Reviewerly/exHarmony huggingface dataset:
A Survey on Depth Ambiguity of 3D Human Pose Estimation

Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose estimation (HPE). The recent strategies of disambiguating have brought significant progress and remarkable breakthroughs in the field of 3D human pose estimation (3D HPE). This survey extensively reviews the causes and solutions of the depth ambiguity. The solutions are systematically classified into four categories: camera parameter constraints, temporal consistency constraints, kinematic constraints, and image cues constraints. This paper summarizes the performance comparison, challenges, main frameworks, and evaluation metrics, and discusses some promising future research directions.
Comparing approaches for selection, development, and deployment of extended reality (XR) teaching applications: A case study at The University of Newcastle Australia

Abstract The use of extended reality (XR) technology in education offers many advantages for transferring knowledge and practical skills training at the higher education level. As a result, many Universities over the past 5 + years have undertaken pilot programs to both develop XR content and assess how to best implement it within existing teaching and learning systems. Unfortunately, very few of these efforts have included structured evaluation or documentation. As such, limited published evidence exists to inform processes and approaches that may assist or hinder broad scale implementation. This leads many Universities to unnecessarily commit significant time and resources to testing identical or similar approaches, resulting in repeated identification of the same or similar challenges. In response to this situation, The University of Newcastle, Australia decided to systematically document the approach for selection, development and implementation of four new virtual-reality (VR) teaching applications. The current paper contains a detailed intrinsic case study, outlining the process and critical elements that shaped the selection of suitable teaching content, software development, hardware solutions and implementation. Details are provided on how decisions were made, what components were considered helpful, challenges identified, and important lessons outlined. These findings will be useful to organisations and individuals as they look to develop pathways and processes to integrate XR technology, particularly within their existing training and educational frameworks.
Unsupervised Pre-Training for Detection Transformers

DEtection TRansformer (DETR) for object detection reaches competitive performance compared with Faster R-CNN via a transformer encoder-decoder architecture. However, trained with scratch transformers, DETR needs large-scale training data and an extreme long training schedule even on COCO dataset. Inspired by the great success of pre-training transformers in natural language processing, we propose a novel pretext task named random query patch detection in Unsupervised Pre-training DETR (UP-DETR). Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the input image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we find that freezing the CNN backbone is the prerequisite for the success of pre-training transformers. (2) To perform multi-query localization, we develop UP-DETR with multi-query patch detection with attention mask. Besides, UP-DETR also provides a unified perspective for fine-tuning object detection and one-shot detection tasks. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation. Code and pre-training models: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dddzg/up-detr</uri> .
The relationship between trust and attitudes towards the COVID-19 digital contact-tracing app in the UK

During the COVID-19 pandemic, digital contact-tracing has been employed in many countries to monitor and manage the spread of the disease. However, to be effective such a system must be adopted by a substantial proportion of the population; therefore, public trust plays a key role. This paper examines the NHS COVID-19 smartphone app, the digital contact-tracing solution in the UK. A series of interviews were carried out prior to the app’s release (n = 12) and a large scale survey examining attitudes towards the app (n = 1,001) was carried out after release. Extending previous work reporting high level attitudes towards the app, this paper shows that prevailing negative attitudes prior to release persisted, and affected the subsequent use of the app. They also show significant relationships between trust, app features, and the wider social and societal context. There is lower trust amongst non-users of the app and trust correlates to many other aspects of the app, a lack of trust could hinder adoption and effectiveness of digital contact-tracing. The design of technology requiring wide uptake, e.g., for public health, should embed considerations of the complexities of trust and the context in which the technology will be used.
The complexity of quantum circuit mapping with fixed parameters

