Reviewerly/exHarmony huggingface dataset:
A quantum-bit encoding converter

From telecommunication to computing architectures, the realm of classical information hinges on converter technology to enable the exchange of data between digital and analog formats, a process now routinely performed across a variety of electronic devices. A similar exigency exists as well in quantum information technology where different frameworks are being developed for quantum computing, communication, and sensing. Thus, efficient quantum interconnects are a major need to bring these parallel approaches together and scale up quantum information systems. So far, however, the conversion between different optical quantum-bit encodings has remained challenging due to the difficulty of preserving fragile quantum superpositions and the demanding requirements for postselection-free implementations. Here we demonstrate such a conversion of quantum information between the two main paradigms, namely discrete- and continuous-variable qubits. We certify the protocol on a complete set of single-photon qubits, successfully converting them to cat-state qubits with fidelities exceeding the classical limit. Our result demonstrates an essential tool for enabling interconnected quantum devices and architectures with enhanced versatility and scalability.
Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks

Reduction in 30-day readmission rate is an important quality factor for hospitals as it can reduce the overall cost of care and improve patient post-discharge outcomes. While deep-learning-based studies have shown promising empirical results, several limitations exist in prior models for hospital readmission prediction, such as: (a) only patients with certain conditions are considered, (b) do not leverage data temporality, (c) individual admissions are assumed independent of each other, which ignores patient similarity, (d) limited to single modality or single center data. In this study, we propose a multimodal, spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission, which fuses in-patient multimodal, longitudinal data and models patient similarity using a graph. Using longitudinal chest radiographs and electronic health records from two independent centers, we show that MM-STGNN achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 on both datasets. Furthermore, MM-STGNN significantly outperformed the current clinical reference standard, LACE+ (AUROC=0.61), on the internal dataset. For subset populations of patients with heart disease, our model significantly outperformed baselines, such as gradient-boosting and Long Short-Term Memory models (e.g., AUROC improved by 3.7 points in patients with heart disease). Qualitative interpretability analysis indicated that while patients' primary diagnoses were not explicitly used to train the model, features crucial for model prediction may reflect patients' diagnoses. Our model could be utilized as an additional clinical decision aid during discharge disposition and triaging high-risk patients for closer post-discharge follow-up for potential preventive measures.
A systematic literature review of the tension between the GDPR and public blockchain systems

The blockchain technology has been rapidly growing since Bitcoin was invented in 2008. The most common type of blockchain systems, public (permissionless) blockchain systems have some unique features that lead to a tension with European Union's General Data Protection Regulation (GDPR) and other similar data protection laws. In this paper, we report the results of a systematic literature review (SLR) on 114 research papers discussing and/or addressing such a tension. To the best of our knowledge, our SLR is the most comprehensive review of this topic, leading a more in-depth and broader analysis of related research work on this important topic. Our results revealed three main types of issues: (i) difficulties in exercising data subjects' rights such as the ‘right to be forgotten’ (RTBF) due to the immutable nature of public blockchains; (ii) difficulties in identifying roles and responsibilities in the public blockchain data processing ecosystem (particularly on the identification of data controllers and data processors); (iii) ambiguities regarding the application of the relevant law(s) due to the distributed nature of blockchains. Our work also led to a better understanding of solutions for improving the GDPR compliance of public blockchain systems. Our work can help inform not only blockchain researchers and developers, but also policy makers and law markers to consider how to reconcile the tension between public blockchain systems and data protection laws (the GDPR and beyond).
The Emergence and Evolution of Consumer Language Research

Abstract Over the last 50+ years, there has been a huge rise in interest in consumer language research. This manuscript spotlights the emergence and evolution of this area, identifying key themes and trends, and highlighting topics for future research. Work has evolved from exploration of broad language concepts (e.g., rhetorics) to specific linguistic features (e.g., phonemes) and from monologues (e.g., advertiser to consumer) to two-way dialogues (e.g., consumer to service representative and back). We discuss future opportunities that arise from past trends, and suggest two important shifts that prompt questions for future research: the new shift towards using voice (vs. hands) when interacting with objects, and the ongoing shift towards using hands (vs. voices) to communicate with people. By synthesizing the past, and delineating a research agenda for the future, we hope to encourage more researchers to begin to explore this burgeoning area.
Blockchain aware proxy re-encryption algorithm-based data sharing scheme

