Reviewerly/exHarmony huggingface dataset:
An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques

This paper presents a fast and accurate system to determine the type of blood automatically based on image processing. Blood type determination is important in emergency situations, where there is a need for blood transfusion to save lives. The traditional blood determination techniques are performed manually by a specialist in medical labs, where the result requires a long time or may be affected by human error. This may cause serious consequences or even endanger people’s lives. The proposed approach performs blood determination in real-time with low cost using any available mobile device equipped with a camera. A total of 500 blood samples were processed in this study using different image matching techniques including oriented fast and rotated brief (ORB), scale invariant feature transform (SIFT), and speed-up robust feature (SURF). The evaluation results show that our proposed system, which adopts the ORB algorithm, is the fastest and the most accurate among the state-of-the-art systems. It can achieve an accuracy of 99.6% in an average time of 250 ms.
An Efficient Long Chinese Text Sentiment Analysis Method Using BERT-Based Models with BiGRU

There is thereby an urgent need but it is still a significant challenge to solve long Chinese t ext sentiment. BERT-based pre-trained language model (PLM) has been demonstrated to be the state-of-the-art approach for sentiment analysis. However, BERT can only process 510 tokens at a time, limiting the accuracy of sentiment analysis for long texts. Meanwhile, existing long text truncation methods for this BERT deficiency perform still weak in capture core sentiments. Aiming to better solve long text sentiment analysis, we propose a BERT-based fusion model. Firstly, we elaborately devise a new truncation method to gain four types of embeddings and resample the dataset to alter the imbalanced distribution of labels. Secondly, N BERT-based models are leveraged to joint learn the above four embeddings. Thirdly, we adopt a BiGRU network to fuse N BERT-based models and further utilizes attention mechanism to obtain effective core sentiments of the long Chinese texts. In the end, we employ three ensemble algorithms to optimize our model, improving Micro and Macro F1 by 1.68% and 1.53% respectively.
SIGTYP 2021 Shared Task: Robust Spoken Language Identification

While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task.For many low-resource and endangered languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have different domains than desired application scenarios, demanding a need for domain and speakerinvariant language identification systems.This year's shared task on robust spoken language identification sought to investigate just this scenario: systems were to be trained on largely single-speaker speech from one domain, but evaluated on data in other domains recorded from speakers under different recording circumstances, mimicking realistic low-resource scenarios.We see that domain and speaker mismatch proves very challenging for current methods which can perform above 95% accuracy in-domain, which domain adaptation can address to some degree, but that these conditions merit further investigation to make spoken language identification accessible in many scenarios.
Project Management of Smart University Development: Models and Tools

Our research is based on the problem of insufficient knowledge of the hybrid approach to smart university project management and the possibility of joint classical (cascade) and flexible methodology use. In contrast to the existing publications, here we regard the possibility of managing the transformation processes of a classical university into a smart university which is considered on the basis of project management methods and means. At the same time, the essence of the smart university structure and its individual components are not considered. The university is evaluated as a business structure and a management object of many internal innovative projects presented in a portfolio form. The main attention is focused on project management tools that allow managing the timing, cost and quality of the project portfolio. The novelty of the solutions and the practical significance of the publication consist in the author's models application for managing a smart university development based on the implemented project office and the evaluation model of integration. Expert methods and the apparatus of linear and dynamic programming are used for modeling. The proposed tools have been tested in the activities of three universities. The optimal ratio of practical application of project management integration methods is obtained. A significant reduction in the appearance of unidentified processes in project activities that initiate the appearance of risks and uncertainties has been achieved.
Nonlinear Multi-Image Encryption Scheme with the Reality-Preserving Discrete Fractional Angular Transform and DNA Sequences

A nonlinear multi-image encryption scheme is proposed by combining the reality-preserving discrete fractional angular transform with the deoxyribonucleic acid sequence operations. Four approximation coefficients of the four images are extracted by performing the two-dimensional lifting wavelet transform. Then, the four approximation coefficients are synthesized to generate a real-valued output with the reality-preserving discrete fractional angular transform. Finally, based on the deoxyribonucleic acid operation and the Logistic-sine system, the real-valued intermedium output will be encrypted to yield the final ciphertext image. To enhance the security of the image encryption algorithm, the initial value of the chaotic system is calculated by the 256-bit binary sequence, which is obtained by taking the statistics information of the plaintext images as the input of SHA-256. Deoxyribonucleic acid sequence operations, as nonlinear processes, could help to improve the robustness of the cryptosystem. Simulation results and security analysis demonstrate the effectiveness of the image encryption algorithm and the capability of withstanding various common attacks.
Exploring the Educational Affordances of Augmented Reality for Pupils with Moderate Learning Difficulties

