Reviewerly/exHarmony huggingface dataset:
Accuracy of gross intraoperative margin assessment for breast cancer: experience since the SSO-ASTRO margin consensus guidelines

Abstract Gross intraoperative assessment can be used to ensure negative margins at the time of surgery. Previous studies of this technique were conducted before the introduction of consensus guidelines defining a “positive” margin. We performed a retrospective study examining the accuracy of this technique since these guidelines were published. We identified all specimens that were grossly examined at the time of breast conserving surgery from January 2014 to July 2020. Gross and final microscopic diagnoses were compared and the performance of intraoperative examination was assessed in terms of false positive and false negative rates. Logistic regression models were used to examine the effect of clinicopathologic covariates on discordance. 327 cases were reviewed. Gross exam prompted re-excision in 166 cases (61%). The rate of false negative discordance was 8.6%. In multivariate analysis, multifocality on final pathology was associated with discordance. We consider the false negative rate acceptable for routine clinical use; however, there is an ongoing need for more accurate methods for the intraoperative assessment of margins.
An Evaluation Testbed for Locomotion in Virtual Reality

A common operation performed in Virtual Reality (VR) environments is locomotion. Although real walking can represent a natural and intuitive way to manage displacements in such environments, its use is generally limited by the size of the area tracked by the VR system (typically, the size of a room) or requires expensive technologies to cover particularly extended settings. A number of approaches have been proposed to enable effective explorations in VR, each characterized by different hardware requirements and costs, and capable to provide different levels of usability and performance. However, the lack of a well-defined methodology for assessing and comparing available approaches makes it difficult to identify, among the various alternatives, the best solutions for selected application domains. To deal with this issue, this paper introduces a novel evaluation testbed which, by building on the outcomes of many separate works reported in the literature, aims to support a comprehensive analysis of the considered design space. An experimental protocol for collecting objective and subjective measures is proposed, together with a scoring system able to rank locomotion approaches based on a weighted set of requirements. Testbed usage is illustrated in a use case requesting to select the technique to adopt in a given application scenario.
Serialized Output Training for End-to-End Overlapped Speech Recognition

This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instead of having multiple output layers as with the permutation invariant training (PIT), SOT uses a model with only one output layer that generates the transcriptions of multiple speakers one after another. The attention and decoder modules take care of producing multiple transcriptions from overlapped speech. SOT has two advantages over PIT: (1) no limitation in the maximum number of speakers, and (2) an ability to model the dependencies among outputs for different speakers. We also propose a simple trick that allows SOT to be executed in O(S), where S is the number of the speakers in the training sample, by using the start times of the constituent source utterances. Experimental results on LibriSpeech corpus show that the SOT models can transcribe overlapped speech with variable numbers of speakers significantly better than PIT-based models. We also show that the SOT models can accurately count the number of speakers in the input audio.
Development of trust based access control models using fuzzy logic in cloud computing

Cloud computing is the technology that provides different types of services as a useful resource on the Internet. Resource trust value will help the cloud users to select the services of a cloud provider for processing and storing their essential information. Also, service providers can give access to users based on trust value to secure cloud resources from malicious users. In this paper, trust models are proposed, which comes under the subjective trust model based on the behavior of user and service provider to calculate the trust values. The trust is fuzzy, which motivated us to apply fuzzy logic for calculating the trust values of the cloud users and service providers in the cloud environment. We use a Mamdani fuzzy method with gauss membership function for fuzzification and triangular membership function for defuzzification. Parameters such as performance and elasticity are taken for trust evaluation of the resource. The attributes for calculating performance are workload and response time. And for calculating elasticity, we have taken scalability, availability, security, and usability. The fuzzy C-means clustering is applied to parameters for evaluating the trust value of users such as bad requests, bogus requests, unauthorized requests, and total requests.
A Simple Yet Strong Pipeline for HotpotQA

State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition.However, does their strong performance on popular multihop datasets really justify this added design complexity?Our results suggest that the answer may be no, because even our simple pipeline based on BERT, named QUARK, performs surprisingly well.Specifically, on Hot-potQA, QUARK outperforms these models on both question answering and support identification (and achieves performance very close to a RoBERTa model).Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences independently of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences.The strong performance of QUARK resurfaces the importance of carefully exploring simple model designs before using popular benchmarks to justify the value of complex techniques.
DeSMOG: Detecting Stance in Media On Global Warming

