Reviewerly/exHarmony huggingface dataset:
Operational advantages provided by nonclassical teleportation

The standard benchmark for teleportation is the average fidelity of teleportation and according to this benchmark not all states are useful for teleportation. It was recently shown, however, that all entangled states lead to nonclassical teleportation, with there being no classical scheme able to reproduce the states teleported to Bob. Here we study the operational significance of this result. On the one hand, we demonstrate that every state is useful for teleportation if a generalization of the average fidelity of teleportation is considered which concerns teleporting quantum correlations. On the other hand, we show the strength of a particular entangled state and entangled measurement for teleportation—as quantified by the robustness of teleportation—precisely characterizes their ability to offer an advantage in the task of subchannel discrimination with side information. This connection allows us to prove that every entangled state outperforms all separable states when acting as a quantum memory in this discrimination task. Finally, within the context of a resource theory of teleportation, we show that the two operational tasks considered provide complete sets of monotones for two partial orders based on the notion of teleportation simulation, one classical and one quantum.Received 29 August 2019Accepted 2 March 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.023029Published 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 AreasEntanglement measuresQuantum channelsQuantum entanglementQuantum networksQuantum nonlocalityQuantum repeatersQuantum teleportationResource theoriesAtomic, Molecular & OpticalQuantum Information
Universal superposition codes: Capacity regions of compound quantum broadcast channel with confidential messages

We derive universal codes for transmission of broadcast and confidential messages over classical-quantum-quantum and fully quantum channels. These codes are robust to channel uncertainties considered in the compound model. To construct these codes we generalize random codes for transmission of public messages, to derive a universal superposition coding for the compound quantum broadcast channel. As an application, we give a multi-letter characterization of regions corresponding to the capacity of the compound quantum broadcast channel for transmitting broadcast and confidential messages simultaneously. This is done for two types of broadcast messages, one called public and the other common.
Quantifying Intimacy in Language

Intimacy is a fundamental aspect of how we relate to others in social settings.Language encodes the social information of intimacy through both topics and other more subtle cues (such as linguistic hedging and swearing).Here, we introduce a new computational framework for studying expressions of the intimacy in language with an accompanying dataset and deep learning model for accurately predicting the intimacy level of questions (Pearson's r=0.87).Through analyzing a dataset of 80.5M questions across social media, books, and films, we show that individuals employ interpersonal pragmatic moves in their language to align their intimacy with social settings.Then, in three studies, we further demonstrate how individuals modulate their intimacy to match social norms around gender, social distance, and audience, each validating key findings from studies in social psychology.Our work demonstrates that intimacy is a pervasive and impactful social dimension of language.
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

An important challenge for safety in machine learning and artificial intelligence systems is a set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart’s or Campbell’s law. This paper presents additional failure modes for interactions within multi-agent systems that are closely related. These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case, and are also already occurring, largely unnoticed. After motivating the discussion with examples from poker-playing artificial intelligence (AI), the paper explains why these failure modes are in some senses unavoidable. Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes: accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking. The paper then discusses how extant literature on multi-agent AI fails to address these failure modes, and identifies work which may be useful for the mitigation of these failure modes.
Reactive Navigation on Natural Environments by Continuous Classification of Ground Traversability

Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describes the case study of mobile robot Andabata that classifies traversable points from 3D laser scans acquired in motion of its vicinity to build 2D local traversability maps. Realistic robotic simulations with Gazebo were employed to appropriately adjust reactive behaviors. As a result, successful navigation tests with Andabata using the robot operating system (ROS) were performed on natural environments at low speeds.
On the freedom in representing quantum operations

We discuss the effects of a gauge freedom in representing quantum information processing devices, and its implications for characterizing these devices. We demonstrate with experimentally relevant examples that there exists equally valid descriptions of the same experiment which distribute errors differently among objects in a gate-set, leading to different error rates. Consequently, it can be misleading to attach a concrete operational meaning to figures of merit for individual gate-set elements. We propose an alternative operational figure of merit for a gate-set, the mean variation error, and a protocol for measuring this figure.
Speakers Fill Lexical Semantic Gaps with Context

Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient.If ambiguous words cannot be disambiguated from context, however, this gain in efficiency might make language less clearresulting in frequent miscommunication.For a language to be clear and efficiently encoded, we posit that the lexical ambiguity of a word type should correlate with how much information context provides about it, on average.To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this-one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages.We validate these measures by showing that, on six high-resource languages, there are significant Pearson correlations between our BERT-based estimate of ambiguity and the number of synonyms a word has in Word-Net (e.g.ρ = 0.40 in English).We then test our main hypothesis-that a word's lexical ambiguity should negatively correlate with its contextual uncertainty-and find significant correlations on all 18 typologically diverse languages we analyse.This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest

We present CUT, a dataset for studying Civil Unrest on Twitter.Our dataset includes 4,381 tweets related to civil unrest, hand-annotated with information related to the study of civil unrest discussion and events.Our dataset is drawn from 42 countries from 2014 to 2019.We present baseline systems trained on this data for the identification of tweets related to civil unrest.We include a discussion of ethical issues related to research on this topic.
Minimizing Backaction through Entangled Measurements

When an observable is measured on an evolving coherent quantum system twice, the first measurement generally alters the statistics of the second one, which is known as measurement backaction. We introduce, and push to its theoretical and experimental limits, a novel method of backaction evasion, whereby entangled collective measurements are performed on several copies of the system. This method is inspired by a similar idea designed for the problem of measuring quantum work [Perarnau-Llobet et al., Phys. Rev. Lett. 118, 070601 (2017)]. By using entanglement as a resource, we show that the backaction can be extremely suppressed compared to all previous schemes. Importantly, the backaction can be eliminated in highly coherent processes.
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing.Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets.For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings.A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.
Discontinuous Constituent Parsing as Sequence Labeling

This paper reduces discontinuous parsing to sequence labeling.It first shows that existing reductions for constituent parsing as labeling do not support discontinuities.Second, it fills this gap and proposes to encode tree discontinuities as nearly ordered permutations of the input sequence.Third, it studies whether such discontinuous representations are learnable.The experiments show that despite the architectural simplicity, under the right representation, the models are fast and accurate. 1
Research on the Reform and Innovation of Vocal Music Teaching in Colleges

The fundamental purpose of education is to promote better development of students. As the process of economic globalization accelerates, the teaching methods of art courses are also constantly improving. Especially in today's society with a high degree of tolerance, science and technology and the Internet have not only changed people's lives, but also changed the mode of thinking and learning. As an important front for transporting talents, the school pays more attention to strengthening the construction of student quality education and professional education service system. Obviously, the traditional education model can no longer meet the current social development's demand for high-quality talents. If we want students to achieve better development, we need to actively embrace the changes of the times, be student-oriented, follow the trend, and formulate educational policies that are more in line with the development of the times. Therefore, we need to make drastic reforms and innovations in vocal music teaching, and continuously improve the quality and level of vocal music teaching by reconstructing the teaching model. This article briefly describes the positive significance of the current college vocal music teaching reform and innovation, as well as the specific reform and innovation strategies for reference.
Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples

A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one.However, the performance of GEC models with the seq2seq framework heavily relies on the size and quality of the corpus on hand.We propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying the weak spots of a model, and to enhance the model by gradually adding the generated adversarial examples to the training set.Extensive experimental results show that such adversarial training can improve both the generalization and robustness of GEC models.
Interpretable Entity Representations through Large-Scale Typing

In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models.The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret.In this paper, we present an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box.Our representations are vectors whose values correspond to posterior probabilities over finegrained entity types, indicating the confidence of a typing model's decision that the entity belongs to the corresponding type.We obtain these representations using a fine-grained entity typing model, trained either on supervised ultra-fine entity typing data (Choi et al., 2018) or distantly-supervised examples from Wikipedia.On entity probing tasks involving recognizing entity identity, our embeddings used in parameter-free downstream models achieve competitive performance with ELMoand BERT-based embeddings in trained models.We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains.Finally, we show that these embeddings can be post-hoc modified through a small number of rules to incorporate domain knowledge and improve performance.
Possibilities of Using AR and VR Technologies in Teaching Mathematics to High School Students

