Reviewerly/exHarmony huggingface dataset:
An Intelligent and Trust UAV-Assisted Code Dissemination 5G System for Industrial Internet-of-Things

A large number of devices with communication and sensing capabilities are connected to the 5G network and facilitate distributed industrial Internet of Things (IIoT) applications. Those devices can update their program code by using software-defined technologies and upgrade functions without hardware replacement, thus, greatly facilitating the development of IIoT applications. Disseminating code safely to these devices is a pivotal issue. However, the existing methods rarely consider whether the code received by devices is integrated. Therefore, an unmanned aerial vehicle (UAV) assisted trustworthy code dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible mobile vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the code wait to be verified (CWV) directly from the selected devices (bedrock devices) as evidence. This scheme is a fundamental change from the previous passive, indirect and unverifiable trust evaluation schemes. In UTCD, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV as possible with the minimum cost. After that, based on the CWV, which is collected from bedrock devices, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible within the limited conditions of UAV. Finally, comprehensive experiments conduct on a real-life dataset demonstrate that the proposed scheme outperforms the existing schemes in terms of the efficiency and security of code dissemination.
The utility of color normalization for <scp>AI</scp>‐based diagnosis of hematoxylin and eosin‐stained pathology images

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between H&E images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Contestability For Content Moderation

Content moderation systems for social media have had numerous issues of bias, in terms of race, gender, and ability among many others. One proposal for addressing such issues in automated decision making is by designing for contestability, whereby users can shape and influence how decisions are made. In this study, we conduct a series of participatory design workshops with participants from communities that have experienced problems with social media content moderation in the past. Together with participants, we explore the idea of designing for contestability in content moderation and find that users' designs suggest three fruitful, practical avenues: adding representation, improving communication, and designing with compassion. We conclude with design recommendations drawn from participants' proposals, and reflect on the challenges that remain.
Curriculum-Based Meta-learning

Meta-learning offers an effective solution to learn new concepts with scarce supervision through an episodic training scheme: a series of target-like tasks sampled from base classes are sequentially fed into a meta-learner to extract common knowledge across tasks, which can facilitate the quick acquisition of task-specific knowledge of the target task with few samples. Despite its noticeable improvements, the episodic training strategy samples tasks randomly and uniformly, without considering their hardness and quality, which may not progressively improve the meta-leaner's generalization ability. In this paper, we present a Curriculum-Based Meta-learning (CubMeta) method to train the meta-learner using tasks from easy to hard. Specifically, the framework of CubMeta is in a progressive way, and in each step, we design a module named BrotherNet to establish harder tasks and an effective learning scheme for obtaining an ensemble of stronger meta-learners. In this way, the meta-learner's generalization ability can be progressively improved, and better performance can be obtained even with fewer training tasks. We evaluate our method for few-shot classification on two benchmarks - mini-ImageNet and tiered-ImageNet, where it achieves consistent performance improvements on various meta-learning paradigms.
Generative Theories of Interaction

Although Human–Computer Interaction research has developed various theories and frameworks for analyzing new and existing interactive systems, few address the generation of novel technological solutions, and new technologies often lack theoretical foundations. We introduce Generative Theories of Interaction , which draw insights from empirical theories about human behavior in order to define specific concepts and actionable principles, which, in turn, serve as guidelines for analyzing, critiquing, and constructing new technological artifacts. After introducing and defining Generative Theories of Interaction, we present three detailed examples from our own work: Instrumental Interaction, Human–Computer Partnerships, and Communities &amp; Common Objects. Each example describes the underlying scientific theory and how we derived and applied HCI-relevant concepts and principles to the design of innovative interactive technologies. Summary tables offer sample questions that help analyze existing technology with respect to a specific theory, critique both positive and negative aspects, and inspire new ideas for constructing novel interactive systems.
Can Language Models be Biomedical Knowledge Bases?

Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks.There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs).While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs.To this end, we create the BIOLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs.We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge.Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs.We hope that BIOLAMA can serve as a challenging benchmark for biomedical factual probing. 1
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in practice, but training segmentation models from limited supervision of scribbles is still challenging. To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix. For augmentation of supervision, CycleMix adopts the mixup strategy with a dedicated design of random occlusion, to perform increments and decrements of scribbles. For regularization of supervision, CycleMix intensifies the training objective with consistency losses to penalize inconsistent segmentation, which results in significant improvement of segmentation performance. Results on two open datasets, i.e., ACDC and MSCMRseg, showed that the proposed method achieved exhilarating performance, demonstrating comparable or even better accuracy than the fully-supervised methods. The code and expert-made scribble annotationsfor MSCMRseg are publicly available at https://github.com/BWGZK/CycleMix.
Improved PBFT algorithm for high-frequency trading scenarios of alliance blockchain

