Reviewerly/exHarmony huggingface dataset:
Reading the Manual: Event Extraction as Definition Comprehension

We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals.Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, Some person was born in some location at some time.We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.ea rl ie r in th e w ee k someone AGENT 1 killed someone else VICTIM 2 with something INSTRUMENT 3 in some place PLACE 4 at some time
Legal Aspects of Central Bank Digital Currency

This paper analyzes the legal foundations of central bank digital currency (CBDC) under central bank and monetary law. Absent strong legal foundations, the issuance of CBDC poses legal, financial and reputational risks for central banks. While the appropriate design of the legal framework will up to a degree depend on the design features of the CBDC, some general conclusions can be made. First, most central bank laws do not currently authorize the issuance of CBDC to the general public. Second, from a monetary law perspective, it is not evident that “currency” status can be attributed to CBDC. While the central bank law issue can be solved through rather straithforward law reform, the monetary law issue poses fundmental legal policy challenges.
Variational quantum algorithm for nonequilibrium steady states

We propose a quantum-classical hybrid algorithm to simulate the nonequilibrium steady state of an open quantum many-body system, named the dissipative-system variational quantum eigensolver (dVQE). To employ the variational optimization technique for a unitary quantum circuit, we map a mixed state into a pure state with a doubled number of qubits and design the unitary quantum circuit to fulfill the requirements for a density matrix. This allows us to define a cost function that consists of the time evolution generator of the quantum master equation. Evaluation of physical observables is, in turn, carried out by a quantum circuit with the original number of qubits. We demonstrate our dVQE scheme by both numerical simulation on a classical computer and actual quantum simulation that makes use of the device provided in Rigetti quantum cloud service.2 MoreReceived 28 October 2019Accepted 10 November 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043289Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum simulationTechniquesQuantum master equationCondensed Matter & Materials PhysicsQuantum Information
Metrological Detection of Multipartite Entanglement from Young Diagrams

We characterize metrologically useful multipartite entanglement by representing partitions with Young diagrams. We derive entanglement witnesses that are sensitive to the shape of Young diagrams and show that Dyson's rank acts as a resource for quantum metrology. Common quantifiers, such as the entanglement depth and $k$-separability are contained in this approach as the diagram's width and height. Our methods are experimentally accessible in a wide range of atomic systems, as we illustrate by analyzing published data on the quantum Fisher information and spin-squeezing coefficients.
SCNet: Training Inference Sample Consistency for Instance Segmentation

Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingering issues regarding the disparity in the Intersection-over-Union (IoU) distribution of the samples between training and inference. This disparity can potentially exacerbate detection accuracy. This paper proposes an architecture referred to as Sample Consistency Network (SCNet) to ensure that the IoU distribution of the samples at training time is close to that at inference time. Furthermore, SCNet incorporates feature relay and utilizes global contextual information to further reinforce the reciprocal relationships among classifying, detecting, and segmenting sub-tasks. Extensive experiments on the standard COCO dataset reveal the effectiveness of the proposed method over multiple evaluation metrics, including box AP, mask AP, and inference speed. In particular, while running 38\% faster, the proposed SCNet improves the AP of the box and mask predictions by respectively 1.3 and 2.3 points compared to the strong Cascade Mask R-CNN baseline. Code is available at https://github.com/thangvubk/SCNet.
Service Level Agreements for 5G and Beyond: Overview, Challenges and Enablers of 5G-Healthcare Systems

5G and beyond networks will transform the healthcare sector by opening possibilities for novel use cases and applications. Service level agreements (SLAs) can enable 5G-enabled medical device use cases by documenting how a medical device communication requirements are met by the unique characteristics of 5G networks and the roles and responsibilities of the stakeholders involved in offering safe and effective 5G-enabled healthcare to patients. However, there are gaps in this space that should be addressed to facilitate the efficient implementation of 5G technology in healthcare. Current literature is scarce regarding SLAs for 5G and is absent regarding SLAs for 5G-enabled medical devices. This paper aims to bridge these gaps by identifying key challenges, providing insight, and describing open research questions related to SLAs in 5G and specifically 5G-healthcare systems. This is helpful to network service providers, users, and regulatory authorities in developing, managing, monitoring, and evaluating SLAs in 5G-enabled medical systems.
Investigating the Impact of the Internet of Things in Higher Education Environment

