Reviewerly/exHarmony huggingface dataset:
Bidirectional Transformer GAN for Long-term Human Motion Prediction

The mainstream motion prediction methods usually focus on short-term prediction, and their predicted long-term motions often fall into an average pose, i.e., the freezing forecasting problem [ 27 ]. To mitigate this problem, we propose a novel Bidirectional Transformer-based Generative Adversarial Network (BiTGAN) for long-term human motion prediction. The bidirectional setup leads to consistent and smooth generation in both forward and backward directions. Besides, to make full use of the history motions, we split them into two parts. The first part is fed to the Transformer encoder in our BiTGAN while the second part is used as the decoder input. This strategy can alleviate the exposure problem [ 37 ]. Additionally, to better maintain both the local (i.e., frame-level pose) and global (i.e., video-level semantic) similarities between the predicted motion sequence and the real one, the soft dynamic time warping (Soft-DTW) loss is introduced into the generator. Finally, we utilize a dual-discriminator to distinguish the predicted sequence at both frame and sequence levels. Extensive experiments on the public Human3.6M dataset demonstrate that our proposed BiTGAN achieves state-of-the-art performance on long-term (4 s ) human motion prediction, and reduces the average error of all actions by 4%.
Proto-Quipper with Dynamic Lifting

Quipper is a functional programming language for quantum computing. Proto-Quipper is a family of languages aiming to provide a formal foundation for Quipper. In this paper, we extend Proto-Quipper-M with a construct called dynamic lifting , which is present in Quipper. By virtue of being a circuit description language, Proto-Quipper has two separate runtimes: circuit generation time and circuit execution time. Values that are known at circuit generation time are called parameters , and values that are known at circuit execution time are called states . Dynamic lifting is an operation that enables a state, such as the result of a measurement, to be lifted to a parameter, where it can influence the generation of the next portion of the circuit. As a result, dynamic lifting enables Proto-Quipper programs to interleave classical and quantum computation. We describe the syntax of a language we call Proto-Quipper-Dyn. Its type system uses a system of modalities to keep track of the use of dynamic lifting. We also provide an operational semantics, as well as an abstract categorical semantics for dynamic lifting based on enriched category theory. We prove that both the type system and the operational semantics are sound with respect to our categorical semantics. Finally, we give some examples of Proto-Quipper-Dyn programs that make essential use of dynamic lifting.
A Three-Dimensional ResNet and Transformer-Based Approach to Anomaly Detection in Multivariate Temporal–Spatial Data

Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method. TDRT can automatically learn the multi-dimensional features of temporal-spatial data to improve the accuracy of anomaly detection. Using the TDRT method, we were able to obtain temporal-spatial correlations from multi-dimensional industrial control temporal-spatial data and quickly mine long-term dependencies. We compared the performance of five state-of-the-art algorithms on three datasets (SWaT, WADI, and BATADAL). TDRT achieves an average anomaly detection F1 score higher than 0.98 and a recall of 0.98, significantly outperforming five state-of-the-art anomaly detection methods.
Latin Dances Reloaded: Improved Cryptanalysis Against Salsa and ChaCha, and the Proposal of Forró

In this paper, we present 4 major contributions to ARX ciphers and in particular to the Salsa/ChaCha family of stream ciphers:
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a deep neural network to approximate any Hölder smooth function up to a given approximation error in Hölder norms in such a way that all weights of this neural network are bounded by 1. The latter feature is essential to control generalization errors in many statistical and machine learning applications.
DAC4SH: A Novel Data Access Control Scheme for Smart Home Using Smart Contracts

Smart home (s-home) is an important Internet of Things (IoT)-based application in improving the living environment. Many cryptography-based protection schemes have been proposed to guarantee data confidentiality in s-home. Access control is a promising method to protect generated data from unauthorized access, and it is also urgently needed in s-home. Current centralized access control schemes are not excellent in security and performance. For instance, it still faces the problems of a single point of failure, low reliability, and poor scalability. This study proposes a decentralized and reliable access control scheme for s-home using smart contracts, named DAC4SH. To be specific, this proposed framework consists of an access policy management contract (APMC), a data attribute management contract (DAMC), a subject attribute management contract (SAMC), and a data access control contract (DACC) for realizing the fine-grained data access control. Meanwhile, we record all the access activities into the immutable distributed ledgers for auditing. To verify the feasibility of our DAC4SH, we construct an Ethereum-based prototype system and evaluate the performance of DAC4SH in terms of computational and communication costs. According to the experimental results, we can conclude that the performance of DAC4SH is appropriate.
Smart contract testing

