Reviewerly/exHarmony huggingface dataset:
Foreshadowing the Benefits of Incidental Supervision

Learning theory mostly addresses the standard learning paradigm, assuming the availability of complete and correct supervision signals for large amounts of data. However, in practice, machine learning researchers and practitioners acquire and make use of a range of {\em incidental supervision} signals that only have statistical associations with the gold supervision. This paper addresses the question: {\em Can one quantify models' performance when learning with such supervision signals, without going through an exhaustive experimentation process with various supervision signals and learning protocols?} To quantify the benefits of various incidental supervision signals, we propose a unified PAC-Bayesian Informativeness measure (PABI), characterizing the reduction in uncertainty that incidental supervision signals provide. We then demonstrate PABI's use in quantifying various types of incidental signals such as partial labels, noisy labels, constraints, cross-domain signals, and some combinations of these. Experiments on named entity recognition and question answering show that PABI correlates well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
Quantum states of an electromagnetic field interacting with a classical current and their applications to radiation problems

Synchrotron radiation was originally studied by classical methods using the Liénard–Wiechert potentials of electric currents. Subsequently, quantum corrections to the classical formulas were studied, considering the emission of photons arising from electronic transitions between spectral levels, described in terms of the Dirac equation. In this paper, an intermediate approach is considered, in which electric currents generating the radiation are considered classically while the quantum nature of the radiation is taken into account exactly. Such an approximate approach may be helpful in some cases; it allows one to study one-photon and multi-photon radiation without complicating calculations using corresponding solutions of the Dirac equation. Here, exact quantum states of an electromagnetic field interacting with classical currents are constructed and their properties studied. With their help, the probability of photon emission by classical currents is calculated and relatively simple formulas for one-photon and multi-photon radiation are obtained. Using the specific circular electric current, the corresponding synchrotron radiation is calculated. The relationship between the obtained results and those known before are discussed, for example with the Schott formula, with Schwinger calculations, with one-photon radiation of scalar particles due to transitions between Landau levels, and with some previous results of calculating two-photon synchrotron radiation.
Exemplar Guided Neural Dialogue Generation

Humans benefit from previous experiences when taking actions. Similarly, related examples from the training data also provide exemplary information for neural dialogue models when responding to a given input message. However, effectively fusing such exemplary information into dialogue generation is non-trivial: useful exemplars are required to be not only literally-similar, but also topic-related with the given context. Noisy exemplars impair the neural dialogue models understanding the conversation topics and even corrupt the response generation. To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model. In the first stage, a small subset of conversations is retrieved from a training set given a dialogue context. These candidate exemplars are then finely ranked regarding the topical proximity to choose the best-matched exemplar response. To further induce the neural dialogue generation model consulting the exemplar response and the conversation topics more faithfully, we introduce a multi-source sampling mechanism to provide the dialogue model with both local exemplary semantics and global topical guidance during decoding. Empirical evaluations on a large-scale conversation dataset show that the proposed approach significantly outperforms the state-of-the-art in terms of both the quantitative metrics and human evaluations.
Learning Autoencoders with Relational Regularization

A new algorithmic framework is proposed for learning autoencoders of data distributions. We minimize the discrepancy between the model and target distributions, with a \emph{relational regularization} on the learnable latent prior. This regularization penalizes the fused Gromov-Wasserstein (FGW) distance between the latent prior and its corresponding posterior, allowing one to flexibly learn a structured prior distribution associated with the generative model. Moreover, it helps co-training of multiple autoencoders even if they have heterogeneous architectures and incomparable latent spaces. We implement the framework with two scalable algorithms, making it applicable for both probabilistic and deterministic autoencoders. Our relational regularized autoencoder (RAE) outperforms existing methods, $e.g.$, the variational autoencoder, Wasserstein autoencoder, and their variants, on generating images. Additionally, our relational co-training strategy for autoencoders achieves encouraging results in both synthesis and real-world multi-view learning tasks. The code is at this https URL Relational-AutoEncoders.
Mvgan Maximizing Time-Lag Aware Canonical Correlation for Baseball Highlight Generation

