Reviewerly/exHarmony huggingface dataset:
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Typical fact verification models use retrieved written evidence to verify claims.Evidence sources, however, often change over time as more information is gathered and revised.In order to adapt, models must be sensitive to subtle differences in supporting evidence.We present VITAMINC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes.We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs.Unlike previous resources, the examples in VITAMINC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not.We show that training using this design increases robustness-improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI).Moreover, the structure of VITAMINC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation. 1 BeforeIn animals, spaying involves an invasive removal of the ovaries, but rarely has major complications other than that spayed animals tend to gain weight. AfterIn animals, spaying involves an invasive removal of the ovaries, but rarely has major complications; the superstition that it causes weight gain is not based on fact.Claim 1 Spayed animals gain weight.Claim 2 Weight gain in spayed animals is a superstitious myth.Before As of 16 March, more than 182,000 cases of the disease have been reported in over 160 countries and territories, resulting in around 79,000 recoveries and more than 7,100 deaths.After As of 16 March, more than 182,000 cases of the disease have been reported in over 160 countries and territories, resulting in more than 7,100 deaths and around 79,000 recoveries.Before Global hybrid sales are led by the Prius family, with sales of 4.7 million units representing 66.8% of TMC worldwide sales of 7.053 million Lexus and Toyota units through September 2014.After Global hybrid sales are led by the Prius family, with sales of 5.264 million units representing 65.4% of TMC worldwide sales of 8.048 million Lexus and Toyota units delivered through July 2014.Claim 1 Prius sold less than 5 million units, representing over 65.5% of TMC worldwide sales.Claim 2 Prius sold more than 5 million units, representing less than 65.5% of TMC worldwide sales.
A Systematic Analysis on Blockchain Integration With Healthcare Domain: Scope and Challenges

A secure, smart and peer-to-peer transactions framework can be developed by Blockchain.Blockchain has enormous potential to turn health care systems as a horizontal technology, which has changed many fields of industry.The aim of this article is to critically review 50 papers published between 2015 and 2020 on blockchain-based health systems.36 of these were journal papers; 7 were from conferences, 4 were from various symposiums; 3 were from seminars and 1 chapter was written in the book.Three key questions will be answered in this report.Firstly, what are the emerging trends of blockchain application development in healthcare from a technical perspective?Secondly, in what ways will the systematic analysis presented here contribute to a better understanding of the potential for incorporating blockchain-based technologies into the healthcare domain?Third, what are the important challenges in adopting blockchain as a solution in the healthcare domain?The descriptive analysis contains in this article shows the statistical statistics on the strategies of these 50 papers reveal that many of the blockchain systems proposed are using privately held blockchain and Ethereum platforms.We also address possible emerging trends of blockchain application such as blockchain integration with artificial intelligence, cloud based solutions and parallel block chain architecture.
Recent Trends in Word Sense Disambiguation: A Survey

Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identifying the most suitable meaning from a predefined sense inventory. Recent breakthroughs in representation learning have fueled intensive WSD research, resulting in considerable performance improvements, breaching the 80% glass ceiling set by the inter-annotator agreement. In this survey, we provide an extensive overview of current advances in WSD, describing the state of the art in terms of i) resources for the task, i.e., sense inventories and reference datasets for training and testing, as well as ii) automatic disambiguation approaches, detailing their peculiarities, strengths and weaknesses. Finally, we highlight the current limitations of the task itself, but also point out recent trends that could help expand the scope and applicability of WSD, setting up new promising directions for the future.
Generating Long-Lived Macroscopically Distinct Superposition States in Atomic Ensembles

We propose to create and stabilize long-lived macroscopic quantum superposition states in atomic ensembles. We show that using a fully quantum parametric amplifier can cause the simultaneous decay of two atoms and, in turn, create stabilized atomic Schr\"odinger cat states. Remarkably, even with modest parameters these intracavity atomic cat states can have an extremely long lifetime, up to 4 orders of magnitude longer than that of intracavity photonic cat states under the same parameter conditions, reaching tens of milliseconds. This lifetime of atomic cat states is ultimately limited to several seconds by extremely weak spin relaxation and thermal noise. Our work opens up a new way toward the long-standing goal of generating large-size and long-lived cat states, with immediate interests both in fundamental studies and noise-immune quantum technologies.
REBEL: Relation Extraction By End-to-end Language generation

Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks.However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types.To overcome these issues, we propose the use of autoregressive seq2seq models.Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks.In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types.We show our model's flexibility by finetuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions

Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to deal with just removing one type of degradation. Recently, a CNN-based method using neural architecture search (All-in-One) was proposed to remove all the weather conditions at once. However, it has a large number of parameters as it uses multiple encoders to cater to each weather removal task and still has scope for improvement in its performance. In this work, we focus on developing an efficient solution for the all adverse weather removal problem. To this end, we propose TransWeather, a transformer-based end-to-end model with just a single encoder and a decoder that can restore an image degraded by any weather condition. Specifically, we utilize a novel transformer encoder using intra-patch transformer blocks to enhance attention inside the patches to effectively remove smaller weather degradations. We also introduce a transformer decoder with learnable weather type embeddings to adjust to the weather degradation at hand. Trans Weather achieves significant improvements across multiple test datasets over both All-in-One network as well as methods fine-tuned for specific tasks. TransWeather is also validated on real world test images and found to be more effective than previous methods. Implementation code can be found in the supplementary document. Code is available at https//github.com/jeya-maria-jose/TransWeather.
AOAAO: The Hybrid Algorithm of Arithmetic Optimization Algorithm With Aquila Optimizer

Many new algorithms have been proposed to solve the mathematical equations formulated to describe the real-world problems. But there still does not exist one algorithm that could solve the problems all. And most of the proposed algorithms have defects in some aspects, they need to be improved in application. In order to find a more efficient optimization algorithm and inspired by the better performance of the Arithmetic Optimization algorithm (AOA) and Aquila Optimizer (AO), we proposed a hybridization algorithm of them and abbreviated AOAAO in this paper. Considering the better performance of the Harris Hawk optimization (HHO) algorithm, an energy parameter E was also introduced to balance the exploration and exploitation procedures of individuals in AOAAO swarms, and furthermore, piecewise linear map was introduced to decrease the randomness of the energy parameter. Pseudo code of the proposed AOAAO algorithm was presented, Simulation experiments were carried out on the benchmark functions and three classical engineering problems were also involved in optimization. Nine popular well demonstrated algorithms were included for comparison. Results confirmed the AOAAO would be more efficient in optimization with faster convergence rate, and higher convergence accuracy.
A systematic review of ontology use in E-Learning recommender system

Ontology and knowledge-based systems typically provide e-learning recommender systems. However, ontology use in such systems is not well studied in systematic detail. Therefore, this research examines the development and evaluation of ontology-based recommender systems. The study also discusses technical ontology use and the recommendation process. We identified multidisciplinary ontology-based recommender systems in 28 journal articles. These systems combined ontology with artificial intelligence, computing technology, education, education psychology, and social sciences. Student models and learning objects remain the primary ontology use, followed by feedback, assessments, and context data. Currently, the most popular recommendation item is the learning object, but learning path, feedback, and learning device could be the future considerations. This recommendation process is reciprocal and can be initiated either by the system or students. Standard ontology languages are commonly used, but standards for student profiles and learning object metadata are rarely adopted. Moreover, ontology-based recommender systems seldom use the methodology of building ontologies and hardly use other ontology methodologies. Similarly, none of the primary studies described ontology evaluation methodologies, but the systems are evaluated by nonreal students, algorithmic performance tests, statistics, questionnaires, and qualitative observations. In conclusion, the findings support the implementation of ontology methodologies and the integration of ontology-based recommendations into existing learning technologies. The study also promotes the use of recommender systems in social science and humanities courses, non-higher education, and open learning environments.
Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays

Executing quantum algorithms on error-corrected logical qubits is a critical step for scalable quantum computing, but the requisite numbers of qubits and physical error rates are demanding for current experimental hardware. Recently, the development of error correcting codes tailored to particular physical noise models has helped relax these requirements. In this work, we propose a qubit encoding and gate protocol for ${}^{171}$Yb neutral atom qubits that converts the dominant physical errors into erasures, that is, errors in known locations. The key idea is to encode qubits in a metastable electronic level, such that gate errors predominantly result in transitions to disjoint subspaces whose populations can be continuously monitored via fluorescence. We estimate that 98% of errors can be converted into erasures. We quantify the benefit of this approach via circuit-level simulations of the surface code, finding a threshold increase from 0.937% to 4.15%. We also observe a larger code distance near the threshold, leading to a faster decrease in the logical error rate for the same number of physical qubits, which is important for near-term implementations. Erasure conversion should benefit any error correcting code, and may also be applied to design new gates and encodings in other qubit platforms.
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning

Learning sentence embeddings often requires a large amount of labeled data.However, for most tasks and domains, labeled data is seldom available and creating it is expensive.In this work, we present a new state-of-theart unsupervised method based on pre-trained Transformers and Sequential Denoising Auto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points.It can achieve up to 93.1% of the performance of indomain supervised approaches.Further, we show that TSDAE is a strong domain adaptation and pre-training method for sentence embeddings, significantly outperforming other approaches like Masked Language Model. 1 A crucial shortcoming of previous studies is the narrow evaluation: Most work mainly evaluates on the single task of Semantic Textual Similarity (STS), which does not require any domain knowledge.It is unclear if these proposed methods generalize to other domains and tasks.We fill this gap and evaluate TS-DAE and other recent approaches on four different datasets from heterogeneous domains.
Design of Symmetric-Key Primitives for Advanced Cryptographic Protocols

