Reviewerly/exHarmony huggingface dataset:
GRUEN for Evaluating Linguistic Quality of Generated Text

Automatic evaluation metrics are indispensable for evaluating generated text.To date, these metrics have focused almost exclusively on the content selection aspect of the system output, ignoring the linguistic quality aspect altogether.We bridge this gap by proposing GRUEN for evaluating Grammaticality, non-Redundancy, focUs, structure and coherENce of generated text. 1 GRUEN utilizes a BERTbased model and a class of syntactic, semantic, and contextual features to examine the system output.Unlike most existing evaluation metrics which require human references as an input, GRUEN is reference-less and requires only the system output.Besides, it has the advantage of being unsupervised, deterministic, and adaptable to various tasks.Experiments on seven datasets over four language generation tasks show that the proposed metric correlates highly with human judgments. 2
UNSOUND INFERENCES MAKE PROOFS SHORTER

We give examples of calculi that extend Gentzen's sequent calculus LK by unsound quantifier inferences in such a way that (i) derivations lead only to true sequents, and (ii) proofs therein are non-elementarily shorter than LK-proofs.
Hate Speech Detection Based on Sentiment Knowledge Sharing

The wanton spread of hate speech on the internet brings great harm to society and families.It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech.While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training.In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection.In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing.While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech.Experimental results on two public datasets demonstrate the effectiveness of our model.
An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments

This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference road. First, a vehicle kinematic model with road coordinates is established to describe the lateral movement of the vehicle. Then, nonlinear optimization based on a vehicle kinematic model in the space domain is employed to smooth the reference road. Second, a multilayered search algorithm is applied in the lateral-space domain to deal with obstacles and find a suitable path boundary. Then, the optimized path planner calculates the optimal path by considering the distance to the reference road and the curvature constraints. Furthermore, the optimized speed planner takes into account the speed boundary in the space domain and the constraints on vehicle acceleration. The optimal speed profile is obtained by using a numerical optimization method. Furthermore, a motion controller based on a kinematic error model is proposed to follow the desired trajectory. Finally, the experimental results show the effectiveness of the proposed trajectory planner and motion controller framework in handling typical scenarios and avoiding obstacles safely and smoothly on the reference road and in unstructured environments.
RevCore: Review-Augmented Conversational Recommendation

Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items.Incorporating external information (e.g., reviews) is a potential solution to alleviate this problem.Given that reviews often provide a rich and detailed user experience on different interests, they are potential ideal resources for providing high-quality recommendations within an informative conversation.In this paper, we design a novel end-to-end framework, namely, Review-augmented Conversational Recommender (RevCore), where reviews are seamlessly incorporated to enrich item information and assist in generating both coherent and informative responses.In detail, we extract sentiment-consistent reviews, perform review-enriched and entity-based recommendations for item suggestions, as well as use a review-attentive encoder-decoder for response generation.Experimental results demonstrate the superiority of our approach in yielding better performance on both recommendation and conversation responding. 1The Heat Bad Boys Paul Feig Bridesmaids lookup
Learning Shared Semantic Space for Speech-to-Text Translation

Having numerous potential applications and great impact, end-to-end speech translation (ST) has long been treated as an independent task, failing to fully draw strength from the rapid advances of its sibling -text machine translation (MT).With text and audio inputs represented differently, the modality gap has rendered MT data and its end-to-end models incompatible with their ST counterparts.In observation of this obstacle, we propose to bridge this representation gap with Chimera.By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art.Specifically, Chimera obtains 27.1 BLEU on MuST-C EN-DE, improving the SOTA by a +1.9 BLEU margin.Further experimental analyses demonstrate that the shared semantic space indeed conveys common knowledge between these two tasks and thus paves a new way for augmenting training resources across modalities. 1
Dynamic defenses in cyber security: Techniques, methods and challenges

Driven by the rapid development of the Internet of Things, cloud computing and other emerging technologies, the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities. However, security problems in cyberspace are becoming serious, and traditional defense measures (e.g., firewall, intrusion detection systems, and security audits) often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence. By constructing and implementing the diverse strategy of dynamic transformation, the configuration characteristics of systems are constantly changing, and the probability of vulnerability exposure is increasing. Therefore, the difficulty and cost of attack are increasing, which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace. Nonetheless, few related works systematically introduce dynamic defense mechanisms for cyber security. The related concepts and development strategies of dynamic defense are rarely analyzed and summarized. To bridge this gap, we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security. Specifically, we firstly introduce basic concepts and define dynamic defense in cyber security. Next, we review the architectures, enabling techniques and methods for moving target defense and mimic defense. This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense. Finally, we discuss some open challenges and opportunities for dynamic defense in cyber security.
Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome.This review is a summary of the use of deep learning models for diagnosis, prognosis, and metastasis detection for oral SCC by analyzing information from pathological and radiographic images. Specifically, deep learning has been used to classify different cell types, to differentiate cancer cells from nonmalignant cells, and to identify oral SCC from other cancer types. It can also be used to predict survival, to differentiate between tumor grades, and to detect lymph node metastasis. In general, the performance of these deep learning models has an accuracy ranging from 77.89% to 97.51% and 76% to 94.2% with the use of pathological and radiographic images, respectively. The review also discusses the importance of using good-quality clinical images in sufficient quantity on model performance.Applying pathological and radiographic images in deep learning models for diagnosis and prognosis of oral SCC has been explored, and most studies report results showing good classification accuracy. The successful use of deep learning in these areas has a high clinical translatability in the improvement of patient care.
Unsupervised event classification with graphs on classical and photonic quantum computers