This paper presents multi-view unsupervised generative adversarial network maximizing time-lag aware canonical correlation (MvGAN) for baseball highlight generation. MvGAN has the following two contributions. First, MvGAN utilizes textual, visual and audio features calculated from tweets and videos as multi-view features. MvGAN which adopts these multi-view features is the effective work for highlight generation of baseball videos. Second, since there is a temporal difference between posted tweets and the corresponding events, MvGAN introduces a novel feature embedding scheme considering a time-lag between textual features and other features. Specifically, the proposed method newly derives the time-lag aware canonical correlation maximization of these multi-view features. This is the biggest contribution of this paper. Furthermore, since MvGAN is an unsupervised method for highlight generation, a large amount of training data with annotation is not needed. Thus, the proposed method has high applicability to the real world.
Solving the Problem of Overcoming a Staircase Flight by a Multi-Link Crawling Robot

The paper considers a three-link crawling robot combining the capabilities of a caterpillar-, snake- and worm-like crawling systems, as well as equipped with controlled supports with a variable coefficient of friction. An algorithm of overcoming a flight of stairs is described using the example of a device crawling onto one step. The specified algorithm consists of two stages: positioning (not studied in this paper) and the crawling onto a step. The latter is divided into 12 stages, during each of which two or three supports are fixed on the surface. Stages 1-11 occur in a vertical plane parallel to the flight of stairs (caterpillar-like movement), and stage 12 - in the horizontal plane (serpentine-like movement). Also, at a number of stages, the elongation / shortening of the links is observed (a worm-like principle of movement). The execution adequacy of the algorithm of crawling onto a step is confirmed by the results of a numerical experiment.
Domain Adaptation for Semantic Parsing

Recently, semantic parsing has attracted much attention in the community. Although many neural modeling efforts have greatly improved the performance, it still suffers from the data scarcity issue. In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic parser benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages, i.e., focusing on domain invariant and domain specific information, respectively. In the coarse stage, our novel domain discrimination component and domain relevance attention encourage the model to learn transferable domain general structures. In the fine stage, the model is guided to concentrate on domain related details. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies. Additionally, we show that our model can well exploit limited target data to capture the difference between the source and target domain, even when the target domain has far fewer training instances.
Ontology-based Document Spanning Systems for Information Extraction

Information Extraction (IE) is the task of automatically organizing in a structured form data extracted from free text documents. In several contexts, it is often desirable that the extracted data are then organized according to an ontology, which provides a formal and conceptual representation of the domain of interest. Ontologies allow for a better data interpretation, as well as for their semantic integration with other information, as in Ontology-based Data Access (OBDA), a popular declarative framework for data management where an ontology is connected to a data layer through mappings. However, the data layer considered so far in OBDA has consisted essentially of relational databases, and how to declaratively couple an ontology with unstructured data sources is still unexplored. By leveraging the recent study on document spanners for rule-based IE by Fagin et al., in this paper, we propose a new framework that allows to map text documents to ontologies, in the spirit of OBDA. We investigate the problem of answering conjunctive queries in this framework. For ontologies specified in the Description Logics [Formula: see text] and [Formula: see text], we show that the problem is polynomial in the size of the underlying documents. We also provide algorithms to solve query answering by rewriting the input query on the basis of the ontology and its mapping toward the source documents. Through these techniques, we pursue a virtual approach, similar to that typically adopted in OBDA, which allows us to answer a query without having to first populate the entire ontology. Interestingly, for [Formula: see text], both the spanners used in the mapping and the one computed by the rewriting algorithm belong to the same class of expressiveness. This holds also for [Formula: see text], modulo some limitations on the form of the mapping. These results say that in these cases our framework can be easily implemented by decoupling ontology management and document access, which can be delegated to an external IE system able to process the extraction rules we use in the mapping.
Resource assessment of Sakesar limestone as aggregate from salt range Pakistan based on geotechnical properties

The Sakesar limestone of Eocene age from Salt Range has been studied in detailed based on geotechnical properties with emphasis on its aggregate potential.The Sakesar Limestone was selected based on its easy availability and access with basic aim focused on economic aggregate source for future if proved as good potential.The rock samples were analyzed based on standards procedures of AASHTO by adopting ASTM standards to prove its potential and recommendation.A total of four potential sites are sampled that are A total of four localities are selected for sampling that are Tobar Valley, Bestway Cement Plant Quarry and Pail-Padhrar section.The results were analyzed for suitability as Sub Base, Base Course, Surface Course and Concrete potential.The study portrays that all sample of Sakesar limestone fall in the permissible limits of ASTM and classified as suitable aggregate for road construction work, except for Dhak Pass (<125m) which are considered as weak and porous.
niacin: A Python package for text data enrichment