The blockchain stores transaction data in a distributed shared global ledger. It is challenging to strike a balance between privacy protection and usefulness while sharing data. Moreover, the dynamic adjustment of blockchain data access rights is a challenging problem. To this end, this paper suggests a blockchain data-controlled sharing scheme based on proxy re-encryption. First, a proxy re-encryption algorithm is constructed based on SM2 and the blockchain. Blockchain data sharing can give businesses a secure way to store and share data. Since this network is decentralized, and data is transmitted across a peer-to-peer network under the protection of an unchangeable cryptographic signature. Blockchain makes it more difficult to alter or hack the data. The data-controlled sharing scheme uses proxy re-encryption to protect transaction data privacy and realize data security sharing. Secondly, a dynamic adjustment mechanism for user rights is proposed. Blockchain nodes divide labor and manage re-encryption key parameters separately to achieve user access rights determinism Update, the visibility of transaction data is dynamically adjusted. Finally, the performance and security evaluation demonstrate that this scheme can realize the dynamic sharing of blockchain data while protecting transaction privacy and has advantages in computing overhead, which is better applicable to the Controlled sharing of blockchain data. This research suggests a regulated blockchain-based data-sharing system that makes use of proxy re-encryption. They are developing a proxy re-encryption algorithm with SM2 in order to fully safeguard the privacy of transaction data and to achieve data access authority determination by controlling proxy re-encryption key parameters. It is suggested to employ a hybrid attribute-based proxy re-encryption method that enables the proxy server to change attribute-encrypted cypher texts into identity-based encrypted cypher texts so that users with limited resources can access the previously encrypted material.
Programmable photonic integrated meshes for modular generation of optical entanglement links

Abstract Large-scale generation of quantum entanglement between individually controllable qubits is at the core of quantum computing, communications, and sensing. Modular architectures of remotely-connected quantum technologies have been proposed for a variety of physical qubits, with demonstrations reported in atomic and all-photonic systems. However, an open challenge in these architectures lies in constructing high-speed and high-fidelity reconfigurable photonic networks for optically-heralded entanglement among target qubits. Here we introduce a programmable photonic integrated circuit (PIC), realized in a piezo-actuated silicon nitride (SiN)-in-oxide CMOS-compatible process, that implements an N × N Mach–Zehnder mesh (MZM) capable of high-speed execution of linear optical transformations. The visible-spectrum photonic integrated mesh is programmed to generate optical connectivity on up to N = 8 inputs for a range of optically-heralded entanglement protocols. In particular, we experimentally demonstrated optical connections between 16 independent pairwise mode couplings through the MZM, with optical transformation fidelities averaging 0.991 ± 0.0063. The PIC’s reconfigurable optical connectivity suffices for the production of 8-qubit resource states as building blocks of larger topological cluster states for quantum computing. Our programmable PIC platform enables the fast and scalable optical switching technology necessary for network-based quantum information processors.
Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising

Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a collaborative machine learning technique, aims to address the challenges by learning a joint predictive model across multi-site clients, especially for distributed medical institutions or hospitals. However, most existing FL methods assume that clients possess fully labeled data for training, which is often not the case in e-health datasets due to high labeling costs or expertise requirement. Therefore, this work proposes a novel and feasible approach to learn a Federated Semi-Supervised Learning (FSSL) model from distributed medical image domains, where a federated pseudo-labeling strategy for unlabeled clients is developed based on the embedded knowledge learned from labeled clients. This greatly mitigates the annotation deficiency at unlabeled clients and leads to a cost-effective and efficient medical image analysis tool. We demonstrated the effectiveness of our method by achieving significant improvements compared to the state-of-the-art in both fundus image and prostate MRI segmentation tasks, resulting in the highest Dice scores of 89.23% and 91.95% respectively even with only a few labeled clients participating in model training. This reveals the superiority of our method for practical deployment, ultimately facilitating the wider use of FL in healthcare and leading to better patient outcomes.
Mobile robot path planning based on an improved A* algorithm

