Reviewerly/exHarmony huggingface dataset:
One-Photon Measurement of Two-Photon Entanglement

Measuring entanglement is an essential step in a wide range of applied and foundational quantum experiments. When a two-particle quantum state is not pure, standard methods to measure the entanglement require detection of both particles. We realize a conceptually new method for verifying and measuring entanglement in a class of two-part (bipartite) mixed states. Contrary to the approaches known to date, in our experiment we verify and measure entanglement in mixed quantum bipartite states by detecting only one subsystem, the other remains undetected. Only one copy of the mixed or pure state is used but that state is in a superposition of having been created in two identical sources. We show that information shared in entangled systems can be accessed through single-particle interference patterns. Our experiment enables entanglement characterization even when one of the subsystems cannot be detected, for example, when suitable detectors are not available.
Cross-Hospital Sepsis Early Detection via Semi-Supervised Optimal Transport With Self-Paced Ensemble

Leveraging machine learning techniques for Sepsis early detection and diagnosis has attracted increasing interest in recent years. However, most existing methods require a large amount of labeled training data, which may not be available for a target hospital that deploys a new Sepsis detection system. More seriously, as treated patients are diversified between hospitals, directly applying a model trained on other hospitals may not achieve good performance for the target hospital. To address this issue, we propose a novel semi-supervised transfer learning framework based on optimal transport theory and self-paced ensemble for Sepsis early detection, called SPSSOT, which can efficiently transfer knowledge from the source hospital (with rich labeled data) to the target hospital (with scarce labeled data). Specifically, SPSSOT incorporates a new optimal transport-based semi-supervised domain adaptation component that can effectively exploit all the unlabeled data in the target hospital. Moreover, self-paced ensemble is adapted in SPSSOT to alleviate the class imbalance issue during transfer learning. In a nutshell, SPSSOT is an end-to-end transfer learning method that automatically selects suitable samples from two domains (hospitals) respectively and aligns their feature spaces. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SPSSOT outperforms state-of-the-art transfer learning methods by improving 1-3% of AUC.
LCM-Captioner: A lightweight text-based image captioning method with collaborative mechanism between vision and text

Text-based image captioning (TextCap) aims to remedy the shortcomings of existing image captioning tasks that ignore text content when describing images. Instead, it requires models to recognize and describe images from both visual and textual content to achieve a deeper level of comprehension of the images. However, existing methods tend to use numerous complex network architectures to improve performance, which still fails to adequately model the relationship between vision and text on the one side, while on the other side this leads to long running times, high memory consumption, and other unfavorable deployment problems. To solve the above issues, we have developed a lightweight captioning method with a collaborative mechanism, LCM-Captioner, which balances high efficiency with high performance. First, we propose a feature-lightening transformation for the TextCap task, named TextLighT, which is able to learn rich multimodal representations while mapping features to lower dimensions, thereby reducing memory costs. Next, we present a collaborative attention module for visual and text information, VTCAM, to facilitate the semantic alignment of multimodal information to uncover important visual objects and textual content. Finally, the conducted extensive experiments on the TextCaps dataset demonstrate the effectiveness of our method. Code is available at https://github.com/DengHY258/LCM-Captioner.
A Highway Traffic Image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

To address the problems of low resolution and blurred details in highway images caused by factors such as rain and fog, illumination interference, and nighttime lighting, this paper proposes a highway traffic image enhancement algorithm based on improved GAN in complex weather conditions. The attention mechanism and the multiscale feature fusion were combined to improve the generator network, which could effectively reduce noise while improving the attention of high-frequency region information. The improved PatchGAN in the discriminator used a local discrimination strategy to distinguish the generated image from the real image, and then the Nash equilibrium was achieved through the continuous interaction between the generator and the discriminator, to ensure the integrity and authenticity of the restored image. Compared with other image enhancement algorithms, using PSNR and SSIM as measurement indicators, the experimental results showed that the proposed algorithm’s results were, respectively, 21.97% and 12.89% higher in nighttime enhancement, 26.16% and 12.75% higher in rain removal, and 26.56% and 12.1% higher in fog removal. The proposed algorithm can not only retain the image details and feature information, but also produce effective denoising, which increases the reliability of image-based traffic information processing and analysis.
Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks

Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies. We developed autoencoder-based prediction models to automatically detect anomalous data recorded by three electrochemical aptasensors, with variations in the signals’ lengths for particular concentrations, analytes, and bioreceptors. Prediction models employed autoencoder networks and the kernel density estimation (KDE) method for finding the threshold to detect anomalies. Moreover, the autoencoder networks were vanilla, unidirectional long short-term memory (ULSTM), and bidirectional LSTM (BLSTM) autoencoders for the training stage of the prediction models. However, the decision-making was based on the result of these three networks and the integration of vanilla and LSTM networks’ results. The accuracy as a performance metric of anomaly prediction models showed that the performance of vanilla and integrated models were comparable, while the LSTM-based autoencoder models showed the least accuracy. Considering the integrated model of ULSTM and vanilla autoencoder, the accuracy for the dataset with the lengthier signals was approximately 80%, while it was 65% and 40% for the other datasets. The lowest accuracy belonged to the dataset with the least normal data in its dataset. These results demonstrate that the proposed vanilla and integrated models can automatically detect abnormal data when there is sufficient normal data for training the models.
Semi-device-independent certification of indefinite causal order in a photonic quantum switch