A quantum circuit must be preprocessed before implementing on NISQ devices due to the connectivity constraint. Quantum circuit mapping (QCM) transforms the circuit into an equivalent one that is compliant with the NISQ device’s architecture constraint by adding SWAP gates. The QCM problem asks the minimal number of auxiliary SWAP gates, and is NP-complete. The complexity of QCM with fixed parameters is studied in the paper. We give an exact algorithm for QCM, and show that the algorithm runs in polynomial time if the NISQ device’s architecture is fixed. If the number of qubits of the quantum circuit is fixed, we show that the QCM problem is NL-complete by a reduction from the undirected shortest path problem. Moreover, the fixed-parameter complexity of QCM is W[1]-hard when parameterized by the number of qubits of the quantum circuit. We prove the result by a reduction from the clique problem. If taking the depth of the quantum circuits and the coupling graphs as parameters, we show that the QCM problem is still NP-complete over shallow quantum circuits, and planar, bipartite and degree bounded coupling graphs.
A survey of DeFi security: Challenges and opportunities

DeFi, or Decentralized Finance, is based on a distributed ledger called blockchain technology. Using blockchain, DeFi may customize the execution of predetermined operations between parties. The DeFi system use blockchain technology to execute user transactions, such as lending and exchanging. The total value locked in DeFi decreased from $200 billion in April 2022 to $80 billion in July 2022, indicating that security in this area remained problematic. In this paper, we address the deficiency in DeFi security studies. To our best knowledge, our paper is the first to make a systematic analysis of DeFi security. First, we summarize the DeFi-related vulnerabilities in each blockchain layer. Additionally, application-level vulnerabilities are also analyzed. Then we classify and analyze real-world DeFi attacks based on the principles that correlate to the vulnerabilities. In addition, we collect optimization strategies from the data, network, consensus, smart contract, and application layers. And then, we describe the weaknesses and technical approaches they address. On the basis of this comprehensive analysis, we summarize several challenges and possible future directions in DeFi to offer ideas for further research.
Are Translated Chinese Wuxia Fiction and Western Heroic Literature Similar? A Stylometric Analysis Based on Stylistic Panoramas1

This study investigates the extent to which English translations of Chinese Wuxia fiction and Western heroic literature in modern English are stylistically similar through stylometric analyses. It adds to literary translation research by highlighting possible stylistic connections between heroic literature in the East and that in the West, clues that may help understand the current reception of Wuxia translations. It also contributes to stylometric studies by introducing the stylistic panorama, a novel concept proposed to describe the stylistic picture of a (translated) text in a relatively holistic and functional way. Examining six English translations of Wuxia novels and 12 chivalric stories and heroic fantasies in modern English, the study finds that the Wuxia translations differ from the two Western subgenres in stylistic panoramas built by formal features (dispersion of word lengths, average sentence length, etc.), as well as the most frequent words and the most frequent word sequences. Such differences have foregrounded the unique stylistic features (richer Wuxia-specific vocabularies, shorter paragraph lengths, etc.) of these translations, which has contributed in part to their favorable reception among English-speaking readers. It is hoped that this study will encourage new applications for the concept of stylistic panoramas in future stylometric studies.
HPFL-CN: Communication-Efficient Hierarchical Personalized Federated Edge Learning via Complex Network Feature Clustering

Federated Learning (FL), a promising privacy-preserving distributed learning paradigm, has been extensively applied in urban environmental prediction tasks of Mobile Edge Computing (MEC) by training a global machine learning model without data sharing. However, it is hard for the shared global model to be well generalized among local edge servers, due to the statistical data heterogeneity, especially in real-world urban environmental data. Besides, the existing FL approaches may result in excessive communication and computation overhead due to the frequent transmission and aggregation of model parameters between massive edge servers and remote cloud servers. To address the above issues, we propose HPFL-CN, a novel communication-efficient Hierarchical Personalized Federated edge Learning framework via Complex Network feature clustering, aiming to cluster edge servers with similar environmental data distributions and then high-efficiently train personalized models for each cluster via hierarchical architecture. Specifically, HPFL-CN introduces Privacy-preserving Feature Clustering (PFC) to extract privacy-preserving low-dimensional feature representations of each edge server via mapping the environmental data to different complex network domains for clustering similar edge servers accurately. According to the clustering results of PFC, HPFL-CN further introduces an edge-mediator-cloud architecture for hierarchical model aggregation by Effective Hierarchical Scheduling (EHS), in which every mediator coordinates the training of edge servers within each cluster and periodically uploads model to cloud server for global model aggregation. Meanwhile, each mediator server would find a trade-off between cloud and edge models to realize personalization within clusters. Our extensive experiments on real-world datasets demonstrate the effectiveness and generalization of HPFL-CN, which outperforms other state-of-the-art FL methods regarding personalization performance and communication efficiency.
FPGA Acceleration of Fully Homomorphic Encryption over the Torus

