Reviewerly/exHarmony huggingface dataset:
On-chip quantum interference between the origins of a multi-photon state

There has been broad interest in path identity in recent years due to its role as a foundation for numerous novel quantum information applications. Here, we experimentally demonstrate quantum coherent superposition of two different origins of a four-photon state, where multi-photon frustrated interference emerges from the quantum indistinguishability by path identity. The quantum state is created in four probabilistic photon-pair sources on one integrated silicon photonic chip, two combinations of which can create photon quadruplets. Coherent elimination and revival of the distributed four photons are fully controlled by tuning phases. The experiment gives rise to peculiar quantum interference of two possible ways to create photon quadruplets rather than interference of different intrinsic properties of photons. Besides many known potential applications, this kind of multi-photon nonlinear interference enables the possibility for various fundamental studies such as nonlocality with multiple spatially separated locations.
Asynchronous photonic time-delay reservoir computing

Time-delay reservoir computing uses a nonlinear node associated with a feedback loop to construct a large number of virtual neurons in the neural network. The clock cycle of the computing network is usually synchronous with the delay time of the feedback loop, which substantially constrains the flexibility of hardware implementations. This work shows an asynchronous reservoir computing network based on a semiconductor laser with an optical feedback loop, where the clock cycle (20 ns) is considerably different to the delay time (77 ns). The performance of this asynchronous network is experimentally investigated under various operation conditions. It is proved that the asynchronous reservoir computing shows highly competitive performance on the prediction task of Santa Fe chaotic time series, in comparison with the synchronous counterparts.
Semi-quantum Tokenized Signatures

Quantum tokenized signature schemes (Ben-David and Sattath, QCrypt 2017) allow a sender to generate and distribute quantum unclonable states which grant their holder a one-time permission to sign in the name of the sender. Such schemes are a strengthening of public-key quantum money schemes, as they imply public-key quantum money where some channels of communication in the system can be made classical. An even stronger primitive is semi-quantum tokenized signatures, where the sender is classical and can delegate the generation of the token to a (possibly malicious) quantum receiver. Semi-quantum tokenized signature schemes imply a powerful version of public-key quantum money satisfying two key features: While fully-quantum tokenized signatures (where the sender is quantum and generates the token by itself) are known based on quantum-secure indistinguishability obfuscation and injective one-way functions, the semi-quantum version is not known under any computational assumption. In this work we construct a semi-quantum tokenized signature scheme based on quantum-secure indistinguishability obfuscation and the sub-exponential hardness of the Learning with Errors problem. In the process, we show new properties of quantum coset states and a new hardness result on indistinguishability obfuscation of classical subspace membership circuits.
SIELNet: 3-D Chaotic-Map-Based Secure Image Encryption Using Customized Residual Dense Spatial Network

The continuous development of Industry 5.0 technology has brought great convenience to people’s work and life. However, during digital data transmission and storage, the data may be accessed by unauthorized persons, resulting in privacy disclosure. Therefore, efficient data protection is always a high demand to solve this realistic problem. This work proposes a secure encryption algorithm, SIELNet, for colour images. First, we introduce a new three-dimensional chaotic map to encrypt colour images, obtaining the cipher images with a relationship to the plain images. We provide its excellent chaotic behaviour through standard randomness test. Secondly, we use a customized residual dense spatial network to perform the task of lossy image reconstruction from an encrypted, compressed image, which solves the constrained super-resolution task. Extensive experimental results on four public datasets demonstrate the superior performance of SIELNet against state-of-the-art techniques with excellent reconstruction quality. We believe the secure design of SIELNet can contribute to the favourable data integrity application of Industry 5.0.
smartPip: A Smart Approach to Resolving Python Dependency Conflict Issues

As one of the representative software ecosystems, PyPI, together with the Python package management tool pip, greatly facilitates Python developers to automatically manage the reuse of third-party libraries, thus saving development time and cost. Despite its great success in practice, a recent empirical study revealed the risks of dependency conflict (DC) issues and then summarized the characteristics of DC issues. However, the dependency resolving strategy, which is the foundation of the prior study, has evolved to a new one, namely the backtracking strategy. To understand how the evolution of this dependency resolving strategy affects the prior findings, we conducted an empirical study to revisit the characteristics of DC issues under the new strategy. Our study revealed that, of the two previously discovered DC issue manifestation patterns, one has significantly changed (Pattern A), while the other remained the same (Pattern B). We also observed, the resolving strategy for the DC issues of Pattern A suffers from the efficiency issue, while the one for the DC issues of Pattern B would lead to a waste of time and space. Based on our findings, we propose a tool smartPip to overcome the limitations of the resolving strategies. To resolve the DC issues of Pattern A, instead of iteratively verifying each candidate dependency library, we leverage a pre-built knowledge base of library dependencies to collect version constraints for concerned libraries, and then convert the version constraints into the SMT expressions for solving. To resolve the DC issues of Pattern B, we improve the existing virtual environment solution to reuse the local libraries as far as possible. Finally, we evaluated smartPip in three benchmark datasets of open source projects. The results showed that, smartPip can outperform the existing Python package management tools including pip with the new strategy and Conda in resolving DC issues of Pattern A, and achieve 1.19X - 1.60X speedups over the best baseline approach. Compared with the built-in Python virtual environment (venv), smartPip reduced 34.55% - 80.26% of storage space and achieved up to 2.26X - 6.53X speedups in resolving the DC issues of Pattern B.
Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms

The popularity of encryption mechanisms poses a great challenge to malicious traffic detection. The reason is traditional detection techniques cannot work without the decryption of encrypted traffic. Currently, research on encrypted malicious traffic detection without decryption has focused on feature extraction and the choice of machine learning or deep learning algorithms. In this paper, we first provide an in-depth analysis of traffic features and compare different state-of-the-art traffic feature creation approaches, while proposing a novel concept for encrypted traffic feature which is specifically designed for encrypted malicious traffic analysis. In addition, we propose a framework for encrypted malicious traffic detection. The framework is a two-layer detection framework which consists of both deep learning and traditional machine learning algorithms. Through comparative experiments, it outperforms classical deep learning and traditional machine learning algorithms, such as ResNet and Random Forest. Moreover, to provide sufficient training data for the deep learning model, we also curate a dataset composed entirely of public datasets. The composed dataset is more comprehensive than using any public dataset alone. Lastly, we discuss the future directions of this research.
Revolutions of Blockchain Technology in the Field of Cryptocurrencies

A ledger may be thought of as a group of documents or perhaps an accessible document that would be accessible by persons involved. Every trade which is integrated first goes through user verification. Data that has been saved by bitcoin cannot ever be erased or modified. As a result, the bitcoin may be thought as a log of all transactions processed. Blockchains is also used by cryptocurrency like the decentralized Ethereum and bitcoin, which is also known as peer-to-peer digital money. This study examines the development of currency, some few creative critiques, the operation of chain, and its use.
Metaverse Technologies in Education: A Systematic Literature Review Using PRISMA

Metaverse technology is emerging as a global trend, offering virtual activities with real-world experiences. This paper presents a systematic review of research trends from 2012–2022 to determine the prevalence and current conditions of metaverse technology in education. The PRISMA framework (selected reporting items for systematic review and meta-analysis) guided this systematic review. 42 studies were selected from the Scopus database, with more than 70 studies or references from Google Scholar kept as supporting data after quality assessment. The results indicate that research interest in metaverse technology has significantly increased in the last ten years, peaking in 2022. Augmentation and simulation are the most prominent uses of metaverse technology in the learning process. C. Moro (2017) is the most cited author on this topic, with 313 citations, followed by P. Wang (2022) with 264 citations and P. Pelargos (2022) with 163 citations. AR and VR are the most widely used types of metaverse technology. Hopefully, this review will be useful for researchers, educators, and future research on the metaverse and education.
Win-Win by Competition: Auxiliary-Free Cloth-Changing Person Re-Identification

Recent person Re-IDentification (ReID) systems have been challenged by changes in personnel clothing, leading to the study of Cloth-Changing person ReID (CC-ReID). Commonly used techniques involve incorporating auxiliary information ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., body masks, gait, skeleton, and keypoints) to accurately identify the target pedestrian. However, the effectiveness of these methods heavily relies on the quality of auxiliary information and comes at the cost of additional computational resources, ultimately increasing system complexity. This paper focuses on achieving CC-ReID by effectively leveraging the information concealed within the image. To this end, we introduce an Auxiliary-free Competitive IDentification (ACID) model. It achieves a win-win situation by enriching the identity (ID)-preserving information conveyed by the appearance and structure features while maintaining holistic efficiency. In detail, we build a hierarchical competitive strategy that progressively accumulates meticulous ID cues with discriminating feature extraction at the global, channel, and pixel levels during model inference. After mining the hierarchical discriminative clues for appearance and structure features, these enhanced ID-relevant features are crosswise integrated to reconstruct images for reducing intra-class variations. Finally, by combing with self- and cross-ID penalties, the ACID is trained under a generative adversarial learning framework to effectively minimize the distribution discrepancy between the generated data and real-world data. Experimental results on four public cloth-changing datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., PRCC-ReID, VC-Cloth, LTCC-ReID, and Celeb-ReID) demonstrate the proposed ACID can achieve superior performance over state-of-the-art methods. The code is available soon at: https://github.com/BoomShakaY/Win-CCReID.
Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.
Quantum Algorithms Applied to Satellite Mission Planning for Earth Observation

Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities.Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring.However, there are limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit.Therefore, satellites must be employed efficiently.This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales.However, close-to-optimal algorithms, such as greedy reinforcement learning and optimization algorithms, can often provide satisfactory resolutions.This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far.The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites.This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions.This paper notably illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, significantly improving over the baseline greedy methods with a completion rate of 75.8%.The results presented in this work pave the way to quantum-enabled solutions in the space industry and, more generally, future mission planning problems across industries.
Secure Heterogeneous Multi-Robot Collaboration and Docking with Hyperledger Fabric Blockchain

