Reviewerly/exHarmony huggingface dataset:
Character Randomized Benchmarking for Non-Multiplicity-Free Groups With Applications to Subspace, Leakage, and Matchgate Randomized Benchmarking

Randomized benchmarking (RB) is a powerful method for determining the error rate of experimental quantum gates. Traditional RB, however, is restricted to gatesets, such as the Clifford group, that form a unitary 2-design. The recently introduced character RB can benchmark more general gates using techniques from representation theory; up to now, however, this method has only been applied to “multiplicity-free” groups, a mathematical restriction on these groups. In this paper, we extend the original character RB derivation to explicitly treat non-multiplicity-free groups, and derive several applications. First, we derive a rigorous version of the recently introduced subspace RB, which seeks to characterize a set of one- and two-qubit gates that are symmetric under swap. Second, we develop a new leakage RB protocol that applies to more general groups of gates. Finally, we derive a scalable RB protocol for the matchgate group, a group that like the Clifford group is nonuniversal but becomes universal with the addition of one additional gate. This example provides one of the few examples of a scalable non-Clifford RB protocol. In all three cases, compared to existing theories, our method requires similar resources, but either provides a more accurate estimate of gate fidelity, or applies to a more general group of gates. In conclusion, we discuss the potential, and challenges, of using non-multiplicity-free character RB to develop new classes of scalable RB protocols and methods of characterizing specific gates.1 MoreReceived 10 November 2020Revised 1 February 2021Accepted 8 March 2021DOI:https://doi.org/10.1103/PRXQuantum.2.010351Published 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 benchmarkingQuantum gatesQuantum information with trapped ionsQuantum Information
A Systematic Review of the Security in Cloud Computing: Data Integrity, Confidentiality and Availability

The cloud computing plays the prominent role in many organizations and researchers were focus on securing the cloud computing. The privacy preserving is the major challenge that grows exponentially with increases in user. In this paper, the depth survey is conducted on the recent methodologies of the cloud storage security related with the cloud computing. The overview of the cloud computing and security issues is analyzed in this paper. The key security requirements such as data integrity, availability and confidentiality. Security issues in the recent methodologies of cloud security is analyzed. The challenges in the cloud security is analyzed and possible future scope of the method is discussed. The paper involves in analyzing the state-of-art method to investigate the advantages and limitations.
Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !

The multi-head self-attention of popular transformer models is widely used within Natural Language Processing (NLP), including for the task of extractive summarization.With the goal of analyzing and pruning the parameterheavy self-attention mechanism, there are multiple approaches proposing more parameterlight self-attention alternatives.In this paper, we present a novel parameter-lean self-attention mechanism using discourse priors.Our new tree self-attention is based on document-level discourse information, extending the recently proposed "Synthesizer" framework with another lightweight alternative.We show empirical results that our tree self-attention approach achieves competitive ROUGE-scores on the task of extractive summarization.When compared to the original single-head transformer model, the tree attention approach reaches similar performance on both, EDU and sentence level, despite the significant reduction of parameters in the attention component.We further significantly outperform the 8-head transformer model on sentence level when applying a more balanced hyper-parameter setting, requiring an order of magnitude less parameters 1 .
Broadband lumped-element Josephson parametric amplifier with single-step lithography

We present a lumped-element Josephson parametric amplifier (JPA) fabricated using a straightforward e-beam lithography process. Our strongly coupled flux-pumped JPA achieves a gain of 20 dB with a bandwidth of 95 MHz around 5 GHz, while the center frequency is tunable by more than 1 GHz, with the additional possibility for rapid tuning by varying the pump frequency alone. Analytical calculations based on the input-output theory reproduce our measurement results closely.
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation

We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-explored, with most prior work relying on supervision from, e.g., 3D ground-truth, multiple images of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach to solve this problem with four component: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2.5D surfel-based reconstruction of a scene—from the latent code—(iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution. Pix2Shape can generate complex 3D scenes that scale with the view-dependent on-screen resolution, unlike representations that capture world-space resolution, i.e., voxels or meshes. We show that Pix2Shape learns a consistent scene representation in its encoded latent space, and that the decoder can then be applied to this latent representation in order to synthesize the scene from a novel viewpoint. We evaluate Pix2Shape with experiments on the ShapeNet dataset as well as on a novel benchmark we developed – called 3D-IQTT—to evaluate models based on their ability to enable 3d spatial reasoning. Qualitative and quantitative evaluation demonstrate Pix2Shape’s ability to solve scene reconstruction, generation and understanding tasks.
Causal network discovery by iterative conditioning: Comparison of algorithms