The aim of this study was to examine the potential educational affordances of Augmented Reality (AR) for pupils with Moderate Learning Difficulties (MLD). Data was elicited from 25 teachers specialised in AR and Special Education. Qualitative data analysis revealed 10 affordances, related to the provision of in situ contextual information, individualised guidance, feedback and gamified experiences, as well as to learning object visualisation, interaction reinforcement, and the ability to obtain first-person view. Results also indicated three affordances not previously documented in the literature; namely, attention capturing, skill development efficiency and repeatability. These findings can contribute to a better understanding of the educational value of AR for pupils with MLD, in addition to providing researchers, AR developers and educators with information that was heretofore limited.
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

We consider training models on private data that is distributed across user devices. To ensure privacy, we add on-device noise and use secure aggregation so that only the noisy sum is revealed to the server. We present a comprehensive end-to-end system, which appropriately discretizes the data and adds discrete Gaussian noise before performing secure aggregation. We provide a novel privacy analysis for sums of discrete Gaussians. We also analyze the effect of rounding the input data and the modular summation arithmetic. Our theoretical guarantees highlight the complex tension between communication, privacy, and accuracy. Our extensive experimental results demonstrate that our solution is essentially able to achieve a comparable accuracy to central differential privacy with 16 bits of precision per value.
(Sub)Exponential advantage of adiabatic Quantum computation with no sign problem

We demonstrate the possibility of (sub)exponential quantum speedup via a quantum algorithm that follows an adiabatic path of a gapped Hamiltonian with no sign problem. The Hamiltonian that exhibits this speed-up comes from the adjacency matrix of an undirected graph whose vertices are labeled by n-bit strings, and we can view the adiabatic evolution as an efficient O(poly(n))-time quantum algorithm for finding a specific "EXIT" vertex in the graph given the "ENTRANCE" vertex. On the other hand we show that if the graph is given via an adjacency-list oracle, there is no classical algorithm that finds the "EXIT" with probability greater than exp(−nδ) using at most exp(nδ) queries for δ= 1/5 − o(1). Our construction of the graph is somewhat similar to the "welded-trees" construction of Childs et al., but uses additional ideas of Hastings for achieving a spectral gap and a short adiabatic path.
Background and Implications of China's Central Bank Digital Currency: E-CNY

The People’s Republic of China is a leading experimenter in central bank digital currencies (CBDCs). This paper explores the current background, deployment, features, potential impacts, challenges and legal concerns of China’s CBDC, the electronic yuan, or E-CNY. This paper explains the potential significance of what is known and not known about E-CNY with a particular focus on how E-CNY might fit into existing legal and economic systems, both within China and internationally. Section 1 discusses the development of E-CNY, including known technical features, institutional framework, and E-CNY’s intended role in China’s domestic monetary system. Section 2 explores China’s motivations for developing and deploying E-CNY most of which are focused on domestic concerns, though there are important international dynamics as well. Section 3 outlines a number of potential impacts of E-CNY, including domestic impacts on online payment platforms such as Alipay and WeChat Pay and commercial banks as well as impacts on international settlements and countries involved in the Belt and Road Initiative. Section 4 addresses key challenges and concerns around E-CNY, particularly as it pertains to information access and sharing, privacy protections, due process rights, and various technical issues. The last section examines E-CNY in the context of additional legal paradigms, including taxation, antitrust, anti-money laundering, and fraud prevention.
Image inpainting network for filling large missing regions using residual gather

Filling arbitrary missing regions with visually plausible contents is a challenging task. Existing methods have accomplished promising performance for inpainting regular or small defects. However, filling large continuous holes is still difficult due to out of step with the performance requirement for inferring large region contents. Considering that the model needs to predict more missing contents with less information when filling large holes, we first introduce a Residual Gather Module (RGM) for high prediction performance by fully taking advantage of residual learning and capturing feature information from a hierarchical distribution. To obtain both high-quality textures and deep model performance, we propose a novel image translation architecture. Different from U-Net, our skip method is semi-complete form. It eliminates losing details caused by downsampling and is capable of designing very deep generative models. Experiments demonstrate that the proposed method exhibits outstanding performance on filling large missing regions.
Isogeny-Based Key Compression Without Pairings

