Reviewerly/exHarmony huggingface dataset:
A screening assistance system for cervical cytology of squamous cell atypia based on a two‐step combined CNN algorithm with label smoothing

Abstract Background Although many cervical cytology diagnostic support systems have been developed, it is challenging to classify overlapping cell clusters with a variety of patterns in the same way that humans do. In this study, we developed a fast and accurate system for the detection and classification of atypical cell clusters by using a two‐step algorithm based on two different deep learning algorithms. Methods We created 919 cell images from liquid‐based cervical cytological samples collected at Sapporo Medical University and annotated them based on the Bethesda system as a dataset for machine learning. Most of the images captured overlapping and crowded cells, and images were oversampled by digital processing. The detection system consists of two steps: (1) detection of atypical cells using You Only Look Once v4 (YOLOv4) and (2) classification of the detected cells using ResNeSt. A label smoothing algorithm was used for the dataset in the second classification step. This method annotates multiple correct classes from a single cell image with a smooth probability distribution. Results The first step, cell detection by YOLOv4, was able to detect all atypical cells above ASC‐US without any observed false negatives. The detected cell images were then analyzed in the second step, cell classification by the ResNeSt algorithm, which exhibited average accuracy and F‐measure values of 90.5% and 70.5%, respectively. The oversampling of the training image and label smoothing algorithm contributed to the improvement of the system's accuracy. Conclusion This system combines two deep learning algorithms to enable accurate detection and classification of cell clusters based on the Bethesda system, which has been difficult to achieve in the past. We will conduct further research and development of this system as a platform for augmented reality microscopes for cytological diagnosis.
A Systematic Review on Model Watermarking for Neural Networks

Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered the intellectual property of the legitimate parties who have trained them, which makes their protection against stealing, illegitimate redistribution, and unauthorized application an urgent need. Digital watermarking presents a strong mechanism for marking model ownership and, thereby, offers protection against those threats. This work presents a taxonomy identifying and analyzing different classes of watermarking schemes for ML models. It introduces a unified threat model to allow structured reasoning on and comparison of the effectiveness of watermarking methods in different scenarios. Furthermore, it systematizes desired security requirements and attacks against ML model watermarking. Based on that framework, representative literature from the field is surveyed to illustrate the taxonomy. Finally, shortcomings and general limitations of existing approaches are discussed, and an outlook on future research directions is given.
Towards Zero-Shot Sign Language Recognition

This paper tackles the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign classes to recognize the instances of unseen sign classes. In this context, readily available textual sign descriptions and attributes collected from sign language dictionaries are utilized as semantic class representations for knowledge transfer. For this novel problem setup, we introduce three benchmark datasets with their accompanying textual and attribute descriptions to analyze the problem in detail. Our proposed approach builds spatiotemporal models of body and hand regions. By leveraging the descriptive text and attribute embeddings along with these visual representations within a zero-shot learning framework, we show that textual and attribute based class definitions can provide effective knowledge for the recognition of previously unseen sign classes. We additionally introduce techniques to analyze the influence of binary attributes in correct and incorrect zero-shot predictions. We anticipate that the introduced approaches and the accompanying datasets will provide a basis for further exploration of zero-shot learning in sign language recognition.
Blockchain-Based Secure Energy Trading With Mutual Verifiable Fairness in a Smart Community

This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model’s performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model’s results show satisfactory performance for privacy and security of the system.
Symmetries in quantum networks lead to no-go theorems for entanglement distribution and to verification techniques

Abstract Quantum networks are promising tools for the implementation of long-range quantum communication. The characterization of quantum correlations in networks and their usefulness for information processing is therefore central for the progress of the field, but so far only results for small basic network structures or pure quantum states are known. Here we show that symmetries provide a versatile tool for the analysis of correlations in quantum networks. We provide an analytical approach to characterize correlations in large network structures with arbitrary topologies. As examples, we show that entangled quantum states with a bosonic or fermionic symmetry can not be generated in networks; moreover, cluster and graph states are not accessible. Our methods can be used to design certification methods for the functionality of specific links in a network and have implications for the design of future network structures.
As-Rigid-as-Possible Shape Servoing

Shape servoing is the problem of controlling the shape of a deformable object via robotic manipulation, exploiting sensory feedback. In many shape servoing scenarios of practical interest, the object has a natural tendency to preserve local rigidity. Motivated by this observation, we propose to use the As-Rigid-As-Possible deformation model for shape servoing. Specifically, we propose a novel control scheme based on a deformation Jacobian computed from this model. The proposed scheme drives the object to a desired 3D shape using as feedback the object’s measured current 3D shape. The scheme is simple to implement, and it avoids some typical requirements in existing work: specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> we do not need to know the object’s mechanical deformation parameters, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> we do not use a Jacobian computed from data collected over a time window while the robots move. We test the proposed scheme in bi-arm shape servoing experiments with a variety of deformable objects of different material (paper, rubber, plastic). Tracking of the deformable object’s surface in 3D is performed in our experiments via an algorithm based on monocular vision. The experimental results validate the practicality of our scheme.
A Noise-Robust Self-Supervised Pre-Training Model Based Speech Representation Learning for Automatic Speech Recognition

Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.0 has a good robustness against the domain shift, while the noise robustness is still unclear. In this work, we therefore first analyze the noise robustness of wav2vec2.0 via experiments. We observe that wav2vec2.0 pre-trained on noisy data can obtain good representations and thus improve the ASR performance on the noisy test set, which however brings a performance degradation on the clean test set. To avoid this issue, in this work we propose an enhanced wav2vec2.0 model. Specifically, the noisy speech and the corresponding clean version are fed into the same feature encoder, where the clean speech provides training targets for the model. Experimental results reveal that the proposed method can not only improve the ASR performance on the noisy test set which surpasses the original wav2vec2.0, but also ensure a tiny performance decrease on the clean test set. In addition, the effectiveness of the proposed method is demonstrated under different types of noise conditions.
Microring resonators with external optical feedback for time delay reservoir computing

Microring resonators (MRRs) are a key photonic component in integrated devices, due to their small size, low insertion losses, and passive operation. While the MRRs have been established for optical filtering in wavelength-multiplexed systems, the nonlinear properties that they can exhibit give rise to new perspectives on their use. For instance, they have been recently considered for introducing optical nonlinearity in photonic reservoir computing systems. In this work, we present a detailed numerical investigation of a silicon MRR operation, in the presence of external optical feedback, in a time delay reservoir computing scheme. We demonstrate the versatility of this compact, passive device, by exploiting different operating regimes and solving computing tasks with diverse memory requirements. We show that when large memory is required, as it occurs in the Narma 10 task, the MRR nonlinearity does not play a significant role when the photodetection nonlinearity is involved, while the contribution of the external feedback is significant. On the contrary, for computing tasks such as the Mackey-Glass and the Santa Fe chaotic timeseries prediction, the MRR and the photodetection nonlinearities contribute both to efficient computation. The presence of optical feedback improves the prediction of the Mackey-Glass timeseries while it plays a minor role in the Santa Fe timeseries case.
3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation

Medical image segmentation has been so far achieving promising results with Convolutional Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer tends to discard important information such as positions. Moreover, CNNs are sensitive to rotation and affine transformation. Capsule network is a data-efficient network design proposed to overcome such limitations by replacing pooling layers with dynamic routing and convolutional strides, which aims to preserve the part-whole relationships. Capsule network has shown a great performance in image recognition and natural language processing, but applications for medical image segmentation, particularly volumetric image segmentation, has been limited. In this work, we propose 3D-UCaps, a 3D voxel-based Capsule network for medical volumetric image segmentation. We build the concept of capsules into a CNN by designing a network with two pathways: the first pathway is encoded by 3D Capsule blocks, whereas the second pathway is decoded by 3D CNNs blocks. 3D-UCaps, therefore inherits the merits from both Capsule network to preserve the spatial relationship and CNNs to learn visual representation. We conducted experiments on various datasets to demonstrate the robustness of 3D-UCaps including iSeg-2017, LUNA16, Hippocampus, and Cardiac, where our method outperforms previous Capsule networks and 3D-Unets. Our code is available at https://github.com/VinAIResearch/3D-UCaps.
COCOA

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labeled data, and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data. CL methods have been mostly developed for applications in computer vision and natural language processing where only a single sensor modality is used. A majority of pervasive computing applications, however, exploit data from a range of different sensor modalities. While existing CL methods are limited to learning from one or two data sources, we propose COCOA (Cross mOdality COntrastive leArning), a self-supervised model that employs a novel objective function to learn quality representations from multisensor data by computing the cross-correlation between different data modalities and minimizing the similarity between irrelevant instances. We evaluate the effectiveness of COCOA against eight recently introduced state-of-the-art self-supervised models, and two supervised baselines across five public datasets. We show that COCOA achieves superior classification performance to all other approaches. Also, COCOA is far more label-efficient than the other baselines including the fully supervised model using only one-tenth of available labeled data.
SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) since it exhibits high intra-class and low inter-class variances due to occlusions, deformation, illuminations, etc. Thus, an expressive feature representation describing global structural information is a key to characterize an object/ scene. To this end, we propose a method that effectively captures subtle changes by aggregating context-aware features from most relevant image-regions and their importance in discriminating fine-grained categories avoiding the bounding-box and/or distinguishable part annotations. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) approaches to include a simple yet effective relation-aware feature transformation and its refinement using a context-aware attention mechanism to boost the discriminability of the transformed feature in an end-to-end learning process. Our model is evaluated on eight benchmark datasets consisting of fine-grained objects and human-object interactions. It outperforms the state-of-the-art approaches by a significant margin in recognition accuracy.
Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method