A bstract Photonic Quantum Computers provide several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a #P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the $$ \mathcal{O} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> </mml:math> complexity, with respect to the sample’s feature-vector length, from $$ \mathcal{O}(N) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mfenced> <mml:mi>N</mml:mi> </mml:mfenced> </mml:math> to $$ \mathcal{O}\left(\log (N)\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mfenced> <mml:mrow> <mml:mo>log</mml:mo> <mml:mfenced> <mml:mi>N</mml:mi> </mml:mfenced> </mml:mrow> </mml:mfenced> </mml:math> .
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead.In this study, we present PPTOD, a unified plug-andplay model for task-oriented dialogue.In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora.We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification.Experimental results show that PPTOD achieves new state of the art on all evaluated tasks in both high-resource and lowresource scenarios.Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators.
Emotionally charged text classification with deep learning and sentiment semantic

Abstract Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional document representation, such as vector sequences or matrices combined with document sentiment, should be explored. In this paper, we show that documents can be represented as a sequence of vectors carrying semantic meaning and classified using a recurrent neural network that recognizes long-range relationships. We show that in this representation, additional sentiment vectors can be easily attached as a fully connected layer to the word vectors to further improve classification accuracy. On the UCI sentiment labelled dataset, using the sequence of vectors alone achieved an accuracy of 85.6%, which is better than 80.7% from ridge regression classifier—the best among the classical technique we tested. Additional sentiment information further increases accuracy to 86.3%. On our suicide notes dataset, the best classical technique—the Naíve Bayes Bernoulli classifier, achieves accuracy of 71.3%, while our classifier, incorporating semantic and sentiment information, exceeds that at 75% accuracy.
Grey Correlation Analysis of Haze Impact Factor PM2.5

In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a “local climate” and aim to explore this series of scientific problems. This paper uses remote sensing and data mining methods to study the correlation between haze weather and local climate. First, we select Beijing, China and its surrounding areas (East longitude 115°20′11″–117°40′35″, North latitude 39°21′11″–41°7′51″) as the study area. We collected data from meteorological stations in Beijing and Xianghe from March 2014 to February 2015, and analyzed the meteorological parameters through correlation analysis and a grey correlation model. We study the correlation between the six influencing factors of temperature, dew point, humidity, wind speed, air pressure and visibility and PM2.5, so as to analyze the correlation between haze weather and local climate more comprehensively. The results show that the influence of each index on PM2.5 in descending order is air pressure, wind speed, humidity, dew point, temperature and visibility. The qualitative analysis results confirm each other. Among them, air pressure (correlation 0.771) has the greatest impact on haze weather, and visibility (correlation 0.511) is the weakest.
Neural attention for image captioning: review of outstanding methods

Image captioning is the task of automatically generating sentences that describe an input image in the best way possible. The most successful techniques for automatically generating image captions have recently used attentive deep learning models. There are variations in the way deep learning models with attention are designed. In this survey, we provide a review of literature related to attentive deep learning models for image captioning. Instead of offering a comprehensive review of all prior work on deep image captioning models, we explain various types of attention mechanisms used for the task of image captioning in deep learning models. The most successful deep learning models used for image captioning follow the encoder-decoder architecture, although there are differences in the way these models employ attention mechanisms. Via analysis on performance results from different attentive deep models for image captioning, we aim at finding the most successful types of attention mechanisms in deep models for image captioning. Soft attention, bottom-up attention, and multi-head attention are the types of attention mechanism widely used in state-of-the-art attentive deep learning models for image captioning. At the current time, the best results are achieved from variants of multi-head attention with bottom-up attention.
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