With the continuous development of blockchain technology, the application scenarios of alliance blockchain are also increasing. The consensus algorithm can achieve distributed consensus among nodes in the network. At present, the practical byzantine fault tolerance algorithm (PBFT) consensus algorithm commonly used in alliance blockchain requires all nodes in the network to participate in the consensus process. Experiments show that when the number of consensus nodes in the system exceeds 100, the bandwidth consumption and consensus delay will greatly increase, resulting in the inability of PBFT to be applied. In scenes with many nodes. How to improve the performance of alliance blockchains safely and efficiently has become an urgent problem to be solved at present. For the PBFT commonly used in alliance blockchains, there are some problems, such as large communication overhead, simple selection of master nodes, and inability to expand and exit nodes dynamically in the network. This paper proposes an improved algorithm tPBFT (trust-based practical Byzantine algorithm), which is suitable for high-frequency trading scenarios of consortium chains. By introducing a trust equity scoring mechanism between nodes in the network, the list of consensus nodes can be dynamically adjusted. tPBFT simplifies the pre-prepare stage of the PBFT consensus process, and realizes the verification of the hash transaction list in the reply stage, thereby reducing the interaction overhead between network nodes. Theoretical analysis and experiments show that when the number of nodes in the network is greater than 30, with the further increase of the number of nodes, the improved tPBFT algorithm has a relatively large performance in terms of node communication overhead, consensus efficiency and scalability outperforms the PBFT algorithm.
A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin

Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels

Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
All-optical computing based on convolutional neural networks

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-energy-consumption computing.Existing computing instruments are pre-dominantly electronic processors, which use electrons as information carriers and possess von Neumann architecture featured by physical separation of storage and processing.The scaling of computing speed is limited not only by data transfer between memory and processing units, but also by RC delay associated with integrated circuits.Moreover, excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials, building integrated photonic computing chips under traditional von Neumann architecture has been a challenge.Here, we report a new all-optical computing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed "weight modulators " to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving, multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers, multifarious logic gate operators, and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expanded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for onchip all-optical computing.
Prompt Tuning for Discriminative Pre-trained Language Models

Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks.However, to the best of our knowledge, existing works focus on prompt-tuning generative PLMs that are pre-trained to generate target tokens, such as BERT (Devlin et al., 2019).It is still unknown whether and how discriminative PLMs, e.g., ELECTRA (Clark et al., 2020), can be effectively prompt-tuned.In this work, we present DPT, the first prompt tuning framework for discriminative PLMs, which reformulates NLP tasks into a discriminative language modeling problem.Comprehensive experiments on text classification and question answering show that, compared with vanilla fine-tuning, DPT achieves significantly higher performance, and also prevents the unstable problem in tuning large PLMs in both full-set and low-resource settings.The source code and experiment details of this paper can be obtained from https: //github.com/thunlp/DPT.
Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism

Via counting the different kinds of white blood cells (WBCs), a good quantitative description of a person's health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs.In the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model.The proposed method has great potential for application in intelligent and accurate classification of WBCs.
Comparison of immersive and non-immersive virtual reality videos as substitute for in-hospital teaching during coronavirus lockdown: a survey with graduate medical students in Germany

As a consequence of the continued Covid-19 lockdown in Germany, in-hospital teaching for medical students was impossible. While lectures and other theoretical training were relatively easily converted into online sessions using platforms such as Moodle, Zoom and Microsoft Teams, this was not the case for practical skills and clinical interventions, such as bronchoscopy or colonoscopy. This study describes a workaround that was implemented at the Saarland University Hospital utilizing virtual reality equipment to convey the impressions of shadowing clinical procedures to the students without physical presence. To achieve this, 3D 180° videos of key clinical interventions of various internal medicine specialities were recorded, cut, and censored. The videos were uploaded to the e-learning YouTube channel of our institution and shared with the students via the private share function. The students could choose whether to use a VR-viewer to watch the videos immersively or to watch them without a viewer on a screen non-immersively. At the end of the course after 1 week, the students completed a questionnaire anonymously focusing on learning-success regarding the presented topics, a self-assessment, and an evaluation of the course. A total of 27 students watched the videos with a VR-Viewer and 74 watched non-immersively. Although the VR-viewer group self-assessed their expertise higher, there was no significant difference between the two groups in the learning-success test score. However, students in the VR-viewer group rated the learning atmosphere, comprehensibility, and overall recommendation of the course significantly higher. They also agreed significantly more to the statement, that they gained a better conception of the presented procedures, and that virtual reality might be an appropriate tool for online teaching. Video-assisted teaching facilitates learning and might be a valuable add-on to conventional teaching.Abbreviations: Covid-19: severe acute respiratory syndrome coronavirus 2; 3D: three-dimensional; 2D: Two-dimensional; VR: virtual reality.
MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling

Recent research work has developed powerful generative models (e.g., StyleGAN2) that can synthesize complete human head images with impressive photorealism, enabling applications such as photorealistically editing real photographs. While these models can be trained on large collections of unposed images, their lack of explicit 3D knowledge makes it difficult to achieve even basic control over 3D viewpoint without unintentionally altering identity. On the other hand, recent Neural Radiance Field (NeRF) methods have already achieved multiview-consistent, photorealistic renderings but they are so far limited to a single facial identity. In this paper, we propose a new Morphable Radiance Field (MoRF) method that extends a NeRF into a generative neural model that can realistically synthesize multiview-consistent images of complete human heads, with variable and controllable identity. MoRF allows for morphing between particular identities, synthesizing arbitrary new identities, or quickly generating a NeRF from few images of a new subject, all while providing realistic and consistent rendering under novel viewpoints. We train MoRF in a supervised fashion by leveraging a high-quality database of multiview portrait images of several people, captured in studio with polarization-based separation of diffuse and specular reflection. Here, we demonstrate how MoRF is a strong new step forwards towards generative NeRFs for 3D neural head modeling.