Reviewerly/exHarmony huggingface dataset:
A Boundary-aware Neural Model for Nested Named Entity Recognition

In natural language processing, it is common that many entities contain other entities inside them.Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones.We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels.Our model can locate entities precisely by detecting boundaries using sequence labeling models.Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model.We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities.We conduct our experiments on nested NER datasets and the experimental results demonstrate that our model outperforms other state-of-the-art methods.
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks

We present Unicoder, a universal language encoder that is insensitive to different languages.Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages.Comparing to similar efforts such as Multilingual BERT (Devlin et al., 2018) and XLM (Lample and Conneau, 2019), three new crosslingual pre-training tasks are proposed, including cross-lingual word recovery, crosslingual paraphrase classification and crosslingual masked language model.These tasks help Unicoder learn the mappings among different languages from more perspectives.We also find that doing fine-tuning on multiple languages together can bring further improvement.Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline.On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained.On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.
L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language

Hate speech and abusive language have become a common phenomenon on Arabic social media.Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents.The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research.In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents.We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed.This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen's Kappa (k) and Krippendorff's alpha (α) indicated the consistency of the annotations.
Improving bug detection via context-based code representation learning and attention-based neural networks

Bug detection has been shown to be an effective way to help developers in detecting bugs early, thus, saving much effort and time in software development process. Recently, deep learning-based bug detection approaches have gained successes over the traditional machine learning-based approaches, the rule-based program analysis approaches, and mining-based approaches. However, they are still limited in detecting bugs that involve multiple methods and suffer high rate of false positives. In this paper, we propose a combination approach with the use of contexts and attention neural network to overcome those limitations. We propose to use as the global context the Program Dependence Graph (PDG) and Data Flow Graph (DFG) to connect the method under investigation with the other relevant methods that might contribute to the buggy code. The global context is complemented by the local context extracted from the path on the AST built from the method’s body. The use of PDG and DFG enables our model to reduce the false positive rate, while to complement for the potential reduction in recall, we make use of the attention neural network mechanism to put more weights on the buggy paths in the source code. That is, the paths that are similar to the buggy paths will be ranked higher, thus, improving the recall of our model. We have conducted several experiments to evaluate our approach on a very large dataset with +4.973M methods in 92 different project versions. The results show that our tool can have a relative improvement up to 160% on F-score when comparing with the state-of-the-art bug detection approaches. Our tool can detect 48 true bugs in the list of top 100 reported bugs, which is 24 more true bugs when comparing with the baseline approaches. We also reported that our representation is better suitable for bug detection and relatively improves over the other representations up to 206% in accuracy.
Towards Unconstrained End-to-End Text Spotting

We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene text of irregular shape. We formulate arbitrary shape text detection as an instance segmentation problem; an attention model is then used to decode the textual content of each irregularly shaped text region without rectification. To extract useful irregularly shaped text instance features from image scale features, we propose a simple yet effective RoI masking step. Additionally, we show that predictions from an existing multi-step OCR engine can be leveraged as partially labeled training data, which leads to significant improvements in both the detection and recognition accuracy of our model. Our method surpasses the state-of-the-art for end-to-end recognition tasks on the ICDAR15 (straight) benchmark by 4.6%, and on the Total-Text (curved) benchmark by more than 16%.
Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions

Whole slide imaging (WSI), ever since its first introduction about two decades ago, has been validated for a number of applications in the field of pathology. The recent approval of US FDA to a WSI system for use in primary surgical pathology diagnosis has opened avenues for wider acceptance and application of this technology in routine practice. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide opportunities of its newer applications. Its benefits are innumerable such as ease of access through internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Various barriers such as the high cost, technical glitches, and professional hesitation to adopt a new technology have hindered its use in pathology. This review article summarizes the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It highlights the benefits, limitations, and challenges delaying the use of this technology in routine practice. The review is targeted at students, residents, and budding pathologists to better acquaint them with the key aspects of state-of-the-art technology and enable them to implement WSI judiciously.
Fine-grained Interest Matching for Neural News Recommendation