Math word problem (MWP) solving faces a dilemma in number representation learning.In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solving usually replace real numbers with symbolic placeholders to focus on logic reasoning.However, different from common symbolic reasoning tasks like program synthesis and knowledge graph reasoning, MWP solving has extra requirements in numerical reasoning.In other words, instead of the number value itself, it is the reusable numerical property that matters more in numerical reasoning.Therefore, we argue that injecting numerical properties into symbolic placeholders with contextualized representation learning schema can provide a way out of the dilemma in the number representation issue here.In this work, we introduce this idea to the popular pre-training language model (PLM) techniques and build MWP-BERT, an effective contextual number representation PLM.We demonstrate the effectiveness of our MWP-BERT on MWP solving and several MWP-specific understanding tasks on both English and Chinese benchmarks.
The effect of process quality improvement and lean practices on competitive performance in the UAE healthcare industry

The purpose of this paper is to empirically examine a theoretical model which identifies the effect of process quality improvement and lean practices on competitive performance in the healthcare industry in the United Arab Emirates (UAE). The study uses a quantitative research technique with convenient cluster sampling through applying a descriptive, causal and analytical research design. A valid sample size of 270 respondents is used for analysis by linear regression and hypothesis testing using SPSS. The results indicate a direct significant relationship between process quality improvement and a direct significant relationship between lean practices and competitive performance. Given the substantial resources spent and efforts to improve healthcare quality, the absence of studies demonstrating the impact of quality-related operations and activities would require future research. Hospitals in one city in UAE have access that limits the research results. It is recommended that future research assess more variables dependency on the healthcare sector that can affect increase competitiveness. The research provides some managerial implications that could help hospital managerial members improve their healthcare delivery system's leanness and quality improvement to get competitiveness. Quality improvement and lean practices help increase competitive performance and can assist the healthcare sector in providing better service using these practices that have never been considered in research.
Persistent Identification of Instruments

Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
Monad transformers and modular algebraic effects: what binds them together

For over two decades, monad transformers have been the main modular approach for expressing purely functional side-effects in Haskell. Yet, in recent years algebraic effects have emerged as an alternative whose popularity is growing.
Adaptive routing for quantum memory failures in the quantum Internet

We define an adaptive routing method for the management of quantum memory failures in the quantum Internet. In the quantum Internet, the entangled quantum states are stored in the local quantum memories of the quantum nodes. A quantum memory failure in a particular quantum node can destroy several entangled connections in the entangled network. A quantum memory failure event makes the immediate and efficient determination of shortest replacement paths an emerging issue in a quantum Internet scenario. The replacement paths omit those nodes that are affected by the quantum memory failure to provide a seamless network transmission. In the proposed solution, the shortest paths are determined by a base-graph, which contains all information about the overlay quantum network. The method provides efficient adaptive routing in quantum memory failure scenarios of the quantum Internet. The results can be straightforwardly applied in practical quantum networks, including long-distance quantum communications.
Sub-ns timing accuracy for satellite quantum communications

Satellite quantum communications have rapidly evolved in the past few years, culminating in the proposal, development, and deployment of satellite missions dedicated to quantum key distribution and the realization of fundamental tests of quantum mechanics in space. However, in comparison with the more mature technology based on fiber optics, several challenges are still open, such as the capability of detecting, with high temporal accuracy, single photons coming from orbiting terminals. Satellite laser ranging, commonly used to estimate satellite distance, could also be exploited to overcome this challenge. For example, high repetition rates and a low background noise can be obtained by determining the time-of-flight of faint laser pulses that are retro-reflected by geodynamics satellites and then detected on Earth at the single-photon level. Here we report an experiment with regard to achieving a temporal accuracy of approximately 230 ps in the detection of an optical signal of few photons per pulse reflected by satellites in medium Earth orbit, at a distance exceeding 7500 km, by using commercially available detectors. Lastly, the performance of the Matera Laser Ranging Observatory is evaluated in terms of the detection rate and the signal-to-noise ratio for satellite quantum communications.
Remote preparation for single-photon two-qubit hybrid state with hyperentanglement via linear-optical elements

Linear-optical-based quantum information processing has attached much attention since photon is an ideal medium for transmitting quantum information remotely. Until now, there are some important works in quantum state remote preparation, the method for reconstructing quantum state deterministically via linear optics. However, most of the methods are protocols to prepare single-qubit states remotely via linear-optical elements. In this article, we investigate the methods to prepare two-qubit hybrid states remotely. We present a deterministic remote state preparation scheme for an arbitrary two-qubit hybrid state via a hyperentangled Bell state, resorting to linear-optical elements only. The sender rotates the spatial-mode state and polarization state of the hyperentangled photon respectively in accordance with his knowledge of the two-qubit hybrid state, and the receiver can reconstruct the original two-qubit hybrid state by applying appropriate recovery operations. Moreover, we discuss the remote state preparation scheme for the two-qubit hybrid state via partially hyperentangled Bell state.