Reviewerly/exHarmony huggingface dataset:
Wiki2row – the In’s and Out’s or Row Suggestion with a Large Scale Knowledge Base

Row suggestion, a generalization of set expansion, is the task of augmenting a given table of text and numbers with additional, relevant rows. A viable approach is to generate trustworthy suggestions by grounding candidates in a verifiable source and in our work we focus on knowledge bases, in particular on Wikidata. Our pipeline begins by linking existing rows and columns to entities and properties. The primary focus of this work is to improve candidate generation and ranking without requiring in-domain training or fine-tuning.  Our novel contributions are to account for semantic information by using BigGraph embeddings and GPT-3 free text generation, and tabular information by differentiating between in-table properties (explicit in given columns) and out-of-table properties (implicit from the knowledge base). Measured on the WikiTables benchmark, our solution exceeds or achieves comparable performance to previous state-of-the-art systems that use small, carefully curated knowledge bases (such as DBpedia). We extend our algorithm to present the first approach to bias-aware row suggestion when table completion is not achievable, that is, when we cannot define a complete set of entities. We suggest quantitative measures to evaluate performance on this task.
Algorithmic Thinking as the Meaningful Component of Cognitive Competencies of the Future Engineer

The given paper contains the explanation of the importance of algorithmic thinking development and introduces the idea of special formation of the operational components of this thinking in order to improve the mastering of the educational actions in the sphere of mathematics.The research describes certain specific features of studying higher mathematics at Technical Universities.The aim of the research is to determine experimentally and substantiate the impact of the development of algorithmic thinking components on the level of mathematical knowledge and skills formation.On the base of Vinnytsia National Technical University in 2017, 2018, 2019 the experiment was carried out, it consisted of two stages: exploratory stage and formative stage.We responded 275 students.Main conclusions: the problem of algorithmic thinking formation of schoolchildren and students is generally recognized in the world; only a small number of students are able to apply the operational components of algorithmic thinking comprehensively in a holistic process of solving various problems, manage their mental activity consciously and think logically; if students develop techniques of mental actions, which are the main components of algorithmic thinking (the ability to generalize, classify, draw analogies, establish patterns and reason logically), it contributes to better learning of higher mathematics.
NMF Ensembles? Not for Text Summarization!

Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results.Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology.However, our extensive empirical investigation indicates otherwise.In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.
Non-Markovian Open Dynamics from Collision Models

Convex combinations of completely positive (CP) as well as CP-divisible, continuous time dynamical maps arising from collision models are investigated. While the individual maps are both CP and Markovian we find that convex combinations remain CP but not necessarily Markovian. Examples of such combinations for qubit dynamical maps arising from collisional models are worked out and the invertibility, CP-divisibility, P-divisibility as well as Markovian properties of such maps are explored.
TAGGING AND PARSING OF MULTIDOMAIN COLLECTIONS

In this paper we describe our submission to GramEval2020 competition on morphological tagging, lemmatization and dependency parsing. Our model uses biaffine attention over the BERT representations. The main feature of our work is the extensive usage of language model, tagger and parser fine-tuning on several distinct genres and the implementation of genre classifier. To deal with dataset idiosyncrasies we also extensively apply handwritten rules. Our model took second place in the overall model performance scoring 90.8 aggregate measure over all 4 tasks
Generating Narrative Text in a Switching Dynamical System

Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible.Modern methods use language models for more robust generation, but often lack an explicit representation of the scaffolding and dynamics that guide a coherent narrative.This paper introduces a new model that integrates explicit narrative structure with neural language models, formalizing narrative modeling as a Switching Linear Dynamical System (SLDS).A SLDS is a dynamical system in which the latent dynamics of the system (i.e.how the state vector transforms over time) is controlled by top-level discrete switching variables.The switching variables represent narrative structure (e.g., sentiment or discourse states), while the latent state vector encodes information on the current state of the narrative.This probabilistic formulation allows us to control generation, and can be learned in a semi-supervised fashion using both labeled and unlabeled data.Additionally, we derive a Gibbs sampler for our model that can "fill in" arbitrary parts of the narrative, guided by the switching variables.Our filled-in (English language) narratives outperform several baselines on both automatic and human evaluations.* Author now at Microsoft 1 We use the term scaffold as an umbrella term to cover many types of plans and structures that underlie stories.Tom didn't know why his internet speed was so slow.Tom wasn't sure what to do with his computer.
Artificial Bee Colony Optimization Based Despeckling Framework for Ultrasound Images

This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images.
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard Problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Albeit new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark.
Formalization of Automated Trading Systems in a Concurrent Linear Framework

We present a declarative and modular specification of an automated trading system (ATS) in the concurrent linear framework CLF.We implemented it in Celf, a CLF type checker which also supports executing CLF specifications.We outline the verification of two representative properties of trading systems using generative grammars, an approach to reasoning about CLF specifications.
A Global Two-Stage Histogram Equalization Method for Gray-Level Images

Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of well-known methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods.
Experimental lower bounds to the classical capacity of quantum channels

We show an experimental procedure to certify the classical capacity for noisy qubit channels. The method makes use of a fixed bipartite entangled state, where the system qubit is sent to the channel input and the set of local measurements, ${\ensuremath{\sigma}}_{x}\ensuremath{\bigotimes}{\ensuremath{\sigma}}_{x}, {\ensuremath{\sigma}}_{y}\ensuremath{\bigotimes}{\ensuremath{\sigma}}_{y}$, and ${\ensuremath{\sigma}}_{z}\ensuremath{\bigotimes}{\ensuremath{\sigma}}_{z}$, is performed at the channel output and the ancilla qubit, thus without resorting to full quantum process tomography. The witness to the classical capacity is then achieved by reconstructing sets of conditional probabilities, noise deconvolution, and classical optimization of the pertaining mutual information. The performance of the method to provide lower bounds to the classical capacity is tested by a two-photon polarization entangled state in Pauli channels and amplitude damping channels. The measured lower bounds to the channels are in high agreement with the simulated data, which take into account both the experimental entanglement fidelity $F=0.979\ifmmode\pm\else\textpm\fi{}0.011$ of the input state and the systematic experimental imperfections.
On Uniform Equivalence of Epistemic Logic Programs

Abstract Epistemic Logic Programs (ELPs) extend Answer Set Programming (ASP) with epistemic negation and have received renewed interest in recent years. This led to the development of new research and efficient solving systems for ELPs. In practice, ELPs are often written in a modular way, where each module interacts with other modules by accepting sets of facts as input, and passing on sets of facts as output. An interesting question then presents itself: under which conditions can such a module be replaced by another one without changing the outcome, for any set of input facts? This problem is known as uniform equivalence, and has been studied extensively for ASP. For ELPs, however, such an investigation is, as of yet, missing. In this paper, we therefore propose a characterization of uniform equivalence that can be directly applied to the language of state-of-the-art ELP solvers. We also investigate the computational complexity of deciding uniform equivalence for two ELPs, and show that it is on the third level of the polynomial hierarchy.
FENAS: Flexible and Expressive Neural Architecture Search

Architecture search is the automatic process of designing the model or cell structure that is optimal for the given dataset or task.Recently, this approach has shown good improvements in terms of performance (tested on language modeling and image classification) with reasonable training speed using a weight sharing-based approach called Efficient Neural Architecture Search (ENAS).In this work, we propose a novel architecture search algorithm called Flexible and Expressible Neural Architecture Search (FENAS), with more flexible and expressible search space than ENAS, in terms of more activation functions, input edges, and atomic operations.Also, our FE-NAS approach is able to reproduce the wellknown LSTM and GRU architectures (unlike ENAS), and is also able to initialize with them for finding architectures more efficiently.We explore this extended search space via evolutionary search and show that FENAS performs significantly better on several popular text classification tasks and performs similar to ENAS on standard language model benchmark.Further, we present ablations and analyses on our FENAS approach.
Scalable Source Code Similarity Detection in Large Code Repositories

Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations.Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy.Other maintenance changes, such as extensions or patches, must be applied multiple times.Furthermore, the diversity of coding styles and flexibility of modern languages makes it difficult and cost ineffective to manually inspect large code repositories.Therefore, detection is only feasible by automatic techniques.We present an efficient and scalable approach for similar code fragment identification based on source code control flow graphs fingerprinting.The source code is processed to generate control flow graphs that are then hashed to create a unique fingerprint of the code capturing semantics as well as syntax similarity.The fingerprints can then be efficiently stored and retrieved to perform similarity search between code fragments.Experimental results from our prototype implementation supports the validity of our approach and show its effectiveness and efficiency in comparison with other solutions.
Evaluation of Ensemble Machines in Breast Cancer Prediction

Breast cancer is one of the most deadly diseases encountered among women for which the cause is not clearly defined yet. Early diagnosis may help the physicians in the treatment of this deadly disease which could turn out fatal otherwise. Machine Learning techniques are employed in the process of detecting breast cancer with greater accuracy. Individual classifiers employed in this process, predicted the disease with less accuracy when compared with ensemble models. Ensemble methods employ a group of classifiers to individually classify the data. It then combines the result of the individual classifiers using weighted voting of their predictions. Ensemble machines perform better than individual models and show improved levels in the accuracy of the prediction system. This paper examines and evaluates different ensemble machines that are used in the prediction of breast cancer and tries to identify the combinations that prove to be better than the existing ones.
Mathematical analysis of a simplified general type-2 fuzzy PID controller

