Reviewerly/exHarmony huggingface dataset:
Dynamically Driven Emergence in a Nanomagnetic System

Abstract Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring‐shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field‐dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non‐linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers.
Semantic Models for the First-Stage Retrieval: A Comprehensive Review

Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents and latter stages attempt to re-rank those candidates. Unlike re-ranking stages going through quick technique shifts over the past decades, the first-stage retrieval has long been dominated by classical term-based models. Unfortunately, these models suffer from the vocabulary mismatch problem, which may block re-ranking stages from relevant documents at the very beginning. Therefore, it has been a long-term desire to build semantic models for the first-stage retrieval that can achieve high recall efficiently. Recently, we have witnessed an explosive growth of research interests on the first-stage semantic retrieval models. We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development. In this article, we describe the current landscape of the first-stage retrieval models under a unified framework to clarify the connection between classical term-based retrieval methods, early semantic retrieval methods, and neural semantic retrieval methods. Moreover, we identify some open challenges and envision some future directions, with the hope of inspiring more research on these important yet less investigated topics.
An analysis of the application areas of augmented reality technology in the construction industry

Purpose The paper aims to analyze the application areas of augmented reality (AR) in the construction industry to enhance its usage level. The objectives are to assess the level of awareness of hardware and software devices of AR, examine the application areas of AR and reveal lagging areas in the construction industry. Design/methodology/approach A survey of construction professionals engaged in the built environment was sent a well-structured questionnaire in the study area. The professionals involved are project managers, architects, engineers, builders and quantity surveyors. The sampling technique used in selecting those professionals was convenience sampling. Descriptive and inferential statistics were used in analyzing the retrieved data. Findings The five most applied areas of Augmented Realities are Visualization and simulation of construction works; Project documentation; Project planning, monitoring and modification. The other two areas include on-site real-time information retrieval and health and safety measures. The findings also revealed the difference in the opinions of professionals among the variables. The results showed that there is significant difference in the opinions of the professionals regarding the application areas except one, that is automated measurement. Automated measurement has the same converging opinions of all professionals engaged in the study. Originality/value The study gives deep insight into possible areas where AR can be used in construction. The awareness level of hardware and software devices of AR was revealed by showing that those devices are upcoming in their usage. The areas of application of AR in construction are in their nascent stage. When there is proper implementation of this technology it will improve management in construction, minimize health and safety issues, and enhance the efficiency of workers through visualization and simulation.
Driving-signal aware full-body avatars

We present a learning-based method for building driving-signal aware full-body avatars. Our model is a conditional variational autoencoder that can be animated with incomplete driving signals, such as human pose and facial keypoints, and produces a high-quality representation of human geometry and view-dependent appearance. The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation. To this end, we explicitly account for information deficiency in the driving signal by introducing a latent space that exclusively captures the remaining information, thus enabling the imputation of the missing factors required during full-body animation, while remaining faithful to the driving signal. We also propose a learnable localized compression for the driving signal which promotes better generalization, and helps minimize the influence of global chance-correlations often found in real datasets. For a given driving signal, the resulting variational model produces a compact space of uncertainty for missing factors that allows for an imputation strategy best suited to a particular application. We demonstrate the efficacy of our approach on the challenging problem of full-body animation for virtual telepresence with driving signals acquired from minimal sensors placed in the environment and mounted on a VR-headset.
Factors associated with teachers' competence to develop students’ information literacy: A multilevel approach

Cultivating students' information literacy is becoming increasingly important for teachers in the 21st century; however, teachers' competence to develop students' information literacy (TCDSIL) is far from satisfying. To foster TCDSIL, its influencing factors and promotion strategies need to be studied. However, prior studies on TCDSIL mainly investigate the influencing factors from a single-level perspective and neglect the potential relationship of school context on TCDSIL. In order to fill this gap and provide a deeper understanding of the complex system of TCDSIL, this study surveyed 9909 teachers in 1286 primary and secondary schools and used a two-level hierarchical linear model to analyze the survey data. The analysis results indicated that both teacher characteristics and school context have a significant relationship with TCDSIL. Among school-related factors, school type, resources for instruction, and network bandwidth have positively significant relationships with TCDSIL. Moreover, teachers' perceived usefulness, information processing skills (the skills of information access, information usage, and information management), and information ethics could predict TCDSIL. This study provides implications regarding how to improve TCDSIL, including paying attention to the gap between primary school teachers and secondary school teachers; enriching school's digital teaching resources; ensuring school network quality; enhancing teachers' perceived usefulness of ICT, information processing skills, and information ethics.
High-performance superconducting quantum processors via laser annealing of transmon qubits