In order to improve the shortcomings of long calculation time and uneven path in the pathfinding process of A* algorithm, an optimization algorithm is proposed. First, on the basis of the A* algorithm, combined with the jump point search algorithm (JPS), the amount of calculation is reduced by replacing the expansion nodes in the A* algorithm with jump points. Secondly, a pruning algorithm is used to prune the obtained path to further remove unnecessary nodes in the path. Finally, the path is smoothed with a quadratic Bézier curve. At the end of paper, simulations were carried out in grid maps of different sizes, and experiments were carried out in the real environment. It is shorter and realizes the optimization of path length and smoothness.
Taking Flight with Copilot

Early insights and opportunities of AI-powered pair-programming tools.
Two-level system hyperpolarization using a quantum Szilard engine

The innate complexity of solid-state physics exposes superconducting quantum circuits to interactions with uncontrolled degrees of freedom degrading their coherence. By implementing a quantum Szilard engine with an active feedback control loop, we show that a superconducting fluxonium qubit is coupled to a two-level system (TLS) environment of unknown origin, with a relatively long intrinsic energy relaxation time exceeding 50 ms. The TLSs can be cooled down, resulting in a four times lower qubit population, or they can be heated to manifest themselves as a negative-temperature environment corresponding to a qubit population of ~80%. We show that the TLSs and qubit are the dominant loss mechanism for each other and that qubit relaxation is independent of the TLS populations. Understanding and mitigating TLS environments is, therefore, not only crucial to improve the qubit lifetimes but also to avoid non-Markovian qubit dynamics. The performance of superconducting qubits is often limited by spurious two-level systems. Now, a qubit operating as a heat engine manipulates its bath of nearby two-level systems, providing insights into their dynamics and interactions.
GAN Prior Based Null-Space Learning for Consistent Super-resolution

Consistency and realness have always been the two critical issues of image super-resolution. While the realness has been dramatically improved with the use of GAN prior, the state-of-the-art methods still suffer inconsistencies in local structures and colors (e.g., tooth and eyes). In this paper, we show that these inconsistencies can be analytically eliminated by learning only the null-space component while fixing the range-space part. Further, we design a pooling-based decomposition (PD), a universal range-null space decomposition for super-resolution tasks, which is concise, fast, and parameter-free. PD can be easily applied to state-of-the-art GAN Prior based SR methods to eliminate their inconsistencies, neither compromise the realness nor bring extra parameters or computational costs. Besides, our ablation studies reveal that PD can replace pixel-wise losses for training and achieve better generalization performance when facing unseen downsamplings or even real-world degradation. Experiments show that the use of PD refreshes state-of-the-art SR performance and speeds up the convergence of training up to 2~10 times.
Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts.
Low-Rank Combinatorial Optimization and Statistical Learning by Spatial Photonic Ising Machine

The spatial photonic Ising machine (SPIM) [13D. Pierangeli et al., Large-Scale Photonic Ising Machine by Spatial Light Modulation, Phys. Rev. Lett. 122, 213902 (2019).PRLTAO0031-900710.1103/PhysRevLett.122.213902] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices. In this Letter, we propose a new computing model for the SPIM that can accommodate any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, such as knapsack problems. Moreover, it acquires the learning ability of Boltzmann machines. We demonstrate that learning, classification, and sampling of the MNIST handwritten digit images are achieved efficiently using the model with low-rank interactions. Thus, the proposed model exhibits higher practical applicability to various problems of combinatorial optimization and statistical learning, without losing the scalability inherent in the SPIM architecture.
Can we trust the evaluation on ChatGPT?

ChatGPT, the first large language model with mass adoption, has demonstrated remarkable performance in numerous natural language tasks.Despite its evident usefulness, evaluating ChatGPT's performance in diverse problem domains remains challenging due to the closed nature of the model and its continuous updates via Reinforcement Learning from Human Feedback (RLHF).We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection.We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models.
ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence Systems

Abstract This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems, with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate high-quality outcomes, generative AI systems require substantial quantities of training data, which may frequently comprise copyright-protected information. This prompts inquiries into the legal principles of fair use, the creation of derivative works and the lawfulness of data gathering and utilisation. The utilisation of input data for the purpose of training and enhancing AI models presents significant concerns regarding potential violations of copyright. This paper offers suggestions for safeguarding the interests of copyright holders and competitors, while simultaneously addressing legal challenges and expediting the advancement of AI technologies. This study analyses the ChatGPT platform as a case example to explore the necessary modifications that copyright regulations must undergo to adequately tackle the intricacies of authorship and ownership in the realm of AI-generated creative content.
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation

The MultiWOZ 2.0 dataset has greatly stimulated the research of task-oriented dialogue systems. However, its state annotations contain substantial noise, which hinders a proper evaluation of model performance. To address this issue, massive efforts were devoted to correcting the annotations. Three improved versions (i.e., MultiWOZ 2.1-2.3) have then been released. Nonetheless, there are still plenty of incorrect and inconsistent annotations. This work introduces MultiWOZ 2.4, which refines the annotations in the validation set and test set of MultiWOZ 2.1. The annotations in the training set remain unchanged (same as MultiWOZ 2.1) to elicit robust and noise-resilient model training. We benchmark eight state-of-the-art dialogue state tracking models on MultiWOZ 2.4. All of them demonstrate much higher performance than on MultiWOZ 2.1.
Multi-step Jailbreaking Privacy Attacks on ChatGPT

With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. Though model developers and researchers work hard on dialog safety to avoid generating harmful content from LLMs, it is still challenging to steer AI-generated content (AIGC) for the human good. As powerful LLMs are devouring existing text data from various domains (e.g., GPT-3 is trained on 45TB texts), it is natural to doubt whether the private information is included in the training data and what privacy threats can these LLMs and their downstream applications bring. In this paper, we study the privacy threats from OpenAI’s ChatGPT and the New Bing enhanced by ChatGPT and show that application-integrated LLMs may cause new privacy threats. To this end, we conduct extensive experiments to support our claims and discuss LLMs’ privacy implications.
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance. Generating adjacency matrices with semantically meaningful edges is particularly important for this task, but extracting such edges is challenging problem. To solve this, we propose a hierarchically decomposed graph convolutional network (HD-GCN) architecture with a novel hierarchically decomposed graph (HD-Graph). The proposed HD-GCN effectively decomposes every joint node into several sets to extract major structurally adjacent and distant edges, and uses them to construct an HD-Graph containing those edges in the same semantic spaces of a human skeleton. In addition, we introduce an attention-guided hierarchy aggregation (A-HA) module to highlight the dominant hierarchical edge sets of the HD-Graph. Furthermore, we apply a new six-way ensemble method, which uses only joint and bone stream without any motion stream. The proposed model is evaluated and achieves state-of-the-art performance on four large, popular datasets. Finally, we demonstrate the effectiveness of our model with various comparative experiments. Code is available at https://github.com/Jho-Yonsei/HD-GCN.
Combining Low-Level Perception with Expectations in CHREST

The ability of humans to reliably perceive and recognise objects relies on an interaction between information seen in the visual image and prior expectations. We describe an extension to the CHREST computational model which enables it to learn and combine information from multiple input modalities. Simulations demonstrate the presence of quantitative effects on recognition ability due to cross-modal interactions. Our simulations with CHREST illustrate how expectations can improve classification accuracy, reduce classification time, and enable words to be reconstructed from noisy visual input.
Neural Networks: An Overview and Applications in the Space Economy

A great deal of attention has been paid in the scientific literature to Neural Networks (NN) as alternative models of information processing. The principal inspiration of NN is the human brain whose structure is constituted by a large number of connected neurons. This chapter commences with a concise historical overview of NN. It then describes the general architecture of NN and illustrates with some examples of different structures. The chapter also considers some recent applications of NN in the field of urban and regional economics. It seems interesting to investigate indepth NN potential and their ‘hidden’ rules, as well as to look for new hardware technologies able to improve the speediness of execution. In conclusion, NN appear to be a new computational tool offering interesting results in several research areas, as is witnessed by the increasing number of NN applications.
Unscripted Narrative for Affectively Driven Characters

We discuss work carried out at the University of Salford (UK) and INESC-id (Portugal) on the design and implementation of interactive dramas in 3D graphics environments. We present detailed requirements and methods for the design of unscripted (emergent) dramas based on research into role-playing games. We describe the affectively driven intelligent autonomous characters implemented and discuss both theoretical and technical issues relating to its computational implementation. Finally we consider the implications for game design and new genres of game.