A common component of frameworks for building robust and accurate learning models is a utility for performing sets of perturbations on the input data.In machine vision, for example, rotating, cropping, stretching, warping, and flipping training images have all been shown to increase model accuracy, and reduce certain kinds of overfitting (Chen, Dobriban, & Lee, 2019).Popular machine vision libraries like PyTorch, Tensorflow, and FastAI all have built-in utilities for performing these transformations (Abadi et al.
Implementation of blockchain technology in the smart city

This document presents an attempt to determine guidelines for the implementation of the blockchain projects within smart city (SC) information systems (IS) to improve their security and sustainability through harnessing benefits of blockchain technology.Aldo available scientific sources are scarce a review of contemporary scientific literature and other available sources attempts to determine weaknesses of current IS within SC and to compare their vs. benefits of the blockchain technology in order to show that blockchain technology can offer potential solution to weaknesses and improve SC IS and therefore the sustainability of the SC itself.However, this research only shows the need for such implementations and discusses what further research is required to provide a framework for the implementation of blockchain technology in smart cities.
Security in Mixed Time and Event Triggered Cyber-Physical Systems using Moving Target Defense

Memory corruption attacks such as code injection, code reuse, and non-control data attacks have become widely popular for compromising safety-critical Cyber-Physical Systems (CPS). Moving target defense (MTD) techniques such as instruction set randomization (ISR), address space randomization (ASR), and data space randomization (DSR) can be used to protect systems against such attacks. CPS often use time-triggered architectures to guarantee predictable and reliable operation. MTD techniques can cause time delays with unpredictable behavior. To protect CPS against memory corruption attacks, MTD techniques can be implemented in a mixed time and event-triggered architecture that provides capabilities for maintaining safety and availability during an attack. This paper presents a mixed time and event-triggered MTD security approach based on the ARINC 653 architecture that provides predictable and reliable operation during normal operation and rapid detection and reconfiguration upon detection of attacks. We leverage a hardware-in-the-loop testbed and an advanced emergency braking system (AEBS) case study to show the effectiveness of our approach.
FORMATION OF BACHELORS’ SUBJECTIVE POSITION USING THE CASE METHOD AND MODELS FOR ORGANIZING JOINT ACTIVITIES

Раскрыта возможность проявления саморазвития бакалаврами педагогического образования как основного компонента субъектной позиции и субъект-субъектных отношений. Охарактеризована взаимосвязь ключевых терминов: субъект, субъектность, субъектная позиция, субъект-субъектные отношения. С применением диагностического инструментария выявлены специфика понимания, его уровень и причины отказа демонстрации субъектной позиции бакалавров в образовательном процессе педагогического вуза. Показано, как с помощью кейс-метода можно формировать навык самоорганизации бакалавров при организации совместной деятельности преподавателя и студентов в педагогическом вузе. Self-development and self-organization of the personality are of great importance for the effectiveness of the educational and professional activities of the teacher. The article reveals the possibility of self-development of a bachelor of pedagogical education as the main component of the subject position and subject-subject relations. The interconnection of the key terms: subject, subjectivity, subjective position, subject-subject relations, is characterized. Using diagnostic tools, such as an author’s questionnaire consisting of open and closed questions, the specifics of understanding, the level and reasons for the refusal to demonstrate the subject position by bachelors in the educational process of a pedagogical university are revealed. Processing of the results of the questionnaire was carried out by calculating the average score and content analysis. As a result of the study, it was clarified that many factors influence the manifestation of the students’ subjective position in the process of their study at the university, some of which are the lack of self-organization, self-development skills, the predominant influence of the teacher and the lack of their joint activities. The article shows how, with the help of the case-method, it is possible to build the skill of self-organization of bachelors when organizing joint activities of a teacher and students at a pedagogical university. The substantial similarity of the implementation of the case method with the models of joint activity: authoritarian, leadership, partnership, is indicated. The emphasis is on the fact that, as part of the case method, when organizing joint activities, the main task is to build the subjectivation strategy between the teacher and the students in the process of solving various types of cases that become more complicated for each bachelor’s course. Features of the organization of students’ and teachers’ activities from the reproduction strategy to the strategy of cooperation through the demonstration by subjects of the educational process of their subjective position are presented.
Creation of Interactive Virtual Reality Scenarios as a Training and Education Tool

