Reviewerly/exHarmony huggingface dataset:
AR videos as scaffolding to foster students’ learning achievements and motivation in EFL learning

Abstract Augmented reality (AR) is capable of providing students with proper situated scaffolding via overlaying rich media objects on the real‐world learning context. This paper examines the potential of AR to address existing problems with insufficient scaffolding in video learning materials for English as a foreign language (EFL) learners. Thus, an AR video‐enhanced learning (ARVEL) method was designed to assist students’ EFL learning. Furthermore, an experiment was carried out to investigate the influences of the implemented method on students' EFL learning outcomes. The experimental results displayed that the ARVEL method significantly improved the students' learning achievements and intrinsic motivation, and enhanced their satisfaction with EFL learning, compared to the students learning EFL with conventional video‐based learning. Practitioner Notes What is already known about this topic Learning experience of English as a foreign language (EFL) is highly related to the experience in actual learning contexts. The significance of providing students with proper assistance in a situated learning environment has been emphasized. Just observing videos might be inadequate for advancing students' reflection on learning tasks in video‐based learning. What this paper adds An augmented reality (AR) video‐enhanced learning method was proposed to assist students' EFL learning. An AR video‐enhanced learning (ARVEL) system was designed based on the implemented method. It was evidenced that the implemented method effectively improved students' learning achievements and motivation. The students display a higher degree of satisfaction with EFL learning than with the video‐enhanced learning method. Implications for practice and/or policy The ARVEL method could be a good reference for implementing an EFL learning environment. More kinds of assistance could be designed as scaffolding for assisting students' EFL learning. It is worth probing the influences of the ARVEL on students' learning of more abstract vocabulary and concepts.
Food Quality Monitoring System Based on Smart Contracts and Evaluation Models

Currently, food quality has become a major concern for the food industry. To efficiently detect food quality problems during the production process, food enterprises must build quality monitoring systems. However, in a traditional quality monitoring system, data tampering and centralized storage have become barriers to reliability. In addition, due to lack of sufficient automation, traditional quality monitoring approaches are usually inefficient. Fortunately, blockchain is a promising technology that is tamper-proof and decentralized. Moreover, smart contracts, which are executable codes on the blockchain platform, are able to conduct transactions between mutually untrusted parties and are self-executing and self-verifying. By combining smart contracts and quality evaluation models, this paper presents an intelligent quality monitoring system for fruit juice production. This system has the characteristics of high automation and high reliability. In this system, response surface models are established based on preproduction data, and the optimal production condition for each stage is identified. During the actual production process, smart contracts are executed to record production data on a blockchain. These data serve as the inputs for evaluation models. Based on the evaluation outcome, smart contracts will decide whether the production process can be resumed or not. To evaluate the feasibility of the presented system, a prototype version of the quality monitoring system for flat peach juice production is implemented based on the Ethereum platform and executed in the Remix IDE.
Adaptive Propagation Graph Convolutional Network

Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertexwise operations and message-passing exchanges across nodes. Concerning the latter, two key questions arise: 1) how to design a differentiable exchange protocol (e.g., a one-hop Laplacian smoothing in the original GCN) and 2) how to characterize the tradeoff in complexity with respect to the local updates. In this brief, we show that the state-of-the-art results can be achieved by adapting the number of communication steps independently at every node. In particular, we endow each node with a halting unit (inspired by Graves’ adaptive computation time [1]) that after every exchange decides whether to continue communicating or not. We show that the proposed adaptive propagation GCN (AP-GCN) achieves superior or similar results to the best proposed models so far on a number of benchmarks while requiring a small overhead in terms of additional parameters. We also investigate a regularization term to enforce an explicit tradeoff between communication and accuracy. The code for the AP-GCN experiments is released as an open-source library.
SMOTEFUNA: Synthetic Minority Over-Sampling Technique Based on Furthest Neighbour Algorithm

