Reviewerly/exHarmony huggingface dataset:
GTGraffiti: Spray Painting Graffiti Art from Human Painting Motions with a Cable Driven Parallel Robot

We present GTGraffiti, a graffiti painting system from Georgia Tech that tackles challenges in art, hardware, and human-robot collaboration. The problem of painting graffiti in a human style is particularly challenging and requires a system-level approach because the robotics and art must be designed around each other. The robot must be highly dynamic over a large workspace while the artist must work within the robot's limitations. Our approach consists of three stages: artwork capture, robot hardware, and planning & control. We use motion capture to capture collaborator painting motions which are then composed and processed into a time-varying linear feedback controller for a cable-driven parallel robot (CDPR) to execute. In this work, we will describe the capturing process, the design and construction of a purpose-built CDPR, and the software for turning an artist's vision into control commands. Our work represents an important step towards faithfully recreating human graffiti artwork by demonstrating that we can reproduce artist motions up to 2m/s and 20m/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> within 9.3mm RMSE to paint artworks.
Dynamic Global Memory for Document-level Argument Extraction

Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.While recent work on document-level extraction has gone beyond single-sentence and increased the cross-sentence inference capability of end-to-end models, they are still restricted by certain input sequence length constraints and usually ignore the global context between events.To tackle this issue, we introduce a new global neural generation-based framework for document-level event argument extraction by constructing a document memory store to record the contextual event information and leveraging it to implicitly and explicitly help with decoding of arguments for later events.Empirical results show that our framework outperforms prior methods substantially and it is more robust to adversarially annotated examples with our constrained decoding design. 1
Overcoming a Theoretical Limitation of Self-Attention

Although transformers are remarkably effective for many tasks, there are some surprisingly easy-looking regular languages that they struggle with.Hahn shows that for languages where acceptance depends on a single input symbol, a transformer's classification decisions become less and less confident (that is, with crossentropy approaching 1 bit per string) as input strings get longer and longer.We examine this limitation using two languages: PAR-ITY, the language of bit strings with an odd number of 1s, and FIRST, the language of bit strings starting with a 1.We demonstrate three ways of overcoming the limitation suggested by Hahn's lemma.First, we settle an open question by constructing a transformer that recognizes PARITY with perfect accuracy, and similarly for FIRST.Second, we use layer normalization to bring the cross-entropy of both models arbitrarily close to zero.Third, when transformers need to focus on a single position, as for FIRST, we find that they can fail to generalize to longer strings; we offer a simple remedy to this problem that also improves length generalization in machine translation.
Natural Language Interfaces to Data

Recent advances in NLU and NLP have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems evolved from keyword search and focused on simple factual queries, the complexity of both the input sentences as well as the generated SQL queries has evolved over time. More recently, there has also been a lot of focus on using conversational interfaces for data analytics, empowering a line of non-technical users with quick insights into the data. There are three main challenges in natural language querying (NLQ): (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and (3) generating a structured query in the form of SQL or SPARQL. There are two main approaches for interpreting a user's NLQ. Rule-based systems make use of semantic indices, ontologies, and KGs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. With the advances in deep learning (DL)-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using DL models. Hybrid approaches that utilize both rule-based techniques as well as DL models are also emerging by combining the strengths of both approaches. Conversational interfaces are the next natural step to one-shot NLQ by exploiting query context between multiple turns of conversation for disambiguation. In this article, we review the background technologies that are used in natural language interfaces, and survey the different approaches to NLQ. We also describe conversational interfaces for data analytics and discuss several benchmarks used for NLQ research and evaluation.
BP-EVD: Forward Block-Output Propagation for Efficient Video Denoising

Denoising videos in real-time is critical in many applications, including robotics and medicine, where varying-light conditions, miniaturized sensors, and optics can substantially compromise image quality. This work proposes the first video denoising method based on a deep neural network that achieves state-of-the-art performance on dynamic scenes while running in real-time on VGA video resolution with no frame latency. The backbone of our method is a novel, remarkably simple, temporal network of cascaded blocks with forward block output propagation. We train our architecture with short, long, and global residual connections by minimizing the restoration loss of pairs of frames, leading to a more effective training across noise levels. It is robust to heavy noise following Poisson-Gaussian noise statistics. The algorithm is evaluated on RAW and RGB data. We propose a denoising algorithm that requires no future frames to denoise a current frame, reducing its latency considerably. The visual and quantitative results show that our algorithm achieves state-of-the-art performance among efficient algorithms, achieving from two-fold to two-orders-of-magnitude speed-ups on standard benchmarks for video denoising.
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation

Unlike literal expressions, idioms' meanings do not directly follow from their parts, posing a challenge for neural machine translation (NMT).NMT models are often unable to translate idioms accurately and over-generate compositional, literal translations.In this work, we investigate whether the non-compositionality of idioms is reflected in the mechanics of the dominant NMT model, Transformer, by analysing the hidden states and attention patterns for models with English as source language and one of seven European languages as target language.When Transformer emits a non-literal translation -i.e.identifies the expression as idiomatic -the encoder processes idioms more strongly as single lexical units compared to literal expressions.This manifests in idioms' parts being grouped through attention and in reduced interaction between idioms and their context.In the decoder's cross-attention, figurative inputs result in reduced attention on source-side tokens.These results suggest that Transformer's tendency to process idioms as compositional expressions contributes to literal translations of idioms.
Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel's efficiency, whereas the panel enhances the model's reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.
Multiparameter quantum metrology in the Heisenberg limit regime: Many-repetition scenario versus full optimization

We discuss the Heisenberg limit in the multiparameter metrology within two different paradigms: one where the measurement is repeated many times (so the Cram\'er-Rao bound is guaranteed to be asymptotically saturable). and the second one where all the resources are allocated into one experimental realization (analyzed with the mimimax approach). We investigate the potential advantage of measuring all the parameters simultaneously compared to estimating them individually, while spending the same total amount of resources. We show that in general the existence of such an advantage, its magnitude, and conditions under which it occurs depends on which of the two paradigms has been chosen. In particular, for the problem of magnetic field sensing using $N$ entangled spin $\frac{1}{2}$, we show that the predictions based purely on the Cram\'er-Rao formalism may be overly pessimistic in this matter: the minimax approach reveals the superiority of measuring all the parameters jointly whereas the Cram\'er-Rao approach indicates lack of such an advantage.
Construction and application of COVID-19 infectors activity information knowledge graph

During COVID-19 prevention and control, people need to be aware of the outbreak situation in their area to avoid being inconvenienced by the outbreak and even becoming infected. Thus, this project constructs a knowledge graph with COVID-19 infector activity information, by using the official flow information of the infected people from the provincial and municipal websites. This knowledge graph is the basis of the COVID-19 applications for tracing, visualization and reporting proposes. In the implementation process, we (1) collect a dataset with the information on COVID-19 cases from the prevention and control centers, (2) extract the entity elements with a Bert + BILSTM + CRF-based model, and (3) pre-process the dataset and construct a knowledge graph with manual annotation and human-based review. Finally, we use the knowledge graph to develop a web-based application to implement the question and answer, query, transmission path tracking and the "No.0" tracing infector functions.
Measurement-based generation and preservation of cat and grid states within a continuous-variable cluster state

We present an algorithm to reliably generate various quantum states critical to quantum error correction and universal continuous-variable (CV) quantum computing, such as Schrödinger cat states and Gottesman-Kitaev-Preskill (GKP) grid states, out of Gaussian CV cluster states. Our algorithm is based on the Photon-counting-Assisted Node-Teleportation Method (PhANTM), which uses standard Gaussian information processing on the cluster state with the only addition of local photon-number-resolving measurements. We show that PhANTM can apply polynomial gates and embed cat states within the cluster. This method stabilizes cat states against Gaussian noise and perpetuates non-Gaussianity within the cluster. We show that existing protocols for breeding cat states can be embedded into cluster state processing using PhANTM.
Data Privacy Security Mechanism of Industrial Internet of Things Based on Block Chain

In order to solve the problem that data of the industrial Internet of Things (IIoT) is easily tampered with and therefore, the authenticity of data may be questioned, a data-privacy security mechanism of the IIoT based on blockchain is proposed. At the same time, to solve the problem of master node selection and low efficiency in the practical Byzantine fault tolerance (PBFT) algorithm, a reward mechanism based on node behavior is introduced and an improved PBFT algorithm is proposed. The improved PBFT algorithm is more efficient, in line with the application scenarios of the IIoT. Comparative analysis results showed that the proposed blockchain-based data-privacy security mechanism of the IIoT is superior to other models in terms of consensus efficiency, throughput, and block generation speed.
Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

We propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work, we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive and illocutionary process of answering questions. Specifically, our contribution is an approach to frame illocution in a computer-friendly way, to achieve user-centrality with statistical question answering. Indeed, we frame the illocution of an explanatory process as that mechanism responsible for anticipating the needs of the explainee in the form of unposed, implicit, archetypal questions, hence improving the user-centrality of the underlying explanatory process. Therefore, we hypothesise that if an explanatory process is an illocutionary act of providing content-giving answers to questions, and illocution is as we defined it, the more explicit and implicit questions can be answered by an explanatory tool, the more usable (as per ISO 9241-210) its explanations. We tested our hypothesis with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that increasing the illocutionary power of an explanatory tool can produce statistically significant improvements (hence with a P value lower than .05) on effectiveness. This, combined with a visible alignment between the increments in effectiveness and satisfaction, suggests that our understanding of illocution can be correct, giving evidence in favour of our theory.
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation

Standard automatic metrics, e.g., BLEU, are not reliable for document-level MT evaluation.They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations.This paper introduces a novel automatic metric BLONDE 1 to widen the scope of automatic MT evaluation from the sentence to the document level.BLONDE takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans.We conduct extensive comparisons on a newly constructed document-level translation dataset BWB.The experimental results show that BLONDE possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances.In a large-scale human study, BLONDE also achieves significantly higher Pearson's r correlation with human judgments compared to previous metrics.
RACAI at SemEval-2022 Task 11: Complex named entity recognition using a lateral inhibition mechanism

This paper presents RACAI's system used for the shared task of "Multilingual Complex Named Entity Recognition (MultiCoNER)", organized as part of "The 16th International Workshop on Semantic Evaluation (SemEval 2022)".The system employs a novel layer inspired by the biological mechanism of lateral inhibition.This allowed the system to achieve good results without any additional resources apart from the provided training data.In addition to the system's architecture, results are provided as well as observations regarding the provided dataset.
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.Recent works have shown promising improvements in factuality error identification using text or dependency arc entailments; however, they do not consider the entire semantic graph simultaneously.To this end, we propose FACT-GRAPH, a method that decomposes the document and the summary into structured meaning representations (MR), which are more suitable for factuality evaluation.MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity.FACTGRAPH encodes such graphs using a graph encoder augmented with structure-aware adapters to capture interactions among the concepts based on the graph connectivity, along with text representations using an adapter-based text encoder.Experiments on different benchmarks for evaluating factuality show that FACTGRAPH outperforms previous approaches by up to 15%.Furthermore, FACT-GRAPH improves performance on identifying content verifiability errors and better captures subsentence-level factual inconsistencies.
S-DABT: Schedule and Dependency-aware Bug Triage in open-source bug tracking systems

Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study on four open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and Mozilla, shows that taking into account the schedules of the developers decreases the average bug fixing times. We find that S-DABT leads to a high level of developer utilization through a fair distribution of the tasks among the developers and efficient use of the free spots in their schedules. Via the simulation of the issue tracking system, we also show how incorporating the schedule in the model formulation reduces the bug fixing time, improves the assignment accuracy, and utilizes the capability of each developer without much comprising in the model run times. We find that S-DABT decreases the complexity of the bug dependency graph by prioritizing blocking bugs and effectively reduces the infeasible assignment ratio due to bug dependencies. Consequently, we recommend considering developers' schedules while automating bug triage.
A dual learning-based recommendation approach

Data sparsity and cold start are two critical issues which need to be addressed in recommender systems (RSs). Currently, most methods address these issues by applying user history files or some side information to improve the user model and complete the rating matrix. However, such methods cannot perform well when labeled data is scarce or unavailable. In this paper, we propose a dual learning-based recommendation approach (DLRA). DLRA can trigger initial recommendation and improve the quality of recommendations by using the duality characteristics of RSs, even when the available labeled information is scarce. Specifically, DLRA regards the recommendation task as two independent subtasks — primal task and dual task, and these two tasks show strong duality in DLRA. The primal task is item-centered which aims to find users who can rate high for items, while the dual task is user-centered that aims to recommend the most favorite items to users. These two tasks have strong dualities in terms of the recommendation space, selection probability and recommendation basis. Based on these dualities, we design three dual learning strategies to couple the whole recommendation process and realize the self-tuning and self-improvement of each task model, and finally optimize the whole recommendation model. Based on the dataset of Movielens and BookCrossing, we simulate data sparsity and cold start recommendation scenarios, the experimental results show that DLRA achieves substantial improvement when the labeled data is scare, and it outperforms other hybrid recommendation approaches and deep learning strategies with a smaller predictive error as well as better recommendation accuracy.
Probabilistic Hierarchical Quantum Information Splitting of Arbitrary Multi-Qubit States

By utilizing the non-maximally entangled four-qubit cluster states as the quantum channel, we first propose a hierarchical quantum information splitting scheme of arbitrary three-qubit states among three agents with a certain probability. Then we generalize the scheme to arbitrary multi-qubit states. Hierarchy is reflected on the different abilities of agents to restore the target state. The high-grade agent only needs the help of one low-grade agent, while the low-grade agent requires all the other agents’ assistance. The designated receiver performs positive operator-valued measurement (POVM) which is elaborately constructed with the aid of Hadamard matrix. It is worth mentioning that a general expression of recovery operation is derived to disclose the relationship with measurement outcomes. Moreover, the scheme is extended to multiple agents by means of the symmetry of cluster states.
Gastroenterology in the Metaverse: The dawn of a new era?

2021 is known as the first Year of the Metaverse, and around the world, internet giants are eager to devote themselves to it. In this review, we will introduce the concept, current development, and application of the Metaverse and the use of the current basic technologies in the medical field, such as virtual reality and telemedicine. We also probe into the new model of gastroenterology in the future era of the Metaverse.