Blockchain technologies have found important and concrete applications in the real world. Active solutions leverage Smart Contracts for the management of cryptocurrencies, sensitive data, and other valuable assets. One of the core objectives of blockchain-oriented software engineering (BOSE) is ensuring that Smart Contracts receive adequate pre-release testing to guarantee the deployment of reliable code. However, the novelty and the complexity of the blockchain environment pose new challenges to the validation and verification of Smart Contract based software. In this paper, we analyze the aforementioned challenges to foster the discussion on the specific topic of Smart Contract testing and identify relevant research directions.
Unsupervised anomaly detection with generative adversarial networks in mammography

Abstract Breast cancer is a common cancer among women, and screening mammography is the primary tool for diagnosing this condition. Recent advancements in deep-learning technologies have triggered the implementation of research studies via mammography. Semi-supervised or unsupervised methods are often used to overcome the limitations of supervised learning, such as manpower and time, for labeling in clinical situations where abnormal data are significantly lacking. Accordingly, we proposed a generative model that uses a state-of-the-art generative network (StyleGAN2) to create high-quality synthetic mammographic images and an anomaly detection method to detect breast cancer on mammograms in unsupervised methods. The generation model was trained via only normal mammograms and breast cancer classification was performed via anomaly detection using 50 breast cancer and 50 normal mammograms that did not overlap with the dataset for generative model learning. Our generative model has shown comparable fidelity to real images, and the anomaly detection method via this generative model showed high sensitivity, demonstrating its potential for breast cancer screening. This method could differentiate between normal and cancer-positive mammogram and help overcome the weakness of current supervised methods.
Directional predictability from central bank digital currency to cryptocurrencies and stablecoins

This paper investigates the interaction and the directional predictability between the central bank digital currencies (CBDCs) and the major cryptocurrencies and stablecoins during the period between 17 May, 2019–31 December, 2021. To this aim, we employ the "Cross-Quantilogram” model, to examine how and whether the traditional digital currencies react to the CBDC uncertainty and attention shocks. Our findings suggest that CBDC uncertainty index is negatively related to cryptocurrency and stablecoin returns. Furthermore, the CBDC attention index is negatively associated with Bitcoin, Ethereum, XPR and Terra USD, however, it is positively related to Tether, Binance, USD Coin and Dai. Our results are useful for regulators, investors and policy makers, to understand and assess the potential effect of CBDC adoption news on the volatility of the stablecoins and traditional cryptos.
Job Resource and Job Performance Among Physicians in the Jordanian Health Sector: The mediating role of Job satisfaction

&#x0D; &#x0D; &#x0D; &#x0D; Purpose: Individuals, groups, and organizations are all affected by job performance. Many challenges face the organization such as job performance across the globe as well as in Jordan. The present study aims to investigate how job resources affect a hospital physician’s performance, as well as the role of job satisfaction in mediating these relationships.&#x0D; Theoretical framework: The key idea behind this research will be the focus on job demands-resources theory (JD-R). However, a lack of studies that explore the JDR, and job performance in the Jordan context.&#x0D; methodology: The information was gathered through a questionnaire of 346 physicians who work at Jordan's Ministry of Health. In this paper, structural equation modeling (SEM) was used to assess and evaluate the offered hypotheses.&#x0D; Findings: The study found that five core elements of job resources (skill variety, task identity, performance feedback, autonomy, and job security) have a significant positive correlation with job satisfaction, while one core element (task significance) was insignificant. Furthermore, job resources were shown to have a significant indirect relationship with job performance, with job satisfaction acting as a mediator.&#x0D; Research, Practical &amp; Social implications: The findings of this paper show that the JDR can accurately forecast physicians. The findings can be used as a foundation for future research in this field. The findings of the study will help the Jordanian government design policies to support and encourage physicians in the current work environment.&#x0D; Originality: This paper is a ground-breaking effort to see if job demands resources may be used as a theoretical framework to predict physician performance.&#x0D; &#x0D; &#x0D; &#x0D;
Toward explainable AI-empowered cognitive health assessment