Citing opinions is a powerful yet understudied strategy in argumentation.For example, an environmental activist might say, "Leading scientists agree that global warming is a serious concern," framing a clause which affirms their own stance (that global warming is serious) as an opinion endorsed ([scientists] agree) by a reputable source (leading).In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: "Mistaken scientists claim [...]."Our work studies opinion-framing in the global warming (GW) debate, 1 an increasingly partisan issue that has received little attention in NLP.We introduce Global Warming Stance Dataset (GWSD), a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other's opinions.From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GWskeptical media shows more opponent-doubt.We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author's own view to source entities known to publicly endorse the opposing view.We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.
A dynamical system induced by quantum walk

We consider the Grover walk model on a connected finite graph with two infinite length tails and we set an $\ell^\infty$-infinite external source from one of the tails as the initial state. We show that for any connected internal graph, a stationary state exists, moreover a perfect transmission to the opposite tail always occurs in the long time limit. We also show that the lower bound of the norm of the stationary measure restricted to the internal graph is proportion to the number of edges of this graph. Furthermore when we add more tails (e.g., $r$-tails) to the internal graph, then we find that from the temporal and spatial global view point, the scattering to each tail in the long time limit coincides with the local one-step scattering manner of the Grover walk at a vertex whose degree is $(r+1)$.
Visually Grounded Compound PCFGs

Exploiting visual groundings for language understanding has recently been drawing much attention.In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings.Existing work on this task (Shi et al., 2019) optimizes a parser via REINFORCE and derives the learning signal only from the alignment of images and sentences.While their model is relatively accurate overall, its error distribution is very uneven, with low performance on certain constituents types (e.g., 26.2% recall on verb phrases, VPs) and high on others (e.g., 79.6% recall on noun phrases, NPs).This is not surprising as the learning signal is likely insufficient for deriving all aspects of phrasestructure syntax and gradient estimates are noisy.We show that using an extension of probabilistic context-free grammar model we can do fully-differentiable end-to-end visually grounded learning.Additionally, this enables us to complement the image-text alignment loss with a language modeling objective.On the MSCOCO test captions, our model establishes a new state of the art, outperforming its non-grounded version and, thus, confirming the effectiveness of visual groundings in constituency grammar induction.It also substantially outperforms the previous grounded model, with largest improvements on more 'abstract' categories (e.g., +55.1% recall on VPs). 1
A secure data deduplication system for integrated cloud-edge networks

Abstract Data redundancy is a significant issue that wastes plenty of storage space in the cloud-fog storage integrated environments. Most of the current techniques, which mainly center around the static scenes, for example, the backup and archive systems, are not appropriate because of the dynamic nature of data in the cloud or integrated cloud environments. This problem can be effectively reduced and successfully managed by data deduplication techniques, eliminating duplicate data in cloud storage systems. Implementation of data deduplication (DD) over encrypted data is always a significant challenge in an integrated cloud-fog storage and computing environment to optimize the storage efficiently in a highly secured manner. This paper develops a new method using Convergent and Modified Elliptic Curve Cryptography (MECC) algorithms over the cloud and fog environment to construct secure deduplication systems. The proposed method focuses on the two most important goals of such systems. On one side, the redundancy of data needs to be reduced to its minimum, and on the other hand, a robust encryption approach must be developed to ensure the security of the data. The proposed technique is well suited for operations such as uploading new files by a user to the fog or cloud storage. The file is first encrypted using the Convergent Encryption (CE) technique and then re-encrypted using the Modified Elliptic Curve Cryptography (MECC) algorithm. The proposed method can recognize data redundancy at the block level, reducing the redundancy of data more effectively. Testing results show that the proposed approach can outperform a few state-of-the-art methods of computational efficiency and security levels.
Analysis of atmospheric effects on satellite-based quantum communication: a comparative study