The developments in information and communications technologies (ICT) come with changes in all the fields of life, including the education system. At the same time, the IoT (Internet of Things) is turning ever more important as regards the overall benefits that it brings to smart cities as well as the education system. Starting March 2020, the COVID-19 pandemic has caused fast-paced changes in education and forced the ICT integration in higher education. Despite that, the IoT is still at the beginning in the education system and the impact of its adoption is far from being fully understood. This article is aimed at describing a smart education environment and the extent to which the IOT is conducive to this desideratum. The paper also identifies and describes the most important benefits and challenges related to the adoption of the IoT in higher education. In order to analyze the impact of the IoT adoption in the education environment, the authors propose an assessment model based on six hypotheses, including their definitions and descriptions. They are validated against the Romanian higher education system, as well as a set of survey data. Structural equation modelling (SEM) is used in the study to validate the suggested model as well as to determine how the adoption of the IoT relates to intra- and extra-university connectivity, to attracting additional resources, to the teaching and learning activities, to data security and integrity as well as to education policies. The last part of the paper dwells on the analysis of a set of survey data and of the hypotheses shaped herein. The paper also includes recommendations, as well as the main conclusions, limitations and future research directions. It contributes both from the theoretical and practical perspective to the development of smart universities in the future.
RobOT: Robustness-Oriented Testing for Deep Learning Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by proposing a novel testing framework called Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement. RobOT utilizes the proposed metric to automatically generate test cases valuable for improving model robustness. The proposed metric is also a strong indicator on how well robustness improvement has converged through testing. Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.
The Labeled Multiple Canonical Correlation Analysis for Information Fusion

The objective of multimodal information fusion is to mathematically analyze information carried in different sources and create a new representation that will be more effectively utilized in pattern recognition and other multimedia information processing tasks. In this paper, we introduce a new method for multimodal information fusion and representation based on the Labeled Multiple Canonical Correlation Analysis (LMCCA). By incorporating class label information of the training samples, the proposed LMCCA ensures that the fused features carry discriminative characteristics of the multimodal information representations and are capable of providing superior recognition performance. We implement a prototype of LMCCA to demonstrate its effectiveness on handwritten digit recognition, face recognition, and object recognition utilizing multiple features, bimodal human emotion recognition involving information from both audio and visual domains. The generic nature of LMCCA allows it to take as input features extracted by any means, including those by deep learning (DL) methods. Experimental results show that the proposed method enhanced the performance of both statistical machine learning methods, and methods based on DL.
The Teaching Digital Competence of Health Sciences Teachers. A Study at Andalusian Universities (Spain)

The impact and benefit that information and communication technologies (ICT) have in the educational field require new teaching skills. This fact has been increased by the recent crisis caused by COVID-19. This study tries to investigate the level of digital teaching competence (DTC) of Higher Education teachers of Health Sciences, and its relationship with several variables. For this, it has the participation of 300 teachers from the 9 universities of Andalusia (Spain). The research is structured through a descriptive (RQ1) and inferential (RQ2) design. The answers given to the DigCompEdu Check-In questionnaire adapted to the Spanish context are analyzed. The results, which show high levels of reliability of the questionnaire (Cronbach and McDonald) and validity (CFA), indicate that the level of competence is basic-intermediate. In addition, the area in which teachers excel is digital resources. For this reason, it is proposed to structure personalized training plans and continue expanding the characteristics of this study at an international level.
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP

Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks.In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks.We introduce CROSSFIT , a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols.To instantiate different seen/unseen task partitions in CROSS-FIT and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from openaccess NLP datasets and converted to a unified text-to-text format.Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks.We also observe that the selection of upstream learning tasks can significantly influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability. 1
Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs

Abstractive conversation summarization has received much attention recently.However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-human interactions.To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples ("WHO-DOING-WHAT") in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information.Experiments show that our proposed models outperform state-of-theart methods and generalize well in other domains in terms of both automatic evaluations and human judgments.We have publicly released our code at https://github.com/GT-SALT/Structure-Aware-BART.
Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction

The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations.To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extraction to discover entities, relations, and events with the help of a pre-trained AMR parser.Our framework consists of two novel components: 1) an AMR based semantic graph aggregator to let the candidate entity and event trigger nodes collect neighborhood information from AMR graph for passing message among related knowledge elements; 2) an AMR guided graph decoder to extract knowledge elements based on the order decided by the hierarchical structures in AMR.Experiments on multiple datasets have shown that the AMR graph encoder and decoder have provided significant gains and our approach has achieved new state-of-the-art performance on all IE subtasks 1 .
Introducing CAD: the Contextual Abuse Dataset

Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic.Progress in automatically detecting and classifying abusive content is often held back by the lack of high quality and detailed datasets.We introduce a new dataset of primarily English Reddit entries which addresses several limitations of prior work.It (1) contains six conceptually distinct primary categories as well as secondary categories, (2) has labels annotated in the context of the conversation thread, (3) contains rationales and (4) uses an expert-driven group-adjudication process for high quality annotations.We report several baseline models to benchmark the work of future researchers.The annotated dataset, annotation guidelines, models and code are freely available.
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation

In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence.While many attempts to understand the internal workings of NMT models have been made, none of them explicitly evaluates relative source and target contributions to a generation decision.We argue that this relative contribution can be evaluated by adopting a variant of Layerwise Relevance Propagation (LRP).Its underlying 'conservation principle' makes relevance propagation unique: differently from other methods, it evaluates not an abstract quantity reflecting token importance, but the proportion of each token's influence.We extend LRP to the Transformer and conduct an analysis of NMT models which explicitly evaluates the source and target relative contributions to the generation process.We analyze changes in these contributions when conditioning on different types of prefixes, when varying the training objective or the amount of training data, and during the training process.We find that models trained with more data tend to rely on source information more and to have more sharp token contributions; the training process is non-monotonic with several stages of different nature. 1
MLBiNet: A Cross-Sentence Collective Event Detection Network

We consider the problem of collectively detecting multiple events, particularly in crosssentence settings.The key to dealing with the problem is to encode semantic information and model event inter-dependency at a documentlevel.In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously.Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence.Secondly, an information aggregation module is employed to aggregate sentencelevel semantic and event tag information.Finally, we stack multiple bidirectional decoders and feed cross-sentence information, forming a multi-layer bidirectional tagging architecture to iteratively propagate information across sentences.We show that our approach provides significant improvement in performance compared to the current state-of-the-art results 1 .
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency

We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion, and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we highlight the fundamental difference between inverse and forward projection while modeling the individual motion of each rigid object, and propose a geometrically correct projection pipeline using a neural forward projection module. Second, we design a unified instance-aware photometric and geometric consistency loss that holistically imposes self-supervisory signals for every background and object region. Lastly, we introduce a general-purpose auto-annotation scheme using any off-the-shelf instance segmentation and optical flow models to produce video instance segmentation maps that will be utilized as input to our training pipeline. These proposed elements are validated in a detailed ablation study. Through extensive experiments conducted on the KITTI and Cityscapes dataset, our framework is shown to outperform the state-of-the-art depth and motion estimation methods. Our code, dataset, and models are publicly available.
Value-Decomposition Multi-Agent Actor-Critics

The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative function approximator and achieves the best performance on the StarCraft II micromanagement testbed, a common MARL benchmark. However, our experiments demonstrate that, in some cases, QMIX performs sub-optimally with the A2C framework, a training paradigm that promotes algorithm training efficiency. To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critic methods that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critic (VDAC). We evaluate VDAC on the StarCraft II micromanagement task and demonstrate that the proposed framework improves median performance over other actor-critic methods. Furthermore, we use a set of ablation experiments to identify the key factors that contribute to the performance of VDAC.
Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series

Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system. Anomaly data can be detected if its values lie outside of a normal pattern distribution. We developed a median-based statistical outlier detection approach using a sliding window technique. In order to fill anomalies, various interpolation techniques were considered. Our proposed framework exhibited promising results after evaluating with F1-score and root mean square error (RMSE) based on our artificially induced data points. The present system can also be easily applied to various patterns of hydrological time series with diverse choices of internal methods and fine-tuned parameters. Specifically, the Spline interpolation method yielded a superior performance on non-cyclical data while the long short-term memory (LSTM) outperformed other interpolation methods on a distinct tidal data pattern.
Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation

In this paper, we focus on identifying the limitations and shortcomings of existing deepfake detection frameworks. We identified some key problems surrounding deepfake detection through quantitative and qualitative analysis of existing methods and datasets. We found that deepfake datasets are highly oversampled, causing models to become easily overfitted. The datasets are created using a small set of real faces to generate multiple fake samples. When trained on these datasets, models tend to memorize the actors’ faces and labels instead of learning fake features. To mitigate this problem, we propose a simple data augmentation method termed Face-Cutout. Our method dynamically cuts out regions of an image using the face landmark information. It helps the model selectively attend to only the relevant regions of the input. Our evaluation experiments show that Face-Cutout can successfully improve the data variation and alleviate the problem of overfitting. Our method achieves a reduction in LogLoss of 15.2% to 35.3% on different datasets, compared to other occlusion-based techniques. Moreover, we also propose a general-purpose data pre-processing guideline to train and evaluate existing architectures allowing us to improve the generalizability of these models for deepfake detection.