security for Internet of Things Health Applications

In the recent years, the big data world has developed a cloud with strong storage management that can verify data integrity and maintain one data duplicate. The data deduplication (DD) issue has been solved by the development of numerous cloud auditing storage techniques, but these methods are weak and unable to withstand brute force attacks. In this paper, the proposed technique explore a three-tier cross-domain architecture and suggest a fast and private huge data deduplication in cloud storage. EPCDD achieves data availability as well as privacy preservation while resisting brute-force attacks. In order to provide better privacy protections than previous systems, this method take accountability into consideration. In terms of compute, communication and storage overheads and then it show that EPCDD performs better than currently used competitive strategies. However, since users and data owners may not be confident in cloud storage providers, data will likely be encrypted before outsourcing. Since different users encrypt identical data in different ways, deduplication efforts are complicated. As a result, to examine the performance of the proposed work is utilizing the java software.
Enrichment mechanism of critical metals and records of paleoenvironment in a polymetallic nodule from the Somali Basin

多金属结核富含Mn、Fe、Ni、Cu等元素以及Co和REY(稀土元素和钇)等关键金属, 是重要的大洋矿产资源。为了探究西北印度洋索马里海盆结核中关键金属的富集机制及古环境记录, 本文使用X射线衍射仪(XRD)分析了一个结核的矿物成分, 并通过激光剥蚀电感耦合等离子体质谱仪(LA-ICPMS)和电子探针(EPMA)原位分析了该结核的微区元素组成。结果表明, 研究区结核内层为柱状结构, 中间为掌状, 外层又变为柱状。结核主要由水成型富铁层构成, 但外层中还存在薄的(100~200μm)成岩-混合型富锰层。富铁层MnO平均含量为25.1%, FeO为30.4%, Co为0.26%, Ni为0.24%, Cu为0.09%, ΣREY为1981×10<sup>-6</sup>。富锰层MnO平均含量为52.0%, FeO为7.40%, Co为0.09%, Ni为2.45%, Cu为0.53%, ΣREY为719×10<sup>-6</sup>。富铁层主要由含铁水羟锰矿构成, 而富锰层主要由 Ⅰ 型布赛尔矿构成。Ni和Cu主要赋存于 Ⅰ型布赛尔矿, Co和REY则主要赋存于含铁水羟锰矿中, 并且Co存在一个 "拐点"。富锰矿物(Ⅰ型布赛尔矿)相对更富集HREE, 而含铁水羟锰矿中更富集LREE。从内到外, 结核中ΣREY呈下降趋势, REY的变化主要受Ce影响。结核Mn/Fe比值与<i>δ</i>Ce不相关, 但生长速率与<i>δ</i>Ce显著负相关, 表明生长速率可能是控制Ce异常的主要因素, 仅凭Ce异常不能区分结核微区的成因及氧化还原环境。根据Co经验公式估算, 索马里海盆结核形成年龄为~1.46Ma。柱状结构的开始形成可能与早更新世晚期的古海洋环境转型有关。而掌状结构开始生长于~0.6Ma, 中更新世气候转型之后风尘增加使得结核中Si和Al增加, 并稀释了其他成矿元素。结核富锰层可能生长于末次间冰期的亚氧化底层水环境, 且具有一定对称性和渐变性, 指示了氧化还原环境的逐渐改变和恢复。
A Basic Study on the Establishment of a New Perspective on the United States for Unification Education: Focusing on the U.S. policy on the Korean Peninsula

This paper is a basic study to establish a new perspective on the United States. I studied the implications of the U.S. policy on the Korean Peninsula for unification education. Of course, the United States will not oppose unification if a unified Korea in the future becomes a pro-American country under the influence of the United States. However, at present, we can see that the United States has no will to pay attention to peace and reunification on the Korean Peninsula as it is preoccupied with containing the rising China. The Korea’s national interests and the U.S. national interests are sure to be completely different. So it’s time for us to think deeply on whether we should unconditionally comply with the U.S. demands and pressures. Therefore, I argue that the contents of our subjective efforts must be included in the unification education policy. I do not think we should unconditionally follow the States’ strategy that pursue its national interests because we have comparatively little power or because the United States is an ally. If we do so, inter-Korean reconciliation would be getting difficult, and unification inevitably be far away. This is one that the new unification education policy includes. In other words, we should dissolve the inter-Korean relations and create a peaceful atmosphere on the Korean Peninsula. Based on this, we are obliged to create a peace community here. These contents must be included in unification education. However, in order to reduce unnecessary controversy, the new perspective on America in the unification education should be based on facts, not ideological biases.
Electric Throwing Wheel from Repurposed Bucket, Ceiling Fan Motor, and Sewing Machine Regulator for Pottery Demonstration

The cost of electric potters’ throwing wheel keeps soaring high even when throwing yet a course that firmly exists and is valued in the ceramic production process besides other forming techniques in art/design schools. The challenge herewith is the need to look for cheaper alternative electric throwing wheels through local fabrication to alleviate the impending high cost of imported ones and the general scarcity of potter’s wheels for demonstration in a simple ceramic studio setting by improvising available materials. The experimental-based methodology was deployed by sourcing and assembling the required components – an electric potter's wheel using a sewing machine regulator, iron bucket and basin, German plywood ½ inch, 1inch screw nails, bolts, and nuts, and 3 pin plug head. The principles of the ‘development cycle’ and the ‘rotational kinetic energy (RKE’ were adopted. The results of the study revealed that: the electric has the capacity speed of 200rovulation per minute (rpm) sufficient to center a-2kg of a well-prepared mass of leather hard clay - 50 rpm cylinders and bowls of various heights and widths up to 6" (height) and 5" (width) are viable; the repurposing of the conventional functions of the water bucket and fan the provision of air into an electric throwing wheel was successful. In conclusion, the study showed some success. The recommendations of the study include thus: further improvement as the focus of this one (project) is primarily to proffer a concept even though the topic is relevant to both product and service design; adoption of improvisation through repurposing for the mass construction of wheels as an alternative to the costly imported ones should be encouraged.
An Analysis of Multimedia Technology Application Based on Computer Networks

In recent years, with the rapid development of computer network technology, multimedia technology has emerged. Based on computer network, multimedia technology can meet people's needs for life and production, improve the efficiency of computer network operation, and improve the effect and scope of network services, which is of great significance. Based on this, this article analyzes computer technology and multimedia technology, proposes application strategies for computer network-based multimedia technology, analyzes key elements of computer network-based multimedia technology application, and studies simulation and simulation signal elements, graphic and sound elements. At the same time, it proposes the application approach of computer network-based multimedia technology, aiming to enhance the fusion effect of computer network technology and multimedia technology through the application of intelligent multimedia terminals, efficient integration and transmission of information, and the improvement of network audio-visual experience.