Scaling the number of qubits while maintaining high-fidelity quantum gates remains a key challenge for quantum computing. Presently, superconducting quantum processors with &gt;50 qubits are actively available. For these systems, fixed-frequency transmons are attractive because of their long coherence and noise immunity. However, scaling fixed-frequency architectures proves challenging because of precise relative frequency requirements. Here, we use laser annealing to selectively tune transmon qubits into desired frequency patterns. Statistics over hundreds of annealed qubits demonstrate an empirical tuning precision of 18.5 MHz, with no measurable impact on qubit coherence. We quantify gate error statistics on a tuned 65-qubit processor, with median two-qubit gate fidelity of 98.7%. Baseline tuning statistics yield a frequency-equivalent resistance precision of 4.7 MHz, sufficient for high-yield scaling beyond 10 3 qubit levels. Moving forward, we anticipate selective laser annealing to play a central role in scaling fixed-frequency architectures.
Unsupervised Contrastive Cross-modal Hashing

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we propose a novel momentum optimizer that performs hashing operation learnable in CL, thus making on-the-shelf deep cross-modal hashing possible. In other words, our method does not involve binary-continuous relaxation like most existing methods, thus enjoying better retrieval performance; ii) to alleviate the influence brought by false-negative pairs (FNPs), we propose a Cross-modal Ranking Learning loss (CRL) which utilizes the discrimination from all instead of only the hard negative pairs, where FNP refers to the within-class pairs that wrongly treated as negative pairs. Thanks to such a global strategy, CRL endows our method with better performance because CRL will not overuse the FNPs while ignoring the true-negative pairs. To the best of our knowledge, the proposed method could be one of the first successful contrastive hashing methods. To demonstrate the effectiveness of the proposed method, we carry out experiments on five widely-used datasets compared with 15 state-of-the-art methods.
Twin Contrastive Learning for Online Clustering

This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively. Based on the observation, for a given dataset, the proposed TCL first constructs positive and negative pairs through data augmentations. Thereafter, in the row and column space of the feature matrix, instance- and cluster-level contrastive learning are respectively conducted by pulling together positive pairs while pushing apart the negatives. To alleviate the influence of intrinsic false-negative pairs and rectify cluster assignments, we adopt a confidence-based criterion to select pseudo-labels for boosting both the instance- and cluster-level contrastive learning. As a result, the clustering performance is further improved. Besides the elegant idea of twin contrastive learning, another advantage of TCL is that it could independently predict the cluster assignment for each instance, thus effortlessly fitting online scenarios. Extensive experiments on six widely-used image and text benchmarks demonstrate the effectiveness of TCL. The code is released on https://pengxi.me .
jTrans: jump-aware transformer for binary code similarity detection

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerabilities detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.
MOMDPs: A Solution for Modelling Adaptive Management Problems

In conservation biology and natural resource management, adaptive management is an iterative process of improving management by reducing uncertainty via monitoring. Adaptive management is the principal tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a restricted Mixed Observability MDP called hidden model MDP (hmMDP). We demonstrate how to simplify the value function, the backup operator and the belief update computation. We show that, although a simplified case of POMDPs, hm-MDPs are PSPACE-complete in the finite-horizon case. We illustrate the use of this model to manage a population of the threatened Gouldian finch, a bird species endemic to Northern Australia. Our simple modelling approach is an important step towards efficient algorithms for solving adaptive management problems.
Low Latency Privacy Preserving Inference

When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the width and depth of neural networks that can be used (and hence the accuracy) and exhibit high latency even for relatively simple networks. In this study we provide two solutions that address these limitations. In the first solution, we present more than 10× improvement in latency and enable inference on wider networks compared to prior attempts with the same level of security. The improved performance is achieved by novel methods to represent the data during the computation. In the second solution, we apply the method of transfer learning to provide private inference services using deep networks with latency of ∼0.16 seconds. We demonstrate the efficacy of our methods on several computer vision tasks.
OpenHealth: Open-Source Platform for Wearable Health Monitoring

This article presents an open-source platform for wearable health monitoring. It aims to design a standard set of hardware/software and wearable devices that can enable autonomous collection of clinically relevant data. It provides reference implementations of human activity and gesture recognition applications within this platform.
Covariance matrix based fall detection from multiple wearable sensors

Falls are among the critical accidents experienced by elderly people and patients carrying some diseases. Subsequently, the detection and prevention of falls have become a hot research and industrial topic. This is due to the fact that falls are behind numerous irreversible injuries, or even death, and are weighting on the budgets of the health services. Automatic fall detection is one of the proposed solutions which aim at monitoring people who are likely to fall. Such solutions mitigate the fall impact by taking a quick action, e.g. in case of a fall occurrence, an alert is sent to the hospital. In this paper, we propose a new fall detection system relying on different signals acquired with multiple wearable sensors. Our system makes use of the covariance of the raw signals and the nearest neighbor classifier. Besides feature extraction, we also employ the covariance matrix as a straightforward mean for fusing signals from multiple sensors, to enhance the classification performance. Evaluation on two publicly available fall datasets, namely CogentLabs and DLR, demonstrates that the proposed approach is efficient when exploiting a single sensor as well as when fusing data from multiple sensors. Geodesic metrics are found to provide a higher fall detection accuracy than the Euclidean metric. The best obtained classification accuracies are 92.51% and 98.31% for CogentLabs and DLR datasets, respectively.
Photometry of high-redshift blended galaxies using deep learning