Personalized news recommendation is a critical technology to improve users' online news reading experience.The core of news recommendation is accurate matching between user's interests and candidate news.The same user usually has diverse interests that are reflected in different news she has browsed.Meanwhile, important semantic features of news are implied in text segments of different granularities.Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.In this paper, we propose FIM, a Finegrained Interest Matching method for neural news recommendation.Instead of aggregating user's all historical browsed news into a unified vector, we hierarchically construct multilevel representations for each news via stacked dilated convolutions.Then we perform finegrained matching between segment pairs of each browsed news and the candidate news at each semantic level.High-order salient signals are then identified by resembling the hierarchy of image recognition for final click prediction.Extensive experiments on a real-world dataset from MSN news validate the effectiveness of our model on news recommendation.
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills

Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent.Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them.But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow.In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages.We further propose a new dataset, Blended-SkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes.Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.
Waves, modes, communications, and optics: a tutorial

Modes generally provide an economical description of waves, reducing complicated wave functions to finite numbers of mode amplitudes, as in propagating fiber modes and ideal laser beams.But finding a corresponding mode description for counting the best orthogonal channels for communicating between surfaces or volumes, or for optimally describing the inputs and outputs of a complicated optical system or wave scatterer, requires a different approach.The singular-value decomposition approach we describe here gives the necessary optimal source and receiver "communication modes" pairs and device or scatterer input and output "mode-converter basis function" pairs.These define the best communication or input/output channels, allowing precise counting and straightforward calculations.Here we introduce all the mathematics and physics of this approach, which works for acoustic, radio-frequency and optical waves, including full vector electromagnetic behavior, and is valid from nanophotonic scales to large systems.We show several general behaviors of communications modes, including various heuristic results.We also establish a new "M-gauge" for electromagnetism that clarifies the number of vector wave channels and allows a simple and general quantization.This approach also gives a new modal "M-coefficient" version of Einstein's A&B coefficient argument and revised versions of Kirchhoff's radiation laws.The article is written in a tutorial style to introduce the approach and its consequences.1 SIn V , 2 SIn V , and 3 SIn V that represent the signals (such as three different binary bit streams, for example) that we want to send on the three different "channels" in the communication between the three sources and the three receivers.We would like these signals (e.g., the three binary bit streams) to appear as the three output voltage signals 1 ROut V , 2 ROut V , and 3 ROut V at corresponding electrical outputs at the far end of the system.set of output voltages 1 S V , 2 S V , and 3 SV that drive the sources (e.g., loudspeakers) at
Forecasting chaotic systems with very low connectivity reservoir computers

We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learning the global climate of the forecasted system rather than short-term prediction. We find that optimizing over this measure more quickly excludes reservoirs that fail to reproduce the climate. The results of optimization are surprising: the optimized parameters often specify a reservoir network with very low connectivity. Inspired by this observation, we explore reservoir designs with even simpler structure, and find well-performing reservoirs that have zero spectral radius and no recurrence. These simple reservoirs provide counterexamples to widely used heuristics in the field, and may be useful for hardware implementations of reservoir computers.
A comprehensive survey of sine cosine algorithm: variants and applications

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision. However, the loss function used to train the model may easily be misaligned with the end goal, which is to make the best decisions possible. Hand-tuning the loss function to align with optimization is a difficult and error-prone process (which is often skipped entirely).We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions. Technically, our contribution is a means of integrating common classes of discrete optimization problems into deep learning or other predictive models, which are typically trained via gradient descent. The main idea is to use a continuous relaxation of the discrete problem to propagate gradients through the optimization procedure. We instantiate this framework for two broad classes of combinatorial problems: linear programs and submodular maximization. Experimental results across a variety of domains show that decisionfocused learning often leads to improved optimization performance compared to traditional methods. We find that standard measures of accuracy are not a reliable proxy for a predictive model’s utility in optimization, and our method’s ability to specify the true goal as the model’s training objective yields substantial dividends across a range of decision problems.
From Zero-Shot Learning to Cold-Start Recommendation

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.
Security of the Fiat-Shamir Transformation in the Quantum Random-Oracle Model

