Reviewerly/exHarmony huggingface dataset:
Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset. In this paper, we substantially extend our previous work, by introducing a multi-objective approach that automatically balances the competing objectives of manifold quality and dimensionality. Our proposed approach is competitive with a range of baseline and state-of-the-art manifold learning methods, while also providing a range (front) of solutions that give different trade-offs between quality and dimensionality. Furthermore, the learned models are shown to often be simple and efficient, utilising only a small number of features in an interpretable manner.
Flapping‐Wing Dynamics as a Natural Detector of Wind Direction

Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation

As the E-commerce thrives, high-quality online advertising copywriting has attracted more and more attention.Different from the advertising copywriting for a single product, an advertisement (AD) post includes an attractive topic that meets the customer needs and description copywriting about several products under its topic.A good AD post can highlight the characteristics of each product, thus helps customers make a good choice among candidate products.Hence, multi-product AD post generation is meaningful and important.We propose a novel end-to-end model named S-MG Net to generate the AD post.Targeted at such a challenging real-world problem, we split the AD post generation task into two subprocesses: (1) select a set of products via the SelectNet (Selection Network).( 2) generate a post including selected products via the MGenNet (Multi-Generator Network).Concretely, SelectNet first captures the post topic and the relationship among the products to output the representative products.Then, MGen-Net generates the description copywriting of each product.Experiments conducted on a large-scale real-world AD post dataset demonstrate that our proposed model achieves impressive performance in terms of both automatic metrics as well as human evaluations.
Smooth adversarial examples

Abstract This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally smooth on the flat areas of the input image, but it may be noisy on its textured areas and sharp across its edges.This operation relies on Laplacian smoothing, well-known in graph signal processing, which we integrate in the attack pipeline. We benchmark several attacks with and without smoothing under a white box scenario and evaluate their transferability. Despite the additional constraint of smoothness, our attack has the same probability of success at lower distortion.
Quantum entanglement maintained by virtual excitations in an ultrastrongly-coupled-oscillator system

We study the effect of quantum entanglement maintained by virtual excitations in an ultrastrongly-coupled harmonic-oscillator system. Here, the quantum entanglement is caused by the counterrotating interaction terms and hence it is maintained by the virtual excitations. We obtain the analytical expression for the ground state of the system and analyze the relationship between the average excitation numbers and the ground-state entanglement. We also study the entanglement dynamics between the two oscillators in both the closed- and open-system cases. In the latter case, the quantum master equation is microscopically derived in the normal-mode representation of the coupled-oscillator system. This work will open a route to the study of quantum information processing and quantum physics based on virtual excitations.
Experimental test of an entropic measurement uncertainty relation for arbitrary qubit observables

A tight information-theoretic measurement uncertainty relation is experimentally tested with neutron spin-1/2 qubits. The noise associated to the measurement of an observable is defined via conditional Shannon entropies and a tradeoff relation between the noises for two arbitrary spin observables is demonstrated. The optimal bound of this tradeoff is experimentally obtained for various non-commuting spin observables. For some of these observables this lower bound can be reached with projective measurements, but we observe that, in other cases, the tradeoff is only saturated by general quantum measurements (i.e., positive-operator valued measures), as predicted theoretically.
Retrofitting Structure-aware Transformer Language Model for End Tasks

We consider retrofitting structure-aware Transformer language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model.A middle-layer structural learning strategy is leveraged for structure integration, accomplished with main semantic task training under multi-task learning scheme.Experimental results show that the retrofitted structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases.By performing structure-aware fine-tuning, our model achieves significant improvements for both semantic-and syntactic-dependent tasks.
Link Prediction with Mutual Attention for Text-Attributed Networks

In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-Product Attention, a recently proposed attention mechanism, to design a mutual attention mechanism between pairs of documents. To train its parameters, we use the network links as supervision. We provide preliminary experiment results with a citation dataset on two prediction tasks, demonstrating the capacity of our model to learn a meaningful textual similarity.
A high-fidelity heralded quantum squeezing gate

A universal squeezing gate capable of squeezing arbitrary input states is essential for continuous-variable quantum computation~\cite{PRA79062318,PRL112120504}. However, in present state-of-the-art techniques~\cite{PRA90060302,PRL106240504}, the fidelity of such gates is ultimately limited by the need to create squeezed vacuum modes of unbounded energy. Here we circumvent this fundamental limitation by using a heralded squeezing gate. We propose and experimentally demonstrate a squeezing gate that can achieve near unit fidelity for coherent input states. In particular, for a target squeezing of \SI{2.3}{\dB}, we report a fidelity of \SI{98.5}{\%}. This result cannot be reproduced by conventional schemes even if the currently best available squeezing of \SI{15}{\dB}~\cite{PRL117110801} is utilised when benchmarked on identical detection inefficiencies. Our technique can be applied to non-Gaussian states and provides a promising pathway towards high-fidelity gate operations and fault-tolerant quantum computation.
Privacy-Preserving Passive DNS

The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g., for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that can act as potential malicious insiders, and allows only the data owners to perform queries over these data. We evaluated our proposed solution by creating a proof-of-concept experimental setup that passively collects DNS data from a network and then uses the distributed ledger technology to store the data in an immutable ledger, thus providing a full historical overview of all the records.
A JIT Compiler for Neural Network Inference

This paper describes a C++ library that compiles neural network models at runtime into machine code that performs inference. This approach in general promises to achieve the best performance possible since it is able to integrate statically known properties of the network directly into the code. In our experiments on the NAO V6 platform, it outperforms existing implementations significantly on small networks, while being inferior on large networks. The library was already part of the B-Human code release 2018, but has been extended since and is now available as a standalone version that can be integrated into any C++14 code base.
W-Net: Two-Stage U-Net With Misaligned Data for Raw-to-RGB Mapping