For the type reduction of general type-2 fuzzy PID controller is time consuming and the mathematical expression of general type-2 fuzzy PID controller is difficult to derived. So, a simplified general type-2 fuzzy PID (SGT2-FPID) controller is studied in this article. The SGT2-FPID controller adopts triangular function as the primary and secondary membership function. Then the primary membership degree of apex for the secondary membership degree will be applied to get the output of SGT2-FPID controller, which can reduce the computation complexity of general type-2 fuzzy controller type reduction. Furthermore, the mathematical expressions of SGT2-FPID controller, type-1 fuzzy PID controller and interval type-2 fuzzy PID controller are discussed. Finally, 4 plants are applied to demonstrate the effectiveness and robustness of SGT2-FPID controller. The simulation results show that when the plants have uncertainty in model structure, measurement and external disturbance, the SGT2-FPID controller can achieve better control performances in contrast to compared controllers.
Moving a Unique Collection to Storage: Improving Access Now and Later

The University of Illinois at Urbana-Champaign Library collected college and university publications (the C-Collection) for several decades without allocating the resources to catalog them. A project to make these items discoverable by patrons was initiated, and tens of thousands of items were added to the online catalog. These items were physically stabilized and transferred to the library’s high-density storage facility. A portion of the collection was also digitized, providing electronic access. Although circulation trended downward, there was no clear indication that materials were less accessible in high-density storage, and new items were discovered that had not previously circulated. Digital surrogates of library material clearly allowed the library to reach a much larger audience, and ideal storage conditions to preserve physical materials long-term combined with electronically available copies appear to be an ideal means for providing greater access while preserving content.
EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets

Twitter and, in general, social media has become an indispensable communication channel in times of emergency.The ubiquitousness of smartphone gadgets enables people to declare an emergency observed in real-time.As a result, more agencies are interested in programmatically monitoring Twitter (disaster relief organizations and news agencies).Therefore, recognizing the informativeness of a Tweet can help filter noise from the large volumes of Tweets.In this paper, we present our submission for WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets.Our most successful model is an ensemble of transformers, including RoBERTa, XLNet, and BERTweet trained in a Semi-Supervised Learning (SSL) setting.The proposed system achieves an F 1 score of 0.9011 on the test set (ranking 7 th on the leaderboard) and shows significant gains in performance compared to a baseline system using FastText embeddings.
Statistical Epistemic Logic

We introduce a modal logic for describing statistical knowledge, which we call statistical epistemic logic. We propose a Kripke model dealing with probability distributions and stochastic assignments, and show a stochastic semantics for the logic. To our knowledge, this is the first semantics for modal logic that can express the statistical knowledge dependent on non-deterministic inputs and the statistical significance of observed results. By using statistical epistemic logic, we express a notion of statistical secrecy with a confidence level. We also show that this logic is useful to formalize statistical hypothesis testing and differential privacy in a simple and abstract manner.
Brushless Motor Performance Optimization by Eagle Strategy with Firefly and PSO

Brushless motors has special place though different motors are available because of its special features like absence in commutation, reduced noise and longer lifetime etc., The experimental parameter tracking of BLDC Motor can be achieved by developing a Reference system and their stability is guaranteed by adopting Lyapunov Stability theorems. But the stability is guaranteed only if the adaptive system is incorporated with the powerful and efficient optimization techniques. In this paper the powerful eagle strategy with Particle Swarm optimization and Firefly algorithms are applied to evaluate the performance of brushless motor Where, Eagle Strategy(ES) with the use of Levys walk distribution function performs diversified global search and the Particle Swarm Optimization (PSO) and Firefly Algorithm(FFA) performs the efficient intensive local search. The combined operation makes the overall optimization technique as much convenient The simulation results are obtained by using MATLAB Simulink software
Excitation of the collective states of qubits in a three-qubit system

In the present paper, we have proposed the experimentally achievable method for the characterization of the collective states of qubits in a linear chain. We study a temporal dynamics of absorption of a single-photon pulse by three interacting qubits embedded in a one-dimensional open waveguide. Numerical simulations were performed for a Gaussian-shaped pulse with different frequency detunings and interaction parameters between qubits. The dynamic behavior of the excitation probability for each qubit is investigated. It was shown that the maximum probability amplitudes of excitation of qubits are reached when the frequency of external excitation coincides with the frequency of excitation of the corresponding eigenstate of the system. In this case, the magnitude of the probability amplitude of each qubit in the chain unambiguously correlates with the contribution of this qubit to the corresponding collective state of the system, and the decay of these amplitudes is determined by the resonance width arising from the interaction of the qubit with the photon field of the waveguide. Therefore, we show that the pulsed harmonic probe can be used for the characterization of the energies, widths, and the wave functions of the collective states in a one dimensional qubit chain.