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR , a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.
Tuning XGBoost by Planet Optimization Algorithm: An Application for Diabetes Classification

Recent years have seen an increase in instances of diabetes mellitus, a metabolic condition that if left untreated can severely decrease the quality of life, and even cause the death of those affected. Early diagnostics and treatment are vital for improving the outcome of treatment. This work proposes a novel artificial intelligence-based (AI) approach to diabetes classification. Due to the ability to process large amounts of data at a relatively quick rate with admirable performance, the XGBoost approach is used. However, despite many advantages, the large number of control parameters presented by this algorithm makes the process of tuning delicate and complex. To this end, the planet optimization algorithm (POA) is tasked with selecting the optimal XGBoost hyperparameters so as to achieve the best possible classification outcomes. In order to demonstrate the improvements achieved, a comparative analysis is given that presents the proposed approach alongside other contemporary algorithms addressing the same classification task. The attained results clearly demonstrate the superiority of the proposed approach.
Injecting the BM25 Score as Text Improves BERT-Based Re-rankers

In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as a token in the middle of the input of the cross-encoder re-ranker. It was shown in prior work that interpolation between the relevance score of lexical and BERT-based re-rankers may not consistently result in higher effectiveness. Our idea is motivated by the finding that BERT models can capture numeric information. We compare several representations of the BM25 score and inject them as text in the input of four different cross-encoders. We additionally analyze the effect for different query types, and investigate the effectiveness of our method for capturing exact matching relevance. Evaluation on the MSMARCO Passage collection and the TREC DL collections shows that the proposed method significantly improves over all cross-encoder re-rankers as well as the common interpolation methods. We show that the improvement is consistent for all query types. We also find an improvement in exact matching capabilities over both BM25 and the cross-encoders. Our findings indicate that cross-encoder re-rankers can efficiently be improved without additional computational burden and extra steps in the pipeline by explicitly adding the output of the first-stage ranker to the model input, and this effect is robust for different models and query types.
From 2D to 3D

Mid-air text entry on virtual keyboards suffers from the lack of tactile feedback, which brings challenges to both tap detection and input prediction. In this paper, we explored the feasibility of single-finger typing on virtual QWERTY keyboards in mid-air. We first conducted a study to examine users' 3D typing behavior on different sizes of virtual keyboards. Results showed that the participants perceived the vertical projection of the lowest point on the keyboard during a tap as the target location and inferring taps based on the intersection between the finger and the keyboard was not applicable. Aiming at this challenge, we derived a novel input prediction algorithm that took the uncertainty in tap detection into a calculation as probability, and performed probabilistic decoding that could tolerate false detection. We analyzed the performance of the algorithm through a full-factorial simulation. Results showed that the SVM-based probabilistic touch detection together with a 2D elastic probabilistic decoding algorithm (elasticity = 2) could achieve the optimal top-5 accuracy of 94.2%. In the evaluation user study, the participants reached a single-finger typing speed of 26.1 WPM with 3.2% uncorrected word-level error rate, which was significantly better than both tap-based and gesture-based baseline techniques. Also, the proposed technique received the highest preference score from the users, proving its usability in real text entry tasks.
MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet

U-Nets have achieved tremendous success in medical image segmentation. Nevertheless, it may have limitations in global (long-range) contextual interactions and edge-detail preservation. In contrast, the Transformer module has an excellent ability to capture long-range dependencies by leveraging the self-attention mechanism into the encoder. Although the Transformer module was born to model the long-range dependency on the extracted feature maps, it still suffers high computational and spatial complexities in processing high-resolution 3D feature maps. This motivates us to design an efficient Transformer-based UNet model and study the feasibility of Transformer-based network architectures for medical image segmentation tasks. To this end, we propose to self-distill a Transformer-based UNet for medical image segmentation, which simultaneously learns global semantic information and local spatial-detailed features. Meanwhile, a local multi-scale fusion block is first proposed to refine fine-grained details from the skipped connections in the encoder by the main CNN stem through self-distillation, only computed during training and removed at inference with minimal overhead. Extensive experiments on BraTS 2019 and CHAOS datasets show that our MISSU achieves the best performance over previous state-of-the-art methods. Code and models are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wangn123/MISSU.git</uri>
Finding Many Collisions via Reusable Quantum Walks