A novel approach to the creation of realistic training scenarios for safety-critical industrial applications is presented. This is based on virtual reality techniques but extended by the incorporation of a range of options for interactivity, which permit the trainee to take actions in realistic ways in the simulated environment. These actions may include potentially dangerous errors, with realistic consequences simulated, but in complete safety. Any real environment may be simulated both visually and functionally in the virtual environment. An innovative feature is the use of virtual buttons displayed on the image of the user's hand, thus avoiding the need for accessories such as haptic gloves. The system enables trainee specialists to gain realistic operational experience without the anxieties of causing damage in a real environment, but it is also relevant to a wide range of applications where rich interactivity is needed.
Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression

Abstract Edge-enhancing diffusion (EED) can reconstruct a close approximation of an original image from a small subset of its pixels. This makes it an attractive foundation for PDE based image compression. In this work, we generalize second-order EED to a fourth-order counterpart. It involves a fourth-order diffusion tensor that is constructed from the regularized image gradient in a similar way as in traditional second-order EED, permitting diffusion along edges, while applying a non-linear diffusivity function across them. We show that our fourth-order diffusion tensor formalism provides a unifying framework for all previous anisotropic fourth-order diffusion based methods, and that it provides additional flexibility. We achieve an efficient implementation using a fast semi-iterative scheme. Experimental results on natural and medical images suggest that our novel fourth-order method produces more accurate reconstructions compared to the existing second-order EED.
Laying the Foundation for Effective Partnerships: An Examination of Data Sharing Agreements

In the midst of the COVID-19 pandemic, data has never been more salient. COVID has generated new data demands and increased cross-sector data collaboration. Yet, these data collaborations require careful planning and evaluation of risks and opportunities, especially when sharing sensitive data. Data sharing agreements (DSAs) are written agreements that establish the terms for how data are shared between parties and are important for establishing accountability and trust. However, negotiating DSAs is often time consuming, and collaborators lacking legal or financial capacity are disadvantaged. Contracts for Data Collaboration (C4DC) is a joint initiative between SDSN TReNDS, NYU’s GovLab, the World Economic Forum, and the University of Washington, working to strengthen trust and transparency of data collaboratives. The partners have created an online library of DSAs which represents a selection of data applications and contexts. This report introduces C4DC and its DSA library. We demonstrate how the library can support the data community to strengthen future data collaborations by showcasing various DSA applications and key considerations. First, we explain our method of analyzing the agreements and consider how six major issues are addressed by different agreements in the library. Key issues discussed include data use, access, breaches, proprietary issues, publicization of the analysis, and deletion of data upon termination of the agreement. For each of these issues, we describe approaches illustrated with examples from the library. While our analysis suggests some pertinent issues are regularly not addressed in DSAs, we have identified common areas of practice that may be helpful for entities negotiating partnership agreements to consider in the future.
Leibniz equality is isomorphic to Martin-Löf identity, parametrically

Abstract Consider two widely used definitions of equality. That of Leibniz: one value equals another if any predicate that holds of the first holds of the second. And that of Martin-Löf: the type identifying one value with another is occupied if the two values are identical. The former dates back several centuries, while the latter is widely used in proof systems such as Agda and Coq. Here we show that the two definitions are isomorphic: we can convert any proof of Leibniz equality to one of Martin-Löf identity and vice versa , and each conversion followed by the other is the identity. One direction of the isomorphism depends crucially on values of the type corresponding to Leibniz equality satisfying functional extensionality and Reynolds’ notion of parametricity. The existence of the conversions is widely known (meaning that if one can prove one equality then one can prove the other), but that the two conversions form an isomorphism (internally) in the presence of parametricity and functional extensionality is, we believe, new. Our result is a special case of a more general relation that holds between inductive families and their Church encodings. Our proofs are given inside type theory, rather than meta-theoretically. Our paper is a literate Agda script.