In recent years, multi-robot systems have received increasing attention from both industry and academia. Besides the need of accurate and robust estimation of relative localization, security and trust in the system are essential to enable wider adoption. In this paper, we propose a framework using Hyperledger Fabric for multi-robot collaboration in industrial applications. We rely on blockchain identities for the interaction of ground and aerial robots, and use smart contracts for collaborative decision making. The use of ultra-wideband (UWB) localization for both autonomous navigation and robot collaboration extends our previous work in Fabric-based fleet management. We focus on an inventory management application which uses a ground robot and an aerial robot to inspect a warehouse-like environment and store information about the found objects in the blockchain. We measure the impact of adding the blockchain layer, analyze the transaction commit latency and compare the resource utilization of blockchain-related processes to the already running data processing modules.
Metaverse in Marketing

The term metaverse refers to a collective virtual shared space created by the convergence of virtually enhanced physical reality and physically persistent virtual space, including the sum of all virtual worlds, augmented reality, and the internet. In the context of marketing, the metaverse can be thought of as a new platform for brands to engage with consumers and create immersive, interactive experiences. Brands can use the metaverse to create virtual events, product demonstrations, and other interactive experiences that allow consumers to engage with the brand in a more meaningful way. The metaverse offers new and exciting opportunities for brands to connect with consumers and create engaging, interactive experiences that drive brand awareness and loyalty. As a result, more research is needed to provide reliable and accurate data on Metaverse in marketing. This study aims to assess the challenges and opportunities, thus building a frame of reference on metaverse in marketing.
A primer on Variational Laplace (VL)

This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations.
Dual-rail encoding with superconducting cavities

The design of quantum hardware that reduces and mitigates errors is essential for practical quantum error correction (QEC) and useful quantum computation. To this end, we introduce the circuit-Quantum Electrodynamics (QED) dual-rail qubit in which our physical qubit is encoded in the single-photon subspace, [Formula: see text], of two superconducting microwave cavities. The dominant photon loss errors can be detected and converted into erasure errors, which are in general much easier to correct. In contrast to linear optics, a circuit-QED implementation of the dual-rail code offers unique capabilities. Using just one additional transmon ancilla per dual-rail qubit, we describe how to perform a gate-based set of universal operations that includes state preparation, logical readout, and parametrizable single and two-qubit gates. Moreover, first-order hardware errors in the cavities and the transmon can be detected and converted to erasure errors in all operations, leaving background Pauli errors that are orders of magnitude smaller. Hence, the dual-rail cavity qubit exhibits a favorable hierarchy of error rates and is expected to perform well below the relevant QEC thresholds with today's coherence times.
Mixed Depth Representations for Dialog Processing

We describe our work on developing a general purpose tutoring system that will allow students to practice their decision-making skills in a number of domains. The tutoring system, B2, supports mixed-initiative natural language interaction. The natural language processing and knowledge representation components are also general purpose—which leads to a tradeoff between the limitations of superficial processing and syntactic representations and the difficulty of deeper methods and conceptual representations. Our solution is to use a mixed-depth representation, one that encodes syntactic and conceptual information in the same structure. As a result, we can use the same representation framework to produce a detailed representation of requests (which tend to be well-specified) and to produce a partial representation of questions (which tend to require more inference about the context). Moreover, the representations use the same knowledge representation framework that is used to reason about discourse processing and domain information—so that the system can reason with (and about) the utterances, if necessary.
Copula index for detecting dependence and monotonicity between stochastic signals

This paper introduces a nonparametric copula-based index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals. Our index, termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies several desirable properties of measures of association, including most of Rényi’s properties, the data processing inequality (DPI), and consequently self-equitability. Synthetic data simulations reveal that the statistical power of CIM compares favorably to other state-of-the-art measures of association that are proven to satisfy the DPI. Simulation results with real-world data reveal CIM’s unique ability to detect the monotonicity structure among stochastic signals to find interesting dependencies in large datasets. Additionally, simulations show that CIM compares favorably to estimators of mutual information when discovering Markov network structure.
CRF with deep class embedding for large scale classification

This paper presents a novel deep learning architecture for classifying structured objects in ultrafine-grained datasets, where classes may not be clearly distinguishable by their appearance but rather by their context. We model sequences of images as linear-chain CRFs, and jointly learn the parameters from both local-visual features and neighboring class information. The visual features are learned by convolutional layers, whereas class-structure information is reparametrized by factorizing the CRF pairwise potential matrix. This forms a context-based semantic similarity space, learned alongside the visual similarities, and dramatically increases the learning capacity of contextual information. This new parametrization, however, forms a highly nonlinear objective function which is challenging to optimize. To overcome this, we develop a novel surrogate likelihood which allows for a local likelihood approximation of the original CRF with integrated batch-normalization. This model overcomes the difficulties of existing CRF methods to learn the contextual relationships thoroughly when there is a large number of classes and the data is sparse. The performance of the proposed method is illustrated on a huge dataset that contains images of retail-store product displays, and shows significantly improved results compared to linear CRF parametrization, unnormalized likelihood optimization, and RNN modeling. We also show improved results on a standard OCR dataset.