Fully Homomorphic Encryption over the Torus (TFHE) is a promising approach for secure computing in cloud servers to perform computations directly on encrypted data. However, TFHE has much higher computation complexity than its unencrypted counterpart. In this work, we propose an FPGA accelerator for TFHE computations. We illustrate the effects of an optimization called bootstrapping key unrolling on the tradeoff between performance of bootstrapping and FPGA resource consumption. We customize the data layout of TFHE ciphertext to optimize data access and improve data reuse. We parameterize the FPGA design for TFHE bootstrapping, which can be configured to achieve high performance for different user-specified security requirements and given FPGA resources. We implement our design on a state-of-the-art FPGA and compare it with existing results on CPUs. Our implementation for TFHE bootstrapping achieves 216x improvement in throughput and 16.5x improvement in latency compared with the software baseline on a state-of-the-art CPU server.
Gated Multi-Attention Feedback Network for Medical Image Super-Resolution

Medical imaging technology plays a crucial role in the diagnosis and treatment of diseases. However, the captured medical images are often in a low resolution (LR) due to the limited imaging condition. Super-resolution (SR) technology is a feasible solution to enhance the resolution of a medical image without increasing the hardware cost. However, the existing SR methods often ignore high-frequency details, which results in blurred edges and an unsatisfying visual perception. In this paper, a gated multi-attention feedback network (GAMA) is proposed for medical image SR. Specifically, a gated multi-feedback network is employed as the backbone to extract hierarchical features. Meanwhile, a layer attention feature extraction (LAFE) module is introduced to refine the feature map. In addition, a channel-space attention reconstruction (CSAR) module is built to enhance the representational ability of the semantic feature map. Furthermore, a gradient variance loss is tailored as the regularization in guiding the model learning to regularize the model in generating a faithful high-resolution image with rich textures and sharp edges. The experiments verify the effectiveness of the proposed GAMA compared with the state-of-the-art approaches.
Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists’ confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.
Universal quantum computation with optical four-component cat qubits

We propose a teleportation-based scheme to implement a universal set of quantum gates with a four-component cat code, assisted by appropriate entangled resource states and photon-number-resolving detection. The four-component cat code features the ability to recover from single-photon loss. Here we propose a concrete procedure to correct the single-photon loss, including detecting the single-photon-loss event and recovering the initial states. By concatenating with standard qubit error-correcting codes, we estimate the loss threshold for fault-tolerant quantum computation and obtain a significant improvement over the two-component cat code.
Unsupervised feature selection via discrete spectral clustering and feature weights

Most of the existing unsupervised feature selection methods learn the cluster structure through spectral clustering, and then use various regression models to introduce the data matrix into the indicator matrix to obtain feature selection matrix. In these methods, the clustering indicator matrix is usually continuous value, which is not the best choice for the matrix in terms of its supervising role in feature selection. Based on this, unsupervised feature selection via discrete spectral clustering and feature weights (FSDSC) is proposed in this paper. First, FSDSC integrates regression model and spectral clustering in a unified framework for feature selection, and introduces a feature weight matrix, which intuitively expresses the importance of each feature with its diagonal elements. Compared with the common feature selection matrix that requires constraints such as sparse regular items, the appearance of the feature weight matrix reduces the complexity of the model and simplifies the calculation process of feature evaluation. Secondly, for the value of the indicators matrix, the spectral clustering is improved to obtain a discrete clustering indicator matrix, which provides clearer guidance information for feature selection. Finally, in order to avoid trivial solutions, the transformation matrix is constrained by orthogonal constraint. The combination of the orthogonal regression model and spectral clustering enables the algorithm to perform feature selection and manifold information learning at the same time, thereby preserving the local geometric structure of data. Compared with other excellent unsupervised feature selection algorithms, the experimental results prove the effectiveness of the proposed algorithm.
SAR2HEIGHT: Height Estimation from a Single SAR Image in Mountain Areas via Sparse Height and Proxyless Depth-Aware Penalty Neural Architecture Search for Unet