ABSTRACT The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50 per cent. This proof-of-concept work explores for the first time the use of deep neural networks to estimate the photometry of blended pairs of galaxies in space-based monochrome images similar to the ones that will be delivered by the Euclidspace telescope under simplified idealized conditions. Using a clean sample of isolated galaxies from the CANDELS survey, we artificially blend them and train two different network models to recover the photometry of the two galaxies. We show that our approach can recover the original photometry of the galaxies before being blended with $\sim 7{{\ \rm per\ cent}}$ mean absolute percentage error on flux estimations without any human intervention and without any assumption on the galaxy shape. This represents an improvement of at least a factor of 4 compared to the classical SExtractor approach. We also show that, forcing the network to simultaneously estimate fractional segmentation maps results in a slightly improved photometry. All data products and codes have been made public to ease the comparison with other approaches on a common data set. See https://github.com/aboucaud/coindeblend.
Conditional Neural Movement Primitives

Conditional Neural Movement Primitives (CNMPs) is a learning from demonstration framework that is designed as a robotic movement learning and generation system built on top of a recent deep neural architecture, namely Conditional Neural Processes (CNPs).Based on CNPs, CNMPs extract the prior knowledge directly from the training data by sampling observations from it, and uses it to predict a conditional distribution over any other target points.CNMPs specifically learns complex temporal multi-modal sensorimotor relations in connection with external parameters and goals; produces movement trajectories in joint or task space; and executes these trajectories through a high-level feedback control loop.Conditioned with an external goal that is encoded in the sensorimotor space of the robot, predicted sensorimotor trajectory that is expected to be observed during the successful execution of the task is generated by the CNMP, and the corresponding motor commands are executed.In order to detect and react to unexpected events during action execution, CNMP is further conditioned with the actual sensor readings in each time-step.Through simulations and real robot experiments, we showed that CNMPs can learn the nonlinear relations between low-dimensional parameter spaces and complex movement trajectories from few demonstrations; and they can also model the associations between high-dimensional sensorimotor spaces and complex motions using large number of demonstrations.The experiments further showed that even the task parameters were not explicitly provided to the system, the robot could learn their influence by associating the learned sensorimotor representations with the movement trajectories.The robot, for example, learned the influence of object weights and shapes through exploiting its sensorimotor space that includes proprioception and force measurements; and be able to change the movement trajectory on the fly when one of these factors were changed through external intervention.
BFR-MPC: A Blockchain-Based Fair and Robust Multi-Party Computation Scheme

In a general secure multi-party computation (MPC) protocol, two or more participants who do not trust each other, use their respective secret inputs to calculate a joint function in a distributed environment without a centralized organization. They can get correct outputs on the premise of ensuring privacy and independence of input. In this paper, to solve the problem of fairness and robustness in MPC, a blockchain-based multi-party computation scheme (BFR-MPC) was proposed. The blockchain maintains an open reputation system for parties as a public ledger where a more reputable party has a greater chance to be selected. The block height is used as a trusted timestamp. In each round, parties must send the correct information before the deadline. In our scheme, all parties are considered to be foresighted, and an incentive mechanism encourages parties to cooperate rather than deviate from the protocol. Because of non-cooperative parties will be immediately expelled from the protocol and will be penalized financially, the proposed scheme is robust. The penalty will be used to reward honest parties. We also proved the fairness of our scheme through Game Theory. The comparison results of the proposed scheme with other schemes show that it is a more practical scheme for MPC with high fairness and robustness.
Advanced gesture recognition system using long-term recurrent convolution network

The reliable communication between deaf-dumb and normal people is difficult, because deaf and dumb people use sign language for their communication which is unknown to the normal person. In this research work, to provides solution for problem faced by deaf and dumb people. It deals with an idea of gesture image recognition. In this the concept of embedded system and image processing is combined together which is very effective. This device convert gesture image to voice. The conversion is done using Raspberry pi. Gesture image recognition is processed by implementing LRCN (Long-term Recurrent Convolution Network) algorithm.
Difficulty-Controllable Multi-hop Question Generation from Knowledge Graphs

Knowledge graphs have become ubiquitous data sources and their utility has been amplified by the research on ability to answer carefully crafted questions over knowledge graphs. We investigate the problem of question generation (QG) over knowledge graphs wherein, the level of difficulty of the question can be controlled. We present an end-to-end neural network-based method for automatic generation of complex multi-hop questions over knowledge graphs. Taking a subgraph and an answer as input, our transformer-based model generates a natural language question. Our model incorporates difficulty estimation based on named entity popularity, and makes use of this estimation to generate difficulty-controllable questions. We evaluate our model on two recent multi-hop QA datasets. Our evaluation shows that our model is able to generate high-quality, fluent and relevant questions. We have released our curated QG dataset and code at https://github.com/liyuanfang/mhqg .