Class imbalance occurs in classification problems in which the ''normal'' cases, or instances, significantly outnumber the ''abnormal'' instances.Training a standard classifier on imbalanced data leads to predictive biases which cause poor performance on the class(es) with lower prior probabilities.The less frequent classes are often critically important events, such as system failure or the occurrence of a rare disease.As a result, the class imbalance problem has been considered to be of great importance for many years.In this paper, we propose a novel algorithm that utilizes the furthest neighbor of a candidate example to generate new synthetic samples.A key advantage of SOMTEFUNA over existing methods is that it does not have parameters to tune (such as K in SMOTE).Thus, it is significantly easier to utilize in real-world applications.We evaluate the benefit of resampling with SOMTEFUNA against state-of-the-art methods including SMOTE, ADASYN and SWIM using Naive Bayes and Support Vector Machine classifiers.Also, we provide a statistical analysis based on Wilcoxon Signed-rank test to validate the significance of the SMOTEFUNA results.The results indicate that the proposed method is an efficient alternative to the current methods.Specifically, SOMTEFUNA achieves better 5-fold cross validated ROC and precision-recall space performance.
Hybrid Consensus Algorithm Optimization: A Mathematical Method Based on POS and PBFT and Its Application in Blockchain

Blockchain is a new technology for processing complex and disordered information with respect to business and other industrial applications. This work is aimed at studying the consensus algorithm of blockchain to improve the performance of blockchain. Despite their advantages, the proof of stake (POS) algorithm and the practical Byzantine fault tolerance (PBFT) algorithm have high latency, low throughput, and poor scalability. In this paper, a blockchain hybrid consensus algorithm which combines advantages of the POS and PBFT algorithms is proposed, and the algorithm is divided into two stages: sortition and witness. The proposed algorithm reduces the number of consensus nodes to a constant value by verifiable pseudorandom sortition and performs transaction witness between nodes. The algorithm is improved and optimized from three dimensions: throughput, latency, and scalability. The experimental results show that the improved hybrid consensus algorithm is significantly superior to the previous single algorithms for its excellent scalability, high throughput, and low latency.
Concept Drift Detection via Equal Intensity k-Means Space Partitioning

The data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such a distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on the grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind spots. There is a need to improve the drift detection accuracy for the histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.
SoK: Differential privacies

Abstract Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified. These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, postprocessing, and convexity by either providing a novel proof or collecting existing ones.
Nonadiabatic Molecular Quantum Dynamics with Quantum Computers

The theoretical investigation of nonadiabatic processes is hampered by the complexity of the coupled electron-nuclear dynamics beyond the Born-Oppenheimer approximation. Classically, the simulation of such reactions is limited by the unfavorable scaling of the computational resources as a function of the system size. While quantum computing exhibits proven quantum advantage for the simulation of real-time dynamics, the study of quantum algorithms for the description of nonadiabatic phenomena is still unexplored. In this Letter, we propose a quantum algorithm for the simulation of fast nonadiabatic chemical processes together with an initialization scheme for quantum hardware calculations. In particular, we introduce a first-quantization method for the time evolution of a wave packet on two coupled harmonic potential energy surfaces (Marcus model). In our approach, the computational resources scale polynomially in the system dimensions, opening up new avenues for the study of photophysical processes that are classically intractable.
The Multi-Lane Capsule Network

We introduce Multi-Lane Capsule Networks (MLCN), which are a separable and resource efficient organization of Capsule Networks (CapsNet) that allows parallel processing, while achieving high accuracy at reduced cost. A MLCN is composed of a number of (distinct) parallel lanes, each contributing to a dimension of the result, trained using the routing-by-agreement organization of CapsNet. Our results indicate similar accuracy with a much reduced cost in number of parameters for the Fashion-MNIST and Cifar10 datsets. They also indicate that the MLCN outperforms the original CapsNet when using a proposed novel configuration for the lanes. MLCN also has faster training and inference times, being more than two-fold faster than the original CapsNet in the same accelerator.
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs.Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations.Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions.Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations.This will be more predictive for knowledge acquisition in the few-shot scenario.Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-theart results with different few-shot sizes.
Optimization and Learning With Information Streams: Time-varying algorithms and applications

There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniques cannot produce solutions at time scales that match the inter-arrival times of the data points due to computational and/or communication bottlenecks. Special types of online algorithms can handle this situation, and this article focuses on such time-varying optimization algorithms, with emphasis on Machine Leaning and Signal Processing, as well as data-driven Control. Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated. Insights on performance metrics and accompanying claims are provided, along with evidence of cases where algorithms that are provably convergent in batch optimization may perform poorly in an online regime. The role of distributed computation is discussed. Illustrative numerical examples for a number of applications of broad interest are provided to convey key ideas.
Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations.Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs.They also depend on high embedding dimensions to realize enough expressiveness.Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association.We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation.Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.
UAV Stocktaking Task-Planning for Industrial Warehouses Based on the Improved Hybrid Differential Evolution Algorithm