Height estimation from a single Synthetic Aperture Radar (SAR) image has demonstrated a great potential in real-time environmental monitoring and scene understanding. The projection of a single 2D SAR image from multiple 3D height maps is an ill-posed problem in mathematics. Although Unet has been widely used for height estimation from a single image, the ill-posed problem cannot be completely resolved, and it leads to deteriorated performance with limited training data. This paper tackles the problem by Unet with the help of supplementary sparse height information and proxyless neural architecture search (PDPNAS) for Unet. The sparse height, which can be accepted from low-resolution SRTM or LiDAR products, is included as the supplementary information and is helpful to improve the accuracy of the estimated height map, especially in mountain areas with a wide range of elevations. In order to explore the effect of sparsity of sparse height on the estimated height map, a parameterized method is proposed to generate sparse height with a different sparse ratio. In order to further improve the accuracy of the estimated height map from a single SAR imagery, PDPNAS for Unet is proposed. The optimal architecture for Unet can be searched by PDPNAS automatically with the help of a depth-aware penalty term p. The effectiveness of our approach is evaluated by visual and quantitative analysis on three datasets from mountain areas. The root mean squared error (RMSE) is reduced by 90.30% through observing only 0.0109% of height values from a low-resolution SRTM product. Furthermore, the RMSE is reduced by 3.79% via PDPNAS for Unet. The research proposes a reliable method for estimating height and an alternative method for wide-area DEM mapping from a single SAR image, especially for the implementation of real-time DEM estimation in mountain areas.
Proving UNSAT in Zero Knowledge

Zero-knowledge (ZK) protocols enable one party to prove to others that it knows a fact without revealing any information about the evidence for such knowledge. There exist ZK protocols for all problems in NP, and recent works developed highly efficient protocols for proving knowledge of satisfying assignments to Boolean formulas, circuits and other NP formalisms. This work shows an efficient protocol for the converse: proving formula unsatisfiability in ZK (when the prover posses a non-ZK proof). An immediate practical application is efficiently proving safety of secret programs.
Few-shot re-identification of the speaker by social robots

Nowadays advanced machine learning, computer vision, audio analysis and natural language understanding systems can be widely used for improving the perceptive and reasoning capabilities of the social robots. In particular, artificial intelligence algorithms for speaker re-identification make the robot aware of its interlocutor and able to personalize the conversation according to the information gathered in real-time and in the past interactions with the speaker. Anyway, this kind of application requires to train neural networks having available only a few samples for each speaker. Within this context, in this paper we propose a social robot equipped with a microphone sensor and a smart deep learning algorithm for few-shot speaker re-identification, able to run in real time over an embedded platform mounted on board of the robot. The proposed system has been experimentally evaluated over the VoxCeleb1 dataset, demonstrating a remarkable re-identification accuracy by varying the number of samples per speaker, the number of known speakers and the duration of the samples, and over the SpReW dataset, showing its robustness in real noisy environments. Finally, a quantitative evaluation of the processing time over the embedded platform proves that the processing pipeline is almost immediate, resulting in a pleasant user experience.
SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques

Automated source code generation is currently a popular machine-learning-based task. It can be helpful for software developers to write functionally correct code from a given context. However, just like human developers, a code generation model can produce vulnerable code, which the developers can mistakenly use. For this reason, evaluating the security of a code generation model is a must. In this paper, we describe SecurityEval, an evaluation dataset to fulfill this purpose. It contains 130 samples for 75 vulnerability types, which are mapped to the Common Weakness Enumeration (CWE). We also demonstrate using our dataset to evaluate one open-source (i.e., InCoder) and one closed-source code generation model (i.e., GitHub Copilot).