Difficulties at the beginning of studying stereometry in 10 classes are well known from school practice.One of the main reasons for this is poorly developed spatial thinking and imagination.Therefore, the article describes some possibilities of using virtual and augmented reality in the process of teaching mathematics to students in 10-11 classes.It is established that pedagogical research in the field of theory of teaching mathematics should go this way.Undoubtedly, the lessons of mathematics with the addition of reality have a huge future in the field of education.The extensive use of virtual and augmented reality technologies in teaching mathematics proves the effectiveness and it is an attractive motivation for students.The use of augmented reality can be implemented in the teaching of mathematics from primary schools to universities.The object of research is the process of the formation of spatial imagination using virtual and augmented reality technologies in the process of teaching mathematics.The subject of the study is the environment of virtual and augmented reality.Research methods are analysis of publications on the problem, generalization of domestic and foreign experience, theoretical analysis, system analysis, systematization, and generalization of facts and patterns of research for the formation of spatial thinking using virtual and augmented reality environments, substantiation of the main conclusions.In our opinion, it is best to use free and well-made software such as ROAR AR for the Android operating system.Even in universities, it is advisable to use augmented reality systems in the training of future mathematics teachers, as they will be able to stop the decline in the popularity of mathematics studies in secondary and high schools by using such innovative teaching methods in their future pedagogical activities.
A Generalization of Wirtinger Flow for Exact Interferometric Inversion

Interferometric inversion involves recovery of a signal from cross-correlations of its linear transformations. A close relative of interferometric inversion is the generalized phase retrieval problem, for which significant advancements were made in recent years despite the ill-posed and nonconvex nature of the problem. One such prominent phase retrieval method is Wirtinger flow (WF) [E. J. Candes, X. Li, and M. Soltanolkotabi, IEEE Trans. Inform. Theory, 61 (2015), pp. 1985--2007], a computationally efficient nonconvex optimization framework that provides high probability guarantees for exact recovery under certain measurement models, specifically coded diffraction patterns, and Gaussian sampling vectors. In this paper, we develop a generalization of WF for interferometric inversion, which we refer to as generalized Wirtinger flow (GWF). Our approach treats the theory of low rank matrix recovery and the nonconvex optimization approach of WF in a unified framework. Such a treatment facilitates the identification of a new sufficient condition on the lifted forward model for exact recovery via GWF and results in a deterministic framework based on geometric arguments for convergence. Thereby, GWF extends the model specific probabilistic guarantees in [12] to arbitrary measurement maps characterized over the equivalent lifted domain in the context of interferometric inversion, covering both random and deterministic measurement models. We then establish our sufficient condition for the cross-correlations of linear measurements collected by complex Gaussian sampling vectors. In the particular case of interferometric inversion with the Gaussian model, we show that the exact recovery theory of standard WF implies our sufficient condition when we have cross-correlations, and the regularity condition of WF is redundant. Finally, we demonstrate the effectiveness of GWF numerically in a deterministic, interferometric multistatic radar imaging scenario.
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation

We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but only limited labeled data in the target domain, with the goal to train a good target model.A straightforward solution is to fine-tune a pre-trained source model by using those limited labeled target data, but it usually cannot work well due to the considerable difference between the data distributions of the source and target domains.Moreover, the availability of multiple modalities (i.e., images, questions and answers) in VQA poses further challenges in modeling the transferability between various modalities.In this paper, we address the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities.Specifically, we align the data distributions of the source and target domains by considering those modalities both jointly and separately.Extensive experiments on VQA 2.0 and VizWiz datasets demonstrate that our proposed method outperforms the existing stateof-the-art baselines for open-ended VQA in this challenging domain adaptation setting.
AI Chatbot for Tourist Recommendations: A Case Study in the City of Jeddah, Saudi Arabia

&lt;span lang="EN-GB"&gt;Chatbots have gained increasing importance in today research with a lot of available applications. They are flexible and can be applied in many application domains. In tourism, Chatbots can recommend sights, hotels, activities, or even full travel plans. Usually, users are required to visit plenty of websites and install different Apps into their mobile phones in order to navigate the city and planning for the trip which is inconvenient and time-consuming. To address this issue, this paper proposes “Smart Guidance”, an AI text-based chatbot that is developed as a mobile application. It simulates a chat with users in a natural language, it also provides a two-way interaction and it is a single point of contact for all user communications. We provide a use case in the city of Jeddah, Saudi Arabia. Jeddah is the second largest city in Saudi Arabia and tourists have various choices when it comes to moving around the city. The final evaluation of the chatbot showed that the bot could understand meanings and users’ requests. Moreover, users found that the interaction is effective, and the responses are provided immediately. Users were satisfied and like the idea that they could express their needs in their natural language. Furthermore, users enjoyed the interaction and felt engaged.&lt;/span&gt;
Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor

Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of 65%, 98%, and 78% in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.
Mossad: defeating software plagiarism detection

Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, MOSSAD, that defeats popular software plagiarism detection tools. MOSSAD comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. MOSSAD is effective at defeating four plagiarism detectors, including Moss and JPlag. MOSSAD is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, MOSSAD can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of MOSSAD across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate MOSSAD-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education.