Tobacco industry companies need to conduct a regular inventory of finished products and raw and auxiliary materials, and drones with radio frequency identification (RFID) readers are becoming a major application trend of inventory. Under the condition of ensuring the accuracy of inventory, this article considers the physical performance constraints of the drone, the constraints of the RFID reader, etc., and introduces the force of the drone into the model establishment, and a task planning model for UAV inventory library equipped with RFID reader is proposed. Then, in view of the problem that the greedy strategy in the traditional differential evolution (DE) algorithm will cause the location information retained by other individuals to be lost, a hybrid DE algorithm based on the lion swarm optimization is proposed. Finally, the proposed algorithm was verified by environmental modeling based on the data of the tobacco enterprise warehouse.
Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

Abstract A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.
We are More than Our Joints: Predicting how 3D Bodies Move

A key step towards understanding human behavior is the prediction of 3D human motion. Successful solutions have many applications in human tracking, HCI, and graphics. Most previous work focuses on predicting a time series of future 3D joint locations given a sequence 3D joints from the past. This Euclidean formulation generally works better than predicting pose in terms of joint rotations. Body joint locations, however, do not fully constrain 3D human pose, leaving degrees of freedom (like rotation about a limb) undefined. Note that 3D joints can be viewed as a sparse point cloud. Thus the problem of human motion prediction can be seen as a problem of point cloud prediction. With this observation, we instead predict a sparse set of locations on the body surface that correspond to motion capture markers. Given such markers, we fit a parametric body model to recover the 3D body of the person. These sparse surface markers also carry detailed information about human movement that is not present in the joints, increasing the naturalness of the predicted motions. Using the AMASS dataset, we train MOJO (More than Our JOints), which is a novel variational autoencoder with a latent DCT space that generates motions from latent frequencies. MOJO preserves the full temporal resolution of the input motion, and sampling from the latent frequencies explicitly introduces high-frequency components into the generated motion. We note that motion prediction methods accumulate errors over time, resulting in joints or markers that diverge from true human bodies. To address this, we fit the SMPL-X body model to the predictions at each time step, projecting the solution back onto the space of valid bodies, before propagating the new markers in time. Quantitative and qualitative experiments show that our approach produces state-of-the-art results and realistic 3D body animations. The code is available for research purposes at https://yz-cnsdqz.github.io/MOJO/MOJO.html.
Quantum Error Correction with the Gottesman-Kitaev-Preskill Code

The Gottesman-Kitaev-Preskill (GKP) code was proposed in 2001 by Daniel Gottesman, Alexei Kitaev, and John Preskill as a way to encode a qubit in an oscillator. The GKP codewords are coherent superpositions of periodically displaced squeezed vacuum states. Because of the challenge of merely preparing the codewords, the GKP code was for a long time considered to be impractical. However, the remarkable developments in quantum hardware and control technology in the last two decades has made the GKP code a frontrunner in the race to build practical, fault-tolerant bosonic quantum technology. In this Perspective, we provide an overview of the GKP code with emphasis on its implementation in the circuit-QED architecture and present our outlook on the challenges and opportunities for scaling it up for hardware-efficient, fault-tolerant quantum error correction.6 MoreReceived 27 April 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020101Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum gatesQuantum information processingQuantum information processing with continuous variablesQuantum memoriesQuantum Information
Balanced Product Quantum Codes

This work provides the first explicit and non-random family of $[[N,K,D]]$ LDPC quantum codes which encode $K \in \Theta(N^\frac{4}{5})$ logical qubits with distance $D \in \Omega(N^\frac{3}{5})$. The family is constructed by amalgamating classical codes and Ramanujan graphs via an operation called balanced product. Recently, Hastings-Haah-O'Donnell and Panteleev-Kalachev were the first to show that there exist families of LDPC quantum codes which break the $\operatorname{polylog}(N)\sqrt{N}$ distance barrier. However, their constructions are based on probabilistic arguments which only guarantee the code parameters with high probability whereas our bounds hold unconditionally. Further, balanced products allow for non-abelian twisting of the check matrices, leading to a construction of LDPC quantum codes that can be shown to have $K\in \Theta(N)$ and that we conjecture to have linear distance $D\in \Theta(N)$.