Breast cancer can develop when breast cells replicate abnormally. It is now a worldwide issue that concerns people's safety all around the world. Every day, women die from breast cancer, which is especially common in the United States. Mammography, CT, MRI, ultrasound, and biopsies may all be used to detect breast cancer. Histopathology (biopsy) is often carried out to examine the image and discover breast cancer. Breast cancer detection at an early stage saves lives. Deep and machine learning models aid in the detection of breast cancer. The aim of the research work is to encourage medical research and the development of technology by employing deep learning models to recognize cancer cells that are small in size. For histological annotation and diagnosis, the proposed technique makes use of the BreCaHAD dataset. Color divergence is caused by differences in slide scanners, staining procedures, and biopsy materials. To avoid overfitting, we used data augmentation with 19 factors, such as scale, rotation, and gamma. The proposed hybrid dilation deep learning model is of two sorts. It illustrates edges, curves, and colors, and it improves the key traits. It utilizes dilation convolution and max pooling for multi-scale information. The proposed dilated unit processes the image and sends the processed features to the Alexnet, and it can recognize minute objects and thin borders by using the dilated residual expanding kernel model. An AUC of 96.15 shows that the new strategy is better than the old one.
Primitive quantum gates for an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>S</mml:mi><mml:mi>U</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>2</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> discrete subgroup: Binary tetrahedral

We construct a primitive gate set for the digital quantum simulation of the binary tetrahedral ($\mathbb{BT}$) group on two quantum architectures. This non-Abelian discrete group serves as a crude approximation to $SU(2)$ lattice gauge theory while requiring five qubits or one quicosotetrit per gauge link. The necessary basic primitives are the inversion gate, the group multiplication gate, the trace gate, and the $\mathbb{BT}$ Fourier transform over $\mathbb{BT}$. We experimentally benchmark the inversion and trace gates on ibm_nairobi, with estimated fidelities between 14%--55%, depending on the input state.
Blockchain application for central bank digital currencies (CBDC)

Central Bank Digital Currency (CBDC) is a digital version of domestic currency with a unit of account equivalent to its domestic currency. Blockchain or Distributed Ledger technology (DLT) can be used to implement CBDC to execute and settle peer-to-peer transactions. With the emergence of private money, such as cryptocurrencies and stablecoins, and the growing use of digital payments to lessen the global pandemic spread, CBDC is an active research area among central banks worldwide. Many central banks started their CBDC projects by building DLT proofs of concept (PoCs) to replicate wholesale payment systems and expand their investigation into other use cases, such as delivery versus Payment (DvP) and cross-border remittance. Many large economies like the United States have projects exploring CBDC. The People's Bank of China (PBoC), China Central Bank, has already started a pilot testing of their digital retail currency. This paper discusses the application of blockchain for CBDC by presenting CBDC projects by central banks. Moreover, this paper analyses issues, identify challenges and discusses future works in this rapidly evolving field.
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code.A binary, after disassembly, is expressed in an assembly language.This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a fruitful area focused on processing text of various natural languages.We notice that binary code analysis and NLP share many analogical topics, such as semantics extraction, classification, and code/text comparison.This work thus borrows ideas from NLP to address two important code similarity comparison problems.(I) Given a pair of basic blocks of different instruction set architectures (ISAs), determining whether their semantics is similar; and (II) given a piece of code of interest, determining if it is contained in another piece of code of a different ISA.The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection.Despite the evident importance of Problem I, existing solutions are either inefficient or imprecise.Inspired by Neural Machine Translation (NMT), which is a new approach that tackles text across natural languages very well, we regard instructions as words and basic blocks as sentences, and propose a novel cross-(assembly)-lingual deep learning approach to solving Problem I, attaining high efficiency and precision.Many solutions have been proposed to determine whether two pieces of code, e.g., functions, are equivalent (called the equivalence problem), which is different from Problem II (called the containment problem).Resolving the cross-architecture code containment problem is a new and more challenging endeavor.Employing our technique for crossarchitecture basic-block comparison, we propose the first solution to Problem II.We implement a prototype system INNEREYE and perform a comprehensive evaluation.A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability.The case studies applying the system demonstrate that our solution to Problem II is effective.Moreover, this research showcases how to apply ideas and techniques from NLP to largescale binary code analysis.
Federated pretraining and fine tuning of BERT using clinical notes from multiple silos.

Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years. However, in certain area like healthcare, accessing diverse large scale text data from multiple institutions is extremely challenging due to privacy and regulatory reasons. In this article, we show that it is possible to both pretrain and fine tune BERT models in a federated manner using clinical texts from different silos without moving the data.
Simple security proofs for continuous variable quantum key distribution with intensity fluctuating sources

Abstract Despite tremendous theoretical and experimental progress in continuous variable (CV) quantum key distribution (QKD), the security has not been rigorously established for most current continuous variable quantum key distribution systems that have imperfections. Among these imperfections, intensity fluctuation is one of the principal problems affecting security. In this paper, we provide simple security proofs for continuous variable quantum key distribution systems with intensity fluctuating sources. Specifically, depending on device assumptions in the source, the imperfect systems are divided into two general cases for security proofs. In the most conservative case, we prove the security based on the tagging idea, which is a main technique for the security proof of discrete variable quantum key distribution. Our proofs are simple to implement without any hardware adjustment for current continuous variable quantum key distribution systems. Also, we show that our proofs are able to provide secure secret keys in the finite-size scenario.