SIDH/SIKE-style protocols benefit from key compression to minimize their bandwidth requirements, but proposed key compression mechanisms rely on computing bilinear pairings. Pairing computation is a notoriously expensive operation, and, unsurprisingly, it is typically one of the main efficiency bottlenecks in SIDH key compression, incurring processing time penalties that are only mitigated at the cost of trade-offs with precomputed tables. We address this issue by describing how to compress isogeny-based keys without pairings. As a bonus, we also substantially reduce the storage requirements of other operations involved in key compression.
Integrating Multihub Driven Attention Mechanism and Big Data Analytics for Virtual Representation of Visual Scenes

Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only models objects but also infers their relationships. Existing works usually learn coarse global context when predicting relationships leading to excessive redundant information being considered. In this article, we first classify objects into different subgroups according to the degree of correlations with several hub objects. Then, we propose a multihub driven attention network (MHDANet) based on deep learning that drives the information to pass within the subgroups and forces objects to attend more to related objects. Consequently, MHDANet learns compact relation-aware features of visual scenes and predicts accurate and diverse relationships. Experimental results show that MHDANet achieves superb performance on scene graph generation on real-world datasets and especially alleviates the imbalance of predicted relationship categories.
A Stagewise Hyperparameter Scheduler to Improve Generalization

Stochastic gradient descent (SGD) augmented with various momentum variants (e.g. heavy ball momentum (SHB) and Nesterov's accelerated gradient (NAG)) has been the default optimizer for many learning tasks. Tuning the optimizer's hyperparameters is arguably the most time-consuming part of model training. Many new momentum variants, despite their empirical advantage over classical SHB/NAG, introduce even more hyperparameters to tune. Automating the tedious and error-prone tuning is essential for AutoML. This paper focuses on how to efficiently tune a large class of multistage momentum variants to improve generalization. We use the general formulation of quasi-hyperbolic momentum (QHM) and extend "constant and drop'', the widespread learning rate α scheduler where α is set large initially and then dropped every few epochs, to other hyperparameters (e.g. batch size b, momentum parameter β, instant discount factor ν). Multistage QHM is a unified framework which covers a large family of momentum variants as its special cases (e.g. vanilla SGD/SHB/NAG). Existing works mainly focus on scheduling α's decay, while multistage QHM allows additional varying hyperparameters such as b, β, and ν, and demonstrates better generalization ability than only tuning α. Our tuning strategies have rigorous justifications rather than a blind trial-and-error. We theoretically prove why our tuning strategies could improve generalization. We also show the convergence of multistage QHM for general nonconvex objective functions. Our strategies simplify the tuning process and beat competitive optimizers in test accuracy empirically.
High-Quality Face Sketch Synthesis via Geometric Normalization and Regularization

In this work, we propose a novel Generative Adversarial Network for generating a structure-consistent and texture-realistic sketch, conditioned on a face photo. To this end, we propose to boost the capacity of the generator via geometric normalization and regularization. Specially, we first propose an enhanced spatially-adaptive normalization module to modulate the activation, based on the semantic layout and encoding features of the input face. Besides, we use two regularization loss functions to minimize the structural divergence between a generated sketch and the corresponding face photo. Experimental results show that our proposed techniques significantly improve the quality of synthesized sketches, in terms of both structure and texture. Besides, our full model can generate high-quality sketches and significantly outperform previous state-of-the-arts, over a wide range of challenging data. We have made our code and results publicly available: http://aiart.live/genre/.
Lattice-Based Minimum-Distortion Data Hiding

Lattices have been conceived as a powerful tool for data hiding. While conventional studies and applications focus on achieving the optimal robustness versus distortion tradeoff, in some applications such as data hiding in medical/physiological signals, the primary concern is to achieve a minimum amount of distortion to the cover signal. In this paper, we revisit the celebrated quantization index modulation (QIM) scheme and propose a minimum-distortion version of it, referred to as MD-QIM. The crux of MD-QIM is to move the data point to only the boundary of the Voronoi region of the lattice point indexed by a message, which suffices for subsequent correct decoding. At any fixed code rate, the scheme achieves the minimum amount of distortion by sacrificing the robustness to the additive white Gaussian noise (AWGN) attacks. Simulation results confirm that our scheme significantly outperforms QIM in terms of mean square error (MSE), peak signal to noise ratio (PSNR) and percentage residual difference (PRD).
Human motion analysis and action scoring technology for sports training based on computer vision features