Estimating causal interactions in complex dynamical systems is an important problem encountered in many fields of current science. While a theoretical solution for detecting the causal interactions has been previously formulated in the framework of prediction improvement, it generally requires the computation of high-dimensional information functionals-a situation invoking the curse of dimensionality with increasing network size. Recently, several methods have been proposed to alleviate this problem, based on iterative procedures for the assessment of conditional (in)dependences. In the current work, we bring a comparison of several such prominent approaches. This is done both by theoretical comparison of the algorithms using a formulation in a common framework and by numerical simulations including realistic complex coupling patterns. The theoretical analysis highlights the key similarities and differences between the algorithms, hinting on their comparative strengths and weaknesses. The method assumptions and specific properties such as false positive control and order-dependence are discussed. Numerical simulations suggest that while the accuracy of most of the algorithms is almost indistinguishable, there are substantial differences in their computational demands, ranging theoretically from polynomial to exponential complexity and leading to substantial differences in computation time in realistic scenarios depending on the density and size of networks. Based on the analysis of the algorithms and numerical simulations, we propose a hybrid approach providing competitive accuracy with improved computational efficiency.
Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Background Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furthermore, there has been a lack of introductory information explaining attention mechanisms to clinical researchers. Objective The aim of this study was to introduce the basic concepts and design approaches of attention mechanisms. In addition, we aimed to empirically assess the potential limitations of current attention mechanisms in terms of prediction and interpretability performance. Methods First, the basic concepts and several key considerations regarding attention mechanisms were identified. Second, four approaches to attention mechanisms were suggested according to a two-dimensional framework based on the degrees of freedom and uncertainty awareness. Third, the prediction performance, probability reliability, concentration of variable importance, consistency of attention results, and generalizability of attention results to conventional statistics were assessed in the diabetic classification modeling setting. Fourth, the potential limitations of attention mechanisms were considered. Results Prediction performance was very high for all models. Probability reliability was high in models with uncertainty awareness. Variable importance was concentrated in several variables when uncertainty awareness was not considered. The consistency of attention results was high when uncertainty awareness was considered. The generalizability of attention results to conventional statistics was poor regardless of the modeling approach. Conclusions The attention mechanism is an attractive technique with potential to be very promising in the future. However, it may not yet be desirable to rely on this method to assess variable importance in clinical settings. Therefore, along with theoretical studies enhancing attention mechanisms, more empirical studies investigating potential limitations should be encouraged.
On the Frailty of Universal POS Tags for Neural UD Parsers

We present an analysis on the effect UPOS accuracy has on parsing performance.Results suggest that leveraging UPOS tags as features for neural parsers requires a prohibitively high tagging accuracy and that the use of gold tags offers a non-linear increase in performance, suggesting some sort of exceptionality.We also investigate what aspects of predicted UPOS tags impact parsing accuracy the most, highlighting some potentially meaningful linguistic facets of the problem.
Coreference for Discourse Parsing: A Neural Approach

We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver.In particular, starting with a strong baseline neural parser unaware of any coreference information, we compare a parser which utilizes only the output of a neural coreference resolver, with a more sophisticated model, where discourse parsing and coreference resolution are jointly learned in a neural multitask fashion.Results indicate that these initial attempts to incorporate coreference information do not boost the performance of discourse parsing in a statistically significant way.
Multimode Time-Delay Interferometer for Free-Space Quantum Communication

Optical communication via free-space channels is attractive for establishing long-range secure quantum networks, but is often hindered by deleterious effects on the transverse spatial mode profile of the beam, caused by propagation through the atmosphere. Additionally, interferometric measurement at the receiver becomes much more difficult with multimode profiles. This study presents a compact time-delay interferometer with high stability and interference visibility for single- and multimode spatial profiles. The results of this study are important for the future of quantum communication networks as well as a wide range of techniques for classical and quantum optical measurement.
The Cycle of Academic Reading Fear Among Undergraduates

This study investigates students' fear in academic reading as well as the influence of perceived difficulties in their reading comprehension. The paper aims to study the level of anxiety as experienced by undergaduate students in academic reading. Data from 25 respondents were analyzed quantitatively using Reading Anxiety Scale (FLRAS) developed by Saito, Horwitz, and Garza (1999). Te objectives of the study are to examine the influence of students' fear and perceived difficulties in academic reading. The findings of this study reveal that factors such as background and culture, general reading ability, vocabulary, grammar as well as teaching method can make readers fear reading. The results of this study bear interesting implications towards the teaching and learning of academic reading in English as a Second Language.
Augmented Reality in Sport Marketing

The augmented reality (AR) market is expected to grow to $60.55 billion by 2023 and there are currently an estimated 4.2 billion AR-capable devices in the hands of consumers. Experts believe that sport is a high-value market for AR as it is uniquely positioned to utilize AR in multiple marketing contexts (Haber, 2019). The COVID-19 pandemic has accelerated the rate at which sport organizations are utilizing AR as they attempt to reach fans and consumers that are limited or restricted from attending events in person. This article introduces augmented reality to sport managers and practitioners and defines AR, detailing how it can be used by citing examples of how it has been used in sport marketing to this point. Specifically, the paper discusses the three most common classifications of AR for marketing and their role in sport: advertising/promotion, product management, and customer service. The types of AR delivery systems (e.g., HMD, projector-based, smartphone, broadcast AR) are also discussed to clarify that AR is a grouping of technologies and not just one hardware platform. Sport and non-sport examples of AR implementations of the different classifications and delivery systems are provided in this text. Three recommendations are provided for AR development and implementation, namely that sport AR activations should be a complement to the sport product, focus on visual appeal, and strive for immersion. These recommendations are grounded in academic research and intended to assist practitioners planning to implement AR as part of their marketing strategy.
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues

Incorporating commonsense knowledge can alleviate the issue of generating generic responses in open-domain generative dialogue systems.However, selecting knowledge facts for the dialogue context is still a challenge.The widely used approach Entity Name Matching always retrieves irrelevant facts from the view of local entity words.This paper proposes a novel knowledge selection approach, Prototype-KR, and a knowledge-aware generative model, Prototype-KRG.Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query.We find knowledge facts used in prototype dialogues usually are highly relevant to the current query; thus, Prototype-KR ranks such knowledge facts based on the semantic similarity and then selects the most appropriate facts.Subsequently, Prototype-KRG can generate an informative response using the selected knowledge facts.Experiments demonstrate that our approach has achieved notable improvements on the most metrics, compared to generative baselines.Meanwhile, compared to IR(Retrieval)-based baselines, responses generated by our approach are more relevant to the context and have comparable informativeness.
Reactive Supervision: A New Method for Collecting Sarcasm Data

Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data.We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques.We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features.The dataset is expected to advance sarcasm detection research.Our method can be adapted to other affective computing domains, thus opening up new research opportunities.
A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts

Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.
Pretrained Language Model Embryology: The Birth of ALBERT

While behaviors of pretrained language models (LMs) have been thoroughly examined, what happened during pretraining is rarely studied.We thus investigate the developmental process from a set of randomly initialized parameters to a totipotent 1 language model, which we refer to as the embryology of a pretrained language model.Our results show that ALBERT learns to reconstruct and predict tokens of different parts of speech (POS) in different learning speeds during pretraining.We also find that linguistic knowledge and world knowledge do not generally improve as pretraining proceeds, nor do downstream tasks' performance.These findings suggest that knowledge of a pretrained model varies during pretraining, and having more pretrain steps does not necessarily provide a model with more comprehensive knowledge.We provide source codes and pretrained models to reproduce our results at https://github.
Investigating Sampling Bias in Abusive Language Detection

Abusive language detection is becoming increasingly important, but we still understand little about the biases in our datasets for abusive language detection, and how these biases affect the quality of abusive language detection.In the work reported here, we reproduce the investigation of Wiegand et al. ( 2019) to determine differences between different sampling strategies.They compared boosted random sampling, where abusive posts are upsampled, and biased topic sampling, which focuses on topics that are known to cause abusive language.Instead of comparing individual datasets created using these sampling strategies, we use the sampling strategies on a single, large dataset, thus eliminating the textual source of the dataset as a potential confounding factor.We show that differences in the textual source can have more effect than the chosen sampling strategy.
Hybrid scheme for factorisation: Factoring 551 using a 3-qubit NMR quantum adiabatic processor

Quantum processors are potentially superior to their classical counterparts for many computational tasks including factorisation. Circuit methods as well as adiabatic methods have already been proposed and implemented for finding the factors of a given composite number. The main challenge in scaling it to larger numbers is the unavailability of large number of qubits. Here, we propose a hybrid scheme that involves both classical and quantum computation, based on the previous work of Peng et al, Phys. Rev. Lett. 101(22), 220405 (2008), which reduces the number of qubits required for factorisation. The classical part involves setting up and partially simplifying a set of bit-wise factoring equations and the quantum part involves solving these coupled equations using a quantum adiabatic process. We demonstrate the hybrid scheme by factoring 551 using a 3-qubit NMR quantum register.
PENGEMBANGAN E-COMIC SEBAGAI MEDIA PEMBELAJARAN UNTUK MENINGKATKAN KECAKAPAN HIDUP ANAK USIA DINI

This study aims to develop e-comics as a learning medium to improve life skills of young children. The approach used is research and development. This research method is a mixed methods design. The subjects in this study were kindergarten students group B, teachers and head master Batik Kindergarten Kudus. Data collection techniques were carried out using questionnaires, observations, interviews, and documentation. e-comic was developed referring to the stages of development carried out by Borg and Gall. The results showed that e-comic learning media is feasible to be applied in learning to improve early life skills of children. Quantitatively, the assessment of material experts is (3.87) in either category. Expert assessments are based on curriculum appropriateness, correctness of content and ways of presenting the material included in both categories), and media expert assessments obtained scores (4.0) media expert assessments are based on production considerations, visual design, and technical quality, scores (4.0) in either category. While the assessment given by students in the field test stage (3.88), wider field test (3.99), and operational test (3.91), the acquisition of the score indicates that the e-comic learning media is categorized as good, which means that e-comic learning media are effectively used to improve early life skills. Qualitatively, e-comic media as a learning medium to improve life skills can attract students' attention to learning, facilitate student learning, and stimulate students to remember material more easily.
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification

Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering.Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annotations.Creating and annotating a large amount of training data for low-resource languages is impractical and expensive.To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision.For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX.We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages.Our code and data are available at: