Reviewerly/exHarmony huggingface dataset:
Multiple Level Hierarchical Network-Based Clause Selection for Emotion Cause Extraction

Emotion cause extraction is one of the most important applications in natural language processing tasks. It is a difficult challenge due to the complex semantic information between emotion description and the whole document. Previous approaches have revealed that clause is an important indicator of emotion-cause extraction. As such, selecting a suitable clause has become an interesting challenge. Different from existed clause selection methods which mainly focus on semantic similarity between clause and emotion description, in this paper, we proposed a hierarchical network-based clause selection framework in which the similarity is calculated by considering document features from word’s position, different semantic levels (word and phrase), and interaction among clauses, respectively. Experimental study on a Chinese emotion-cause corpus has shown the proposed framework’s effectiveness and the potential of integrating different level’s information.
Feedpal

Use of chatbots in different spheres of life is continuously increasing since a couple of years. We attempt to understand the potential of chatbots for breastfeeding education by conducting an Wizard-of-Oz experiment with 22 participants. Our participants included breastfeeding mothers and community health workers from the slum areas of Delhi, India. We prototyped our chatbot as an interactive question-answering application and analyzed users' interaction patterns, perceptions, and contexts of use. The chatbot use cases emerged primarily as the first line of support. The participants, especially the mothers, were enthusiastic with the opportunity to ask questions and get reliable answers. We also observed the influencing role of female relative, e.g. mothers-in-law, in breastfeeding practices. Our analysis of user information-seeking suggests that a majority of questions (88%) are of nature that can be answered by a chatbot application. We further observe that the queries are embedded deeply into myths and existing belief systems. Therefore requiring the designers to focus on subtle aspects for providing information such as positive reinforcement and contextual sensitivity. Further, we discuss, different societal and ethical issues associated with Chatbot usage for a public health topic such as breastfeeding education.
All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA

We propose a simple hardware architecture for solving exclusive OR (XOR) tasks in a single step by using a single photonic spiking neuron based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA) subject to dual-polarized pulsed optical injection. We model the inhibitory photonic spiking neuron by extending the Yamada model and spin-flip model to incorporate the two polarization-resolved modes and the saturable absorber. It is shown that, by carefully adjusting the temporal difference according to the inhibitory window, the XOR operation can be realized in a single photonic spiking neuron, which is interesting and valuable for the photonic neuromorphic computing and information processing.
Teleportation Systems Toward a Quantum Internet

Quantum teleportation is essential for many quantum information technologies, including long-distance quantum networks. Using fiber-coupled devices, including state-of-the-art low-noise superconducting nanowire single-photon detectors and off-the-shelf optics, we achieve conditional quantum teleportation of time-bin qubits at the telecommunication wavelength of 1536.5 nm. We measure teleportation fidelities of ≥90% that are consistent with an analytical model of our system, which includes realistic imperfections. To demonstrate the compatibility of our setup with deployed quantum networks, we teleport qubits over 22 km of single-mode fiber while transmitting qubits over an additional 22 km of fiber. Our systems, which are compatible with emerging solid-state quantum devices, provide a realistic foundation for a high-fidelity quantum Internet with practical devices.2 MoreReceived 28 July 2020Accepted 16 October 2020Corrected 22 July 2021DOI:https://doi.org/10.1103/PRXQuantum.1.020317Published 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 channelsQuantum entanglementQuantum information architectures & platformsQuantum networksQuantum teleportationQuantum tomographyString dualitiesQuantum InformationParticles & Fields
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

Understanding broadcast videos is a challenging task in computer vision, as it requires generic reasoning capabilities to appreciate the content offered by the video editing. In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet [24] video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Specifically, we release around 300k annotations within SoccerNet’s 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection, and we define a novel replay grounding task. For each task, we provide and discuss benchmark results, reproducible with our open-source adapted implementations of the most relevant works in the field. SoccerNet-v2 is presented to the broader research community to help push computer vision closer to automatic solutions for more general video understanding and production purposes.
Foreground Gating and Background Refining Network for Surveillance Object Detection

Detecting objects in surveillance videos is an important problem due to its wide applications in traffic control and public security. Existing methods tend to face performance degradation because of false positive or misalignment problems. We propose a novel framework, namely, Foreground Gating and Background Refining Network (FG-BR Net), for surveillance object detection (SOD). To reduce false positives in background regions, which is a critical problem in SOD, we introduce a new module that first subtracts the background of a video sequence and then generates high-quality region proposals. Unlike previous background subtraction methods that may wrongly remove the static foreground objects in a frame, a feedback connection from detection results to background subtraction process is proposed in our model to distill both static and moving objects in surveillance videos. Furthermore, we introduce another module, namely, the background refining stage, to refine the detection results with more accurate localizations. Pairwise non-local operations are adopted to cope with the misalignments between the features of original and background frames. Extensive experiments on real-world traffic surveillance benchmarks demonstrate the competitive performance of the proposed FG-BR Net. In particular, FG-BR Net ranks on the top among all the methods on hard and sunny subsets of the UA-DETRAC detection dataset, without any bells and whistles.
Cubical agda: a dependently typed programming language with univalence and higher inductive types

Proof assistants based on dependent type theory provide expressive languages for both programming and proving within the same system. However, all of the major implementations lack powerful extensionality principles for reasoning about equality, such as function and propositional extensionality. These principles are typically added axiomatically which disrupts the constructive properties of these systems. Cubical type theory provides a solution by giving computational meaning to Homotopy Type Theory and Univalent Foundations, in particular to the univalence axiom and higher inductive types. This paper describes an extension of the dependently typed functional programming language Agda with cubical primitives, making it into a full-blown proof assistant with native support for univalence and a general schema of higher inductive types. These new primitives make function and propositional extensionality as well as quotient types directly definable with computational content. Additionally, thanks also to copatterns, bisimilarity is equivalent to equality for coinductive types. This extends Agda with support for a wide range of extensionality principles, without sacrificing type checking and constructivity.
Self-Attention Guided Copy Mechanism for Abstractive Summarization

Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary.Generally, the encoder-decoder attention is served as the copy distribution, while how to guarantee that important words in the source are copied remains a challenge.In this work, we propose a Transformer-based model to enhance the copy mechanism.Specifically, we identify the importance of each source word based on the degree centrality with a directed graph built by the self-attention layer in the Transformer.We use the centrality of each source word to guide the copy process explicitly.Experimental results show that the self-attention graph provides useful guidance for the copy distribution.Our proposed models significantly outperform the baseline methods on the CNN/Daily Mail dataset and the Gigaword dataset.
Design of interval type-2 fuzzy precompensated PID controller applied to two-DOF robotic manipulator with variable payload

The interval type-2 fuzzy logic controller (IT2-FLC), with footprint of uncertainty (FOU) in membership functions (MF), has increasingly recognized for controlling uncertainties and nonlinearities. Within the ambit of this, the efficient interval type-2 fuzzy precompensated PID (IT2FP-PID) controller is designed for trajectory tracking of 2-DOF robotic manipulator with variable payload. A systematic strategy for optimizing the controller parameters along with scaling factors and the antecedent MF parameters for minimization of performance metric integral time absolute error (ITAE) is presented. Prominently, recently proposed optimization technique hybridizing grey wolf optimizer and artificial bee colony algorithm (GWO–ABC) is utilized for solving this high-dimensional constrained optimization problem. In order to witness effectiveness, the performance is compared with type-1 fuzzy precompensated PID (T1FP-PID), fuzzy PID (FPID), and conventional PID controllers. More significantly, the robustness of IT2FP-PID is examined for payload variation, model uncertainties, external disturbance, and noise cancellation. After experimental outcome, it is inferred that IT2FP-PID controller outperforms others and can be referred as a viable alternative for controlling nonlinear complex systems with higher uncertainties.
Use of Blockchain in Education: A Systematic Literature Review

Blockchain technology enables the formation of a distributed record of a digital event in decentralized manner where data and related transactions are not under the control of any third party. This technology was early used for value transfer but now it has vast range of applications in different fields such as healthcare, banking, internet of things and many others. In the field of education, it also provides numerous opportunities for decentralized management of records in educational institutions in an interoperability manner. The main objective of this research is to highlight the existing issues related to the educational institutes and to find suitable blockchain features that could resolve them. We have adopted a systematic literature review approach for the identification and the extraction of relevant information from the shortlisted studies. This study describes existing issues in three aspects physical, digital and financial. The results of the analysis shows that the manipulation risk, difficulty in verification and exchanging record between institutions are the major issues faced by the educational institutions. This study, then explores blockchain features including decentralization, traceability and consensus mechanism that can be used to address the issues related to the educational institution. Finally, due to unique and underlying technology, it has still some technical challenges and boundaries along with immutability feature, disclosure of personal privacy and scalability issue also discussed in this study.
Quantum Networks with Deterministic Spin–Photon Interfaces

Abstract This report considers how recent experimental progress on deterministic solid‐state spin–photon interfaces enables the construction of a number of key elements of quantum networks. After reviewing some of the recent experimental achievements, a discussion of their integration into Bell state analyzers, quantum non‐demolition detection, and photonic cluster state generation is presented. Finally, it is outlined how these elements can be used for long‐distance entanglement generation and quantum key distribution in a quantum network.
Commit Message Generation for Source Code Changes

Commit messages, which summarize the source code changes in natural language, are essential for program comprehension and software evolution understanding. Unfortunately, due to the lack of direct motivation, commit messages are sometimes neglected by developers, making it necessary to automatically generate such messages. State-of-the-art adopts learning based approaches such as neural machine translation models for the commit message generation problem. However, they tend to ignore the code structure information and suffer from the out-of-vocabulary issue. In this paper, we propose CoDiSum to address the above two limitations. In particular, we first extract both code structure and code semantics from the source code changes, and then jointly model these two sources of information so as to better learn the representations of the code changes. Moreover, we augment the model with copying mechanism to further mitigate the out-of-vocabulary issue. Experimental evaluations on real data demonstrate that the proposed approach significantly outperforms the state-of-the-art in terms of accurately generating the commit messages.
The Effects of a 'Black Swan' Event (COVID-19) on Herding Behavior in Cryptocurrency Markets: Evidence from Cryptocurrency USD, EUR, JPY and KRW Markets

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Adversarial Background-Aware Loss for Weakly-Supervised Temporal Activity Localization

Temporally localizing activities within untrimmed videos has been extensively studied in recent years. Despite recent advances, existing methods for weakly-supervised temporal activity localization struggle to recognize when an activity is not occurring. To address this issue, we propose a novel method named A2CL-PT. Two triplets of the feature space are considered in our approach: one triplet is used to learn discriminative features for each activity class, and the other one is used to distinguish the features where no activity occurs (i.e. background features) from activity-related features for each video. To further improve the performance, we build our network using two parallel branches which operate in an adversarial way: the first branch localizes the most salient activities of a video and the second one finds other supplementary activities from non-localized parts of the video. Extensive experiments performed on THUMOS14 and ActivityNet datasets demonstrate that our proposed method is effective. Specifically, the average mAP of IoU thresholds from 0.1 to 0.9 on the THUMOS14 dataset is significantly improved from 27.9% to 30.0%.
Federated Learning: A signal processing perspective

The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smartphones, vehicles, and sensors, and in some cases cannot be shared due to privacy considerations. Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Learning in a federated manner differs from conventional centralized machine learning and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications. Consequently, dedicated schemes derived from these areas are expected to play an important role in the success of federated learning and the transition of deep learning from the domain of centralized servers to mobile edge devices.
Artificial Neural Networks Hidden Unit and Weight Connection Optimization by Quasi-Refection-Based Learning Artificial Bee Colony Algorithm

Artificial neural networks are one of the most commonly used methods in machine learning. Performance of network highly depends on the learning method. Traditional learning algorithms are prone to be trapped in local optima and have slow convergence. At the other hand, nature-inspired optimization algorithms are proven to be very efficient in complex optimization problems solving due to derivative-free solutions. Addressing issues of traditional learning algorithms, in this study, an enhanced version of artificial bee colony nature-inspired metaheuristics is proposed to optimize connection weights and hidden units of artificial neural networks. Proposed improved method incorporates quasi-reflection-based learning and guided best solution bounded mechanisms in the original approach and manages to conquer its deficiencies. First, the method is tested on a recent challenging CEC 2017 benchmark function set, then applied for training artificial neural network on five well-known medical benchmark datasets. Further, devised algorithm is compared to other metaheuristics-based methods. The efficiency is measured by five metrics - accuracy, specificity, sensitivity, geometric mean, and area under the curve. Simulation results prove that the proposed algorithm outperforms other metaheuristics in terms of accuracy and convergence speed. The improvement of the accuracy over the other methods on different datasets are between 0.03% and 12.94%. The quasi-refection-based learning mechanism significantly improves the convergence speed of the original artificial bee colony algorithm and together with the guided best solution bounded, the exploitation capability is enhanced, which results in significantly better accuracy.
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the number of networks. In this paper, we propose BatchEnsemble, an ensemble method whose computational and memory costs are significantly lower than typical ensembles. BatchEnsemble achieves this by defining each weight matrix to be the Hadamard product of a shared weight among all ensemble members and a rank-one matrix per member. Unlike ensembles, BatchEnsemble is not only parallelizable across devices, where one device trains one member, but also parallelizable within a device, where multiple ensemble members are updated simultaneously for a given mini-batch. Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, and contextual bandits tasks, BatchEnsemble yields competitive accuracy and uncertainties as typical ensembles; the speedup at test time is 3X and memory reduction is 3X at an ensemble of size 4. We also apply BatchEnsemble to lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable performance to progressive neural networks while having a much lower computational and memory costs. We further show that BatchEnsemble can easily scale up to lifelong learning on Split-ImageNet which involves 100 sequential learning tasks.
MedDialog: Large-scale Medical Dialogue Datasets

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets -MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases.To our best knowledge, MedDialog is the largest medical dialogue dataset to date.We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance.It is shown that models trained on MedDialog are able to generate clinically correct and human-like medical dialogues.We also study the transferability of models trained on MedDialog to lowresource medical dialogue generation tasks.It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation.
Consensus One-Step Multi-View Subspace Clustering

Multi-view clustering has attracted increasing attention in multimedia, machine learning and data mining communities. As one kind of the essential multi-view clustering algorithm, multi-view subspace clustering (MVSC) becomes more and more popular due to its strong ability to reveal the intrinsic low dimensional clustering structure hidden across views. Despite superior clustering performance in various applications, we observe that existing MVSC methods <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly fuse multi-view information in the similarity level by merging noisy affinity matrices</i> ; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">isolate the processes of affinity learning, multi-view information fusion and clustering</i> . Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing multiple affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful to eliminate noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels matrix are learned simultaneously in a unified framework. By doing so, the three steps can negotiate with each other to best serve the clustering task, leading to improved performance. Accordingly, we propose an iterative algorithm to solve the resulting optimization problem. Extensive experiment results on benchmark datasets demonstrate the superiority of our method against other state-of-the-art approaches.