While traditional symmetric algorithms like AES and SHA-3 are optimized for efficient hardware and software implementations, a range of emerging applications using advanced cryptographic protocols such as multi-party computation and zero knowledge proofs require optimization with respect to a different metric: arithmetic complexity.In this paper we study the design of secure cryptographic algorithms optimized to minimize this metric. We begin by identifying the differences in the design space between such arithmetization-oriented ciphers and traditional ones, with particular emphasis on the available tools, efficiency metrics, and relevant cryptanalysis. This discussion highlights a crucial point—the considerations for designing arithmetization-oriented ciphers are oftentimes different from the considerations arising in the design of software- and hardware-oriented ciphers.The natural next step is to identify sound principles to securely navigate this new terrain, and to materialize these principles into concrete designs. To this end, we present the Marvellous design strategy which provides a generic way to easily instantiate secure and efficient algorithms for this emerging domain. We then show two examples for families following this approach. These families — Vision and Rescue — are benchmarked with respect to three use cases: the ZK-STARK proof system, proof systems based on Rank-One Constraint Satisfaction (R1CS), and Multi-Party Computation (MPC). These benchmarks show that our algorithms achieve a highly compact algebraic description, and thus benefit the advanced cryptographic protocols that employ them.
Memory-Efficient Abstractions for Pathfinding

From an academic perspective there has been a lot of work on using state abstraction to speed path planning. But, this work often does not directly address the needs of the game development community, specifically for mechanisms that will fit the limited memory budget of most commercial games. In this paper we bring together several related pieces of work on using abstraction for pathfinding, showing how the ideas can be implemented using a minimal amount of memory. Our techniques use about 3% additional storage to compute complete paths up to 100 times faster than A*.
Adversarial quantum circuit learning for pure state approximation

Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: one tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.
Image-text dual neural network with decision strategy for small-sample image classification

Small-sample classification is a challenging problem in computer vision. In this work, we show how to efficiently and effectively utilize semantic information of the annotations to improve the performance of small-sample classification. First, we propose an image-text dual neural network to improve the classification performance on small-sample datasets. The proposed model consists of two sub-models, an image classification model and a text classification model. After training the sub-models separately, we design a novel method to fuse the two sub-models rather than simply combine their results. Our image-text dual neural network aims to utilize the text information to overcome the training problem of deep models on small-sample datasets. Then, we propose to incorporate a decision strategy into the image-text dual neural network to further improve the performance of our original model on few-shot datasets. To demonstrate the effectiveness of the proposed models, we conduct experiments on the LabelMe and UIUC-Sports datasets. Experimental results show that our method is superior to other models.
Using High-Bandwidth Networks Efficiently for Fast Graph Computation

Nowadays, high-bandwidth networks are more easily accessible than ever before. However, existing distributed graph-processing frameworks, such as GPS, fail to efficiently utilize the additional bandwidth capacity in these networks for higher performance, due to their inefficient computation and communication models, leading to very long waiting times experienced by users for the graph-computing results. The root cause lies in the fact that the computation and communication models of these frameworks generate, send and receive messages so slowly that only a small fraction of the available network bandwidth is utilized. In this paper, we propose a high-performance distributed graph-processing framework, called BlitzG, to address this problem. This framework fully exploits the available network bandwidth capacity for fast graph processing. Our approach aims at significant reduction in (i) the computation workload of each vertex for fast message generation by using a new slimmed-down vertex-centric computation model and (ii) the average message overhead for fast message delivery by designing a light-weight message-centric communication model. Evaluation on a 40Gbps Ethernet, driven by real-world graph datasets, shows that BlitzG outperforms GPS by up to 27x with an average of 20.7x.
Adversarial unseen visual feature synthesis for Zero-shot Learning

Due to the extreme imbalance of training data between seen classes and unseen classes, most existing methods fail to achieve satisfactory results in the challenging task of Zero-shot Learning (ZSL). To avoid the need for labelled data of unseen classes, in this paper, we investigate how to synthesize visual features for ZSL problem. The key challenge is how to capture the realistic feature distribution of unseen classes without training samples. To this end, we propose a hybrid model consists of Random Attribute Selection (RAS) and conditional Generative Adversarial Network (cGAN). RAS aims to learn the realistic generation of attributes by their correlations in nature. To improve the discrimination for the large number of classes, we add a reconstruction loss in the generative network, which can solve the domain shift problem and significantly improve the classification accuracy. Extensive experiments on four benchmarks demonstrate that our method can outperform all the state-of-the-art methods. Qualitative results show that, compared to conventional generative models, our method can capture more realistic distribution and remarkably improve the variability of the synthesized data.
Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.