Quantum processes are transformations that act on quantum operations. Their study led to the discovery of the phenomenon of indefinite causal order: some quantum processes, such as the quantum switch, act on independent quantum operations in such a way that the order in which the operations are acted upon not only cannot be determined but is simply undefined. This is the property that we experimentally certify in this work. We report an experimental certification of indefinite causal order that relies only on the characterization of the operations of a single party. We do so in the semi-device-independent scenario with the fewest possible assumptions of characterization of the parties’ local operations in which indefinite causal order can be demonstrated with the quantum switch. To achieve this result, we introduce the concept of semi-device-independent causal inequalities and show that the correlations generated in a photonic quantum switch, in which all parties are able to collect local outcome statistics, achieve a violation of this inequality of 224 standard deviations. This result consists of the experimental demonstration of indefinite causal order with the fewest device-characterization assumptions to date.
Benefits of pre-trained mono- and cross-lingual speech representations for spoken language understanding of Dutch dysarthric speech

Abstract With the rise of deep learning, spoken language understanding (SLU) for command-and-control applications such as a voice-controlled virtual assistant can offer reliable hands-free operation to physically disabled individuals. However, due to data scarcity, it is still a challenge to process dysarthric speech. Pre-training (part of) the SLU model with supervised automatic speech recognition (ASR) targets or with self-supervised learning (SSL) may help to overcome a lack of data, but no research has shown which pre-training strategy performs better for SLU on dysarthric speech and to which extent the SLU task benefits from knowledge transfer from pre-training with dysarthric acoustic tasks. This work aims to compare different mono- or cross-lingual pre-training ( supervised and unsupervised ) methodologies and quantitatively investigates the benefits of pre-training for SLU tasks on Dutch dysarthric speech. The designed SLU systems consist of a pre-trained speech representations encoder and a SLU decoder to map encoded features to intents. Four types of pre-trained encoders, a mono-lingual time-delay neural network (TDNN) acoustic model, a mono-lingual transformer acoustic model, a cross-lingual transformer acoustic model (Whisper), and a cross-lingual SSL Wav2Vec2.0 model (XLSR-53), are evaluated complemented with three types of SLU decoders: non-negative matrix factorization (NMF), capsule networks, and long short-term memory (LSTM) networks. The acoustic analysis of the four pre-trained encoders are tested on Dutch dysarthric home-automation data with word error rate (WER) results to investigate the correlations of the dysarthric acoustic task (ASR) and the semantic task (SLU). By introducing the intelligibility score (IS) as a metric of the impairment severity, this paper further quantitatively analyzes dysarthria-severity-dependent models for SLU tasks.
A 3D shape measurement method for high-reflective surface based on dual-view multi-intensity projection

Abstract Phase shifting profilometry has been commonly used in three-dimensional shape measurement with the advantages of high-precision and non-contact. However, it is still challenging to measure high-reflective surface because image saturation will lead to absolute phase errors and reconstruction errors. In this paper, a dual-view multi-intensity projection method was proposed. Compared with the single-view method, the proposed method can reconstruct more points at each projection intensity especially for pixels around the specular angle and reduce the number of projections to reduce the time consumption. First, we established the dual-view structured light system consisting of two monocular systems that share the same projector. Subsequently, a dual-view saturated pixel judging method was proposed that enables the reconstruction results under two views to be combined without duplicate points. The multi-intensity projection method was adopted by reducing the input projection intensity step by step and reconstructing the remaining pixels around the specular angle. Finally, the reconstruction result can be obtained by stitching point clouds at each projection intensity. Experiments verified that the proposed method could improve the integrity of reconstructed point clouds and measurement efficiency.
A Comprehensive Review of Data‐Driven Co‐Speech Gesture Generation

Abstract Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co‐speech gestures is a long‐standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non‐periodic nature of human co‐speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep‐learning‐based generative models that benefit from the growing availability of data. This review article summarizes co‐speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule‐based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non‐linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human‐like motion; grounding the gesture in the co‐occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.
A Survey on Reinforcement Learning for Recommender Systems

Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years, owing to the interactive nature and autonomous learning ability. Empirical results show that RL-based recommendation methods often surpass most of supervised learning methods. Nevertheless, there are various challenges of applying RL in recommender systems. To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems. To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational recommendatin, sequential recommendation, and explainable recommendation. Furthermore, we systematically analyze the challenges and relevant solutions on the basis of existing literature. Finally, under discussion for open issues of RL and its limitations of recommender systems, we highlight some potential research directions in this field.
The evaluation of tourists' virtual reality experiences in the transition process to Metaverse