Recent research on learning a mapping between raw Bayer images and RGB images has progressed with the development of deep convolutional neural networks. A challenging data set namely the Zurich Raw-to-RGB data set (ZRR) has been released in the AIM 2019 raw-to-RGB mapping challenge. In ZRR, input raw and target RGB images are captured by two different cameras and thus not perfectly aligned. Moreover, camera metadata such as white balance gains and color correction matrix are not provided, which makes the challenge more difficult. In this paper, we explore an effective network structure and a loss function to address these issues. We exploit a two-stage U-Net architecture and also introduce a loss function that is less variant to alignment and more sensitive to color differences. In addition, we show an ensemble of networks trained with different loss functions can bring a significant performance gain. We demonstrate the superiority of our method by achieving the highest score in terms of both the peak signal-to-noise ratio and the structural similarity and obtaining the second-best mean-opinion-score in the challenge.
Effective Compression of Quantum Braided Circuits Aided by ZX-Calculus

Mapping a quantum algorithm to any practical large-scale quantum computer will require a sequence of compilations and optimizations. At the level of fault-tolerant encoding, one likely requirement of this process is the translation into a topological circuit, for which braided circuits represent one candidate model. Given the large overhead associated with encoded circuits, it is paramount to reduce their size in terms of computation time and qubit number through circuit compression. While these optimizations have typically been performed in the language of three-dimensional diagrams, such a representation does not allow an efficient, general, and scalable approach to reduction or verification. We propose the use of the ZX-calculus as an intermediate language for braided circuit compression, demonstrating advantage by comparing results using this approach with those previously obtained for the compression of A- and Y-state distillation circuits. We then provide a benchmark of our method against a small set of Clifford+T circuits, yielding compression percentages of 77%. Our results suggest that the overheads of braided, defect-based circuits are comparable to those of their lattice-surgery counterparts, restoring the potential of this model for surface-code quantum computation.
It’s not a Non-Issue: Negation as a Source of Error in Machine Translation

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger.In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance.We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed.Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%.We also provide a linguistically motivated analysis that directly explains the majority of our findings.We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely, agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent. Data-driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent, is used to allow the agents in the network to reach synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm's reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN-based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, and rural). Real data sets are also used acquired by autonomous vehicles performing different tasks in a controlled environment.
pixelNeRF: Neural Radiance Fields from One or Few Images

We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. We take a step towards resolving these shortcomings by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one). Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. For the video and code, please visit the project website: https://alexyu.net/pixelnerf
Research on Teaching a Welding Implementation Course Assisted by Sustainable Virtual Reality Technology

The purpose of this study is to explore the application of VR (virtual reality) technology to assist the development of VR welding courses in welding practice teaching, and to implement experimental teaching to verify its effectiveness. The preliminary VR welding curriculum structure was developed by this study according to the results of literature review and focus group interviews, the student-based “VR welding course” was developed, and 34 first-year students of the electric welding practice course were taken as the research subjects to implement experimental teaching and case study. The qualitative and quantitative research and analysis results are as follows. (1) The results of the final test of the welding practice of most students are significantly higher than the results of the mid-term test. (2) Most students expressed significant positive affirmation of the learning effect of the VR-assisted welding course. (3) Most students were very significantly positive regarding their learning satisfaction with the VR-assisted welding teaching course. (4) The four major implementation priorities of VR welding courses were planned. This study develops a “teaching mode of a welding implementation course assisted by virtual reality technology”, which can provide students with a safe, low-cost, repeatable, and sustainable welding skills learning environment, and has been positively affirmed by most students. In the future, the results of this study can provide reference for the introduction of virtual-reality-assisted teaching of welding related courses in various universities of science and technology, in order to strengthen teachers’ teaching ability in VR assisted implementation courses and provide students with more diversified learning stimuli.
Mobile Data Usage on Online Learning during COVID-19 Pandemic in Higher Education

Information about network availability and the amount of internet data usage is very important for planning and implementing management of free internet data assistance programs in the COVID-19 pandemic for students. Research Objectives to analyze for the mobile data usage in online learning (Zoom cloud meetings apps) during the COVID-19 pandemic at Higher Education institutions. The results study revealed that access to online learning using the Zoom cloud meeting apps for 1-minute internet data usage of 5.02bB for a meeting duration of 40 minutes and for a meeting duration of 60 minutes (1 hour) of 13.66 Mb. The measurement results are then used as a reference in the internet data assistance program for students as an effort in supporting the online learning process (Learning-From-Home), objective and proportionate in its distribution to beneficiaries
Querying Little Is Enough: Model Inversion Attack via Latent Information

With the development of machine learning (ML) technology, various online intelligent services use ML models to provide predictions. However, attacker may obtain privacy information of the model through interaction with online services. Model inversion attacks (MIA) is a privacy stealing method that utilizes ML models output values to reconstruct input values. In particular, an indispensable step of implementing proposed MIA approaches is that the attacker query the auxiliary datasets entirely. However, in reality, it will be inefficient to transfer huge datasets to online services to get prediction values of inference models. More seriously, the huge transmission may cause the administrator’s active defense. In this paper, we propose a novel MIA scheme which reduce queries on auxiliary datasets, by utilizing latent information of primitive models as high dimension features. We systematically evaluate our inversion approach in convolutional neural networks (CNN) classifier on LFW, pubFig, MNIST datasets. The experimental results show that even with a few queries of the inference model, our inversion approach still work accurately and outperforms than previous approaches. As conclusion, our method proves that implementing MIA does not require querying all auxiliary data on the classifier model, making it more difficult for the administrator to defend against the attack and elicit more investigations for privacy-preserving.