Given a random function f with domain $$[2^n]$$ and codomain $$[2^m]$$ , with $$m \ge n$$ , a collision of f is a pair of distinct inputs with the same image. Collision finding is an ubiquitous problem in cryptanalysis, and it has been well studied using both classical and quantum algorithms. Indeed, the quantum query complexity of the problem is well known to be $$\varTheta (2^{m/3})$$ , and matching algorithms are known for any value of m. The situation becomes different when one is looking for multiple collision pairs. Here, for $$2^k$$ collisions, a query lower bound of $$\varTheta (2^{(2k+m)/3})$$ was shown by Liu and Zhandry (EUROCRYPT 2019). A matching algorithm is known, but only for relatively small values of m, when many collisions exist. In this paper, we improve the algorithms for this problem and, in particular, extend the range of admissible parameters where the lower bound is met. Our new method relies on a chained quantum walk algorithm, which might be of independent interest. It allows to extract multiple solutions of an MNRS-style quantum walk, without having to recompute it entirely: after finding and outputting a solution, the current state is reused as the initial state of another walk. As an application, we improve the quantum sieving algorithms for the shortest vector problem (SVP), with a complexity of $$2^{0.2563d + o(d)}$$ instead of the previous $$2^{0.2570d + o(d)}$$ .
Crafting Interactive Circuits on Glazed Ceramic Ware

Glazed ceramic is a versatile material that we use every day. In this paper, we present a new approach that instruments existing glazed ceramic ware with interactive electronic circuits. We informed this work by collaborating with a ceramics designer and connected his craft practice to our experience in physical computing. From this partnership, we developed a systematic approach that begins with the subtractive fabrication of traces on glazed ceramic surfaces via the resist-blasting technique, followed by applying conductive ink into the inlaid traces. We capture and detail this approach through an annotated flowchart for others to refer to, as well as externalize the material insights we uncovered through ceramic and circuit swatches. We then demonstrate a range of interactive home applications built with this approach. Finally, we reflect on the process we took and discuss the importance of collaborating with craftspeople for material-driven research within HCI.
Super-resolution reconstruction for the three-dimensional turbulence flows with a back-projection network

Recent attempts to employ deep learning technology for the super-resolution (SR) reconstruction of turbulence have focused chiefly on reconstructing two-dimensional (2D) slices of the three-dimensional (3D) flow fields. However, tomographic particle image velocimetry (Tomo-PIV) measurements yield 3D velocity fields. The resolutions of 3D velocity field data along three directions cannot be simultaneously enhanced using a 2D SR reconstruction model. Additionally, a 3D velocity flow field structure cannot be accurately reconstructed using a 2D model. Therefore, we present a 3D flow field SR reconstruction method (FSR-3D) based on a back-projection network. The FSR-3D model incorporates a multiscale convolutional residual block and an up-and-down projection module and establishes the mapping relationship between low-resolution features and high-resolution (HR) features through an iterative mechanism. The proposed model is validated using two experiments. First, the original velocity field is reconstructed from downsampled forced isotropic turbulence data. Second, the downsampled direct numerical simulation (DNS) data of a turbulent channel flow are used to reconstruct the HR flow field to verify the reconstruction capability of the FSR-3D model with respect to the flow field of the boundary layer. Third, Tomo-PIV measurements of the wake flow behind a circular cylinder are used to further verify the generalization ability of the model and the reconstruction effect of the measured data. The experimental results demonstrate that the proposed FSR-3D model can accurately reconstruct the HR 3D velocity field. At the same time, the properties of the reconstruction results, such as their vortex structure, kinetic energy spectrum, and Reynolds stress, are closer to the DNS results than those of the 2D model. The reconstruction results of the Tomo-PIV measurement data show that the FSR-3D model has good generalization ability.