Purpose This study explores tourists' virtual reality experiences during the transition to the Metaverse. Design/methodology/approach Qualitative approach was employed to capture tourists' virtual reality experiences and knowledge of the Metaverse at two five-star hotels in Kusadasi (Republic of Turkey). The data were collected from Kusadasi using a purposive sampling technique. The research design focused on data collection with the structured interview technique. The interview form consisted of 7 questions in total, and a voice recorder was used to record the answers of the participants. After the first 4 questions were asked, the participants were presented a virtual reality experience with the virtual reality (VR) glasses. The interview was held face-to-face with thirty-five participants consisting of domestic and foreign tourists in two five-star hotels in the summer season of 2022. The collected data were analyzed with the content analysis technique and themes were created. Findings This study's findings enhance the conceptual capital in this emerging field and provide insights into many of the participants who have and have never experienced virtual reality applications and who are familiar and unfamiliar with the Metaverse as a concept. Research limitations/implications This study generates empirical data that informs contemporary debates about virtual reality and the Metaverse. Practical implications The findings show that most participants have never experienced a virtual reality application. Hotels and travel agencies should be aware of this new futuristic technology before the Metaverse transition. Metaverse is for generation Y and Z instead of Baby Boomers and generation X. Originality/value This study is unique in terms of depth and fills the gap as it provides useful insights regarding the evaluation of tourists' virtual reality experiences in the transition process to the Metaverse.
Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark

Warning: this paper contains content that may be offensive or upsetting.Among the safety concerns that hinder the deployment of open-domain dialog systems (e.g., offensive languages, biases, and toxic behaviors), social bias presents an insidious challenge.Addressing this challenge requires rigorous analyses and normative reasoning.In this paper, we focus our investigation on social bias measurement to facilitate the development of unbiased dialog systems.We first propose a novel DIAL-BIAS FRAMEWORK for analyzing the social bias in conversations using a holistic method beyond bias lexicons or dichotomous annotations.Leveraging the proposed framework, we further introduce the CDIAL-BIAS DATASET which is, to the best of our knowledge, the first annotated Chinese social bias dialog dataset.We also establish a finegrained dialog bias measurement benchmark, and conduct in-depth analyses to shed light on the utility of detailed annotations in the proposed dataset.Lastly, we evaluate several representative Chinese generative models using our classifiers to unveil the presence of social bias in these systems.
The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities

chapter Share on The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities Authors: Tim Berners-Lee Search about this author , James Hendler Search about this author , Ora Lassila Search about this author Authors Info & Claims Linking the World’s Information: Essays on Tim Berners-Lee’s Invention of the World Wide WebSeptember 2023Pages 91–103https://doi.org/10.1145/3591366.3591376Published:05 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for CRSs, revealing the inadequacy of the existing evaluation protocol. It might overemphasize the matching with ground-truth items annotated by humans while neglecting the interactive nature of CRSs. To overcome the limitation, we further propose an **i**nteractive **Eva**luation approach based on **L**L**M**s, named **iEvaLM**, which harnesses LLM-based user simulators. Our evaluation approach can simulate various system-user interaction scenarios. Through the experiments on two public CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and ChatGPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and realistic evaluation approach for future research about LLM-based CRSs.
Simulation-Based Story Generation with a Theory of Mind

Emergent narrative refers to simulation-based systems in which stories emerge from the autonomous interactions among character agents and/or the human player. Despite its advantages in interactive games, emergent narrative attracts concern about whether complex narratives emerge from arbitrary interactions. This problem can be alleviated if the character agents act according to what a story should require. Based on the observation that deliberate influence on others' minds constitutes the fabric of most stories, we propose that the capability for social influence is crucial for narrative agents. This paper presents a specialized planning technique called social planning, which allows a character agent to achieve its goal by reasoning about other characters' minds and influencing their actions. A prototype system based on social planning agents succeeds in generating simplified variations of Shakespeare's Othello through simulation.
Wavelet-based segmentation on the sphere

Segmentation, a useful/powerful technique in pattern recognition, is the process of identifying object outlines within images. There are a number of efficient algorithms for segmentation in Euclidean space that depend on the variational approach and partial differential equation modelling. Wavelets have been used successfully in various problems in image processing, including segmentation, inpainting, noise removal, super-resolution image restoration, and many others. Wavelets on the sphere have been developed to solve such problems for data defined on the sphere, which arise in numerous fields such as cosmology and geophysics. In this work, we propose a wavelet-based method to segment images on the sphere, accounting for the underlying geometry of spherical data. Our method is a direct extension of the tight-frame based segmentation method used to automatically identify tube-like structures such as blood vessels in medical imaging. It is compatible with any arbitrary type of wavelet frame defined on the sphere, such as axisymmetric wavelets, directional wavelets, curvelets, and hybrid wavelet constructions. Such an approach allows the desirable properties of wavelets to be naturally inherited in the segmentation process. In particular, directional wavelets and curvelets, which were designed to efficiently capture directional signal content, provide additional advantages in segmenting images containing prominent directional and curvilinear features. We present several numerical experiments, applying our wavelet-based segmentation method, as well as the common K-means method, on real-world spherical images. These experiments demonstrate the superiority of our method and show that it is capable of segmenting different kinds of spherical images, including those with prominent directional features. Moreover, our algorithm is efficient with convergence usually within a few iterations.