With the advent of the information age, computer-related application research has become more and more extensive, human motion analysis and action scoring based on computer vision have gradually become the focus of attention. In order to adapt to the development of the times and solve the problems related to the analysis of human motion, the experiment analyzed the similarity of eight common human movement behaviors, analyze the movement speed of men and women under sports training, and analyzed the accuracy of the human body motion recognition model in the two cases of the original gray data and the frame difference channel, finally, the denoising performance of four different algorithms of SMF, EMF, RAMF and median filter algorithm in digital image processing is analyzed. The final result shows that there is a big similarity between the same kind of human movement behavior, the accuracy rate of the frame difference channel human body recognition model is higher than that of the original gray data recognition model, and digital image processing median filter algorithm has good image denoising performance.
Optimal Communication-Computation Trade-Off in Heterogeneous Gradient Coding

Gradient coding allows a master node to derive the aggregate of the partial gradients, calculated by some worker nodes over the local data sets, with minimum communication cost, and in the presence of stragglers. In this paper, for gradient coding with linear encoding, we characterize the optimum communication cost for heterogeneous distributed systems with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arbitrary</i> data placement, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s \in \mathbb {N}$ </tex-math></inline-formula> stragglers and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a \in \mathbb {N}$ </tex-math></inline-formula> adversarial nodes. In particular, we show that the optimum communication cost, normalized by the size of the gradient vectors, is equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r-s-2a)^{-1}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r \in \mathbb {N}$ </tex-math></inline-formula> is the minimum number that a data partition is replicated. In other words, the communication cost is determined by the data partition with the minimum replication, irrespective of the structure of the placement. The proposed achievable scheme also allows us to target the computation of a polynomial function of the aggregated gradient matrix. It also allows us to borrow some ideas from approximation computing and propose an approximate gradient coding scheme for the cases when the repetition in data placement is smaller than what is needed to meet the restriction imposed on communication cost or when the number of stragglers appears to be more than the presumed value in the system design.
Pose-Graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM

The ability to decide if a solution to a pose-graph problem is globally optimal is of high significance for safety-critical applications. Converging to a local-minimum may result in severe estimation errors along the estimated trajectory. In this paper, we propose a graph neural network based on a novel implementation of a graph convolutional-like layer, called PoseConv, to perform classification of pose-graphs as optimal or sub-optimal. The operation of PoseConv required incorporating a new node feature, referred to as cost, to hold the information that the nodes will communicate. A training and testing dataset was generated based on publicly available bench-marking pose-graphs. The neural classifier is then trained and extensively tested on several subsets of the pose-graph samples in the dataset. Testing results have proven the model's capability to perform classification with 92 - 98% accuracy, for the different partitions of the training and testing dataset. In addition, the model was able to generalize to previously unseen variants of pose-graphs in the training dataset. Our method trades a small amount of accuracy for a large improvement in processing time. This makes it faster than other existing methods by up-to three orders of magnitude, which could be of paramount importance when using computationally-limited robots overseen by human operators.
Factored heterogeneous similarity model for recommendation with implicit feedback

Recommendation with implicit feedback such as "bought" in e-commerce sites and "like" in online-to-offline services is an important problem because of the abundance of users' online behaviors. Previous works for modeling users' implicit feedback mainly focus on designing some predefined similarity, learned similarity, or hybrid similarity between two users (or two items). However, these similarities either can not capture both the local and global relations among users (or items) simultaneously, or they are not easy to be tuned due to the tradeoff parameter in the linear hybridization. Moreover, information from one single group, either a user group or an item group, may not be sufficient for modeling users' preferences well. To this end, in this paper, we first propose a heterogeneous similarity to capture rich correlations among users and items via a concise integration, and then design a novel recommendation algorithm, i.e., pointwise factored heterogeneous similarity model (Poi-FHSM), in which we fully exploit the proposed similarity in a flexible pointwise preference learning paradigm. Finally, we conduct extensive empirical studies on five real-world datasets, and find that our Poi-FHSM performs better than those based on the predefined, learned and hybrid similarity, as well as the state-of-the-art deep learning-based methods, showcasing the effectiveness of our heterogeneous similarity and recommendation algorithm.