Quantum key distribution (QKD) is a key exchange protocol which is implemented over free space optical links or optical fiber cable. When direct communication is not possible, QKD is performed over fiber cables, but the imperfections in detectors used at the receiver side and also the material properties of fiber cables limit the long-distance communication. Free space-based QKD is free from such limitations and can pave the way for satellite-based quantum communication to set up a global network for sharing secret messages. To implement free space optical links, it is essential to study the effect of atmospheric turbulence. Here, an analysis is made for satellite-based quantum communication using QKD protocols. We assume two specific attacks, namely PNS (photon number splitting) and IRUD (intercept-resend with unambiguous discrimination), which could be main threats for future QKD-based satellite applications. The key generation rates and the error rates of the considered QKD protocols are presented. Other parameters such as optimum signal and decoy states mean photon numbers are calculated for each protocol and distance. Further, in SARG04 QKD protocol with two decoy states, the optimum signal-state mean photon number is independent of the link distance and is valid for the attacks considered here. This is significant, highlighting its use in a realistic scenario of satellite quantum communication.
How does BERT capture semantics? A closer look at polysemous words

The recent paradigm shift to contextual word embeddings has seen tremendous success across a wide range of down-stream tasks.However, little is known on how the emergent relation of context and semantics manifests geometrically.We investigate polysemous words as one particularly prominent instance of semantic organization.Our rigorous quantitative analysis of linear separability and cluster organization in embedding vectors produced by BERT shows that semantics do not surface as isolated clusters but form seamless structures, tightly coupled with sentiment and syntax.
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments.However, few works study both of them simultaneously.In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the contents, the structure and the connections between them.We prepare a challenging dataset that contains 4,764 fully annotated review-rebuttal passage pairs from an open review platform to facilitate the study of this task.To automatically detect argumentative propositions and extract argument pairs from this corpus, we cast it as the combination of a sequence labeling task and a text relation classification task.Thus, we propose a multitask learning framework based on hierarchical LSTM networks.Extensive experiments and analysis demonstrate the effectiveness of our multi-task framework, and also show the challenges of the new task as well as motivate future research directions.
FACLSTM: ConvLSTM with focused attention for scene text recognition

Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. Due to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs, and propose a convolution LSTM (ConvLSTM)-based scene text recognizer, namely, FACLSTM, i.e., Focused Attention ConvLSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular, low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text with large margins.
TD-regularized actor-critic methods

Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability. This is partly due to the interaction between the actor and critic during learning, e.g., an inaccurate step taken by one of them might adversely affect the other and destabilize the learning. To avoid such issues, we propose to regularize the learning objective of the actor by penalizing the temporal difference (TD) error of the critic. This improves stability by avoiding large steps in the actor update whenever the critic is highly inaccurate. The resulting method, which we call the TD-regularized actor-critic method, is a simple plug-and-play approach to improve stability and overall performance of the actor-critic methods. Evaluations on standard benchmarks confirm this. Source code can be found at https://github.com/sparisi/td-reg .
Mixed-integer programming in motion planning

This paper presents a review of past and present results and approaches in the area of motion planning using MIP (Mixed-integer Programming). Although in the early 2000s MIP was still seen with reluctance as method for solving motion planning-related problems, nowadays, due to increases in computational power and theoretical advances, its extensive modeling capabilities and versatility are coming to the fore and enjoy increased application and appreciation. This class of control problems involves, essentially, either a selection from a limited number of alternatives or a constrained optimization problem over a non-convex domain. In both situations, MIP has proven to be an efficient modeling technique as it will be shown in the present review paper. Furthermore, an emphasis is laid on the existing alternatives for implementation and on various experimental validations documented in the literature.
Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.
Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.
The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.
A Symmetric Local Search Network for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document.To tackle this task, a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses individually, then paired the emotion and cause clauses, and filtered out the pairs without causality.Different from this method that separated the detection and the matching of emotion and cause into two steps, we propose a Symmetric Local Search Network (SLSN) model to perform the detection and matching simultaneously by local search.SLSN consists of two symmetric subnetworks, namely the emotion subnetwork and the cause subnetwork.Each subnetwork is composed of a clause representation learner and a local pair searcher.The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs.Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods.
SardiStance @ EVALITA2020: Overview of the Task on Stance Detection in Italian Tweets

SardiStance is the first shared task for Italian on the automatic classification of stance in tweets. It is articulated in two different settings: A) Textual Stance Detection, exploiting only the information provided by the tweet, and B) Contextual Stance Detection, with the addition of information on the tweet itself such as the number of retweets, the number of favours or the date of posting; contextual information about the author, such as follower count, location, user's biography; and additional knowledge extracted from the user's network of friends, followers, retweets, quotes and replies. The task has been one of the most participated at EVALITA 2020 (Basile et al. 2020), with a total of 22 submitted runs for Task A, and 13 for Task B, and 12 different participating teams from both academia and industry.