The famous Fiat-Shamir transformation turns any public-coin three-round interactive proof, i.e., any so-called $$\Sigma {\text {-protocol}}$$ , into a non-interactive proof in the random-oracle model. We study this transformation in the setting of a quantum adversary that in particular may query the random oracle in quantum superposition. Our main result is a generic reduction that transforms any quantum dishonest prover attacking the Fiat-Shamir transformation in the quantum random-oracle model into a similarly successful quantum dishonest prover attacking the underlying $$\Sigma {\text {-protocol}}$$ (in the standard model). Applied to the standard soundness and proof-of-knowledge definitions, our reduction implies that both these security properties, in both the computational and the statistical variant, are preserved under the Fiat-Shamir transformation even when allowing quantum attacks. Our result improves and completes the partial results that have been known so far, but it also proves wrong certain claims made in the literature. In the context of post-quantum secure signature schemes, our results imply that for any $$\Sigma {\text {-protocol}}$$ that is a proof-of-knowledge against quantum dishonest provers (and that satisfies some additional natural properties), the corresponding Fiat-Shamir signature scheme is secure in the quantum random-oracle model. For example, we can conclude that the non-optimized version of Fish, which is the bare Fiat-Shamir variant of the NIST candidate Picnic, is secure in the quantum random-oracle model.
Near-term quantum-repeater experiments with nitrogen-vacancy centers: Overcoming the limitations of direct transmission

Quantum channels enable the implementation of communication tasks inaccessible to their classical counterparts. The most famous example is the distribution of secret key. However, in the absence of quantum repeaters, the rate at which these tasks can be performed is dictated by the losses in the quantum channel. In practice, channel losses have limited the reach of quantum protocols to short distances. Quantum repeaters have the potential to significantly increase the rates and reach beyond the limits of direct transmission. However, no experimental implementation has overcome the direct transmission threshold. Here, we propose three quantum repeater schemes and assess their ability to generate secret key when implemented on a setup using nitrogen-vacancy (NV) centers in diamond with near-term experimental parameters. We find that one of these schemes---the so-called single-photon scheme, requiring no quantum storage---has the ability to surpass the capacity---the highest secret-key rate achievable with direct transmission---by a factor of 7 for a distance of approximately 9.2 km with near-term parameters, establishing it as a prime candidate for the first experimental realization of a quantum repeater.
A Ten-Step Decision Path to Determine When to Use Blockchain Technologies

Many organizations are looking at blockchain technologies. However, the drawbacks of blockchain databases (e.g., scalability, capacity, latency, privacy) mean that the technology is not always appropriate. This article presents a ten-step decision path that can help determine whether the application of blockchain is justified and, if so, which kind of blockchain technology to use. We describe how this decision path was used to develop a blockchain prototype for the Danish maritime shipping industry.
Learning to Speak and Act in a Fantasy Text Adventure Game

We introduce a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue.In it, agents can perceive, emote, and act whilst conducting dialogue with other agents.Models and humans can both act as characters within the game.We describe the results of training state-of-the-art generative and retrieval models in this setting.We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions.In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue.We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.
Solving Large-Scale Function Optimization Problem by Using a New Metaheuristic Algorithm Based on Quantum Dolphin Swarm Algorithm

Meta-heuristic algorithm has been a research hotspot in solving the optimal solution of large-scale functions.However, meta-heuristic algorithms are prone to fall into local optimum problems, such as the recently proposed dolphin swarm algorithm (DSA).To solve this problem, in this study, the quantum search algorithm is introduced into DSA.In addition, to test the performance of the proposed quantum dolphin swarm algorithm (QDSA), six commonly used large-scale functions (e.g.Rotated hyperellipsoid function) are taken as examples.Furthermore, some advanced algorithms (e.g.whale optimization algorithm (WOA)) are used for comparison.The results show that the ability of QDSA to obtain global optimal solution is obviously improved compared with DSA, and the performance of QDSA is superior to other algorithms considered for comparison.Finally, it can be concluded that such a novel meta-heuristic algorithm may help to improve the problem of solving the optimal solution of large-scale functions.