Reviewerly/exHarmony huggingface dataset:
Supertagging with CCG primitives

In CCG and other highly lexicalized grammars, supertagging a sentence's words with their lexical categories is a critical step for efficient parsing.Because of the high degree of lexicalization in these grammars, the lexical categories can be very complex.Existing approaches to supervised CCG supertagging treat the categories as atomic units, even when the categories are not simple; when they encounter words with categories unseen during training, their guesses are accordingly unsophisticated.In this paper, we make use of the primitives and operators that constitute the lexical categories of categorial grammars.Instead of opaque labels, we treat lexical categories themselves as linear sequences.We present an LSTM-based model that replaces standard word-level classification with prediction of a sequence of primitives, similarly to LSTM decoders.Our model obtains state-of-the-art word accuracy for single-task English CCG supertagging, increases parser coverage and F 1 , and is able to produce novel categories.Analysis shows a synergistic effect between this decomposed view and incorporation of prediction history.
Transformer-Capsule Model for Intent Detection (Student Abstract)

Intent recognition is one of the most crucial tasks in NLU systems, which are nowadays especially important for designing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU network implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu ,and WebApp.
Extracting the Main Trend in a Data Set: The Sequencer Algorithm

Abstract Scientists aim to extract simplicity from observations of the complex world. An important component of this process is the exploration of data in search of trends. In practice, however, this tends to be more of an art than a science. Among all trends existing in the natural world, one-dimensional trends, often called sequences, are of particular interest, as they provide insights into simple phenomena. However, some are challenging to detect, as they may be expressed in complex manners. We present the Sequencer, an algorithm designed to generically identify the main trend in a data set. It does so by constructing graphs describing the similarities between pairs of observations, computed with a set of metrics and scales. Using the fact that continuous trends lead to more elongated graphs, the algorithm can identify which aspects of the data are relevant in establishing a global sequence. Such an approach can be used beyond the proposed algorithm and can optimize the parameters of any dimensionality reduction technique. We demonstrate the power of the Sequencer using real-world data from astronomy, geology, and images from the natural world. We show that, in a number of cases, it outperforms the popular t-Distributed Stochastic Neighbor Embedding and Uniform Manifold Approximation and Projection dimensionality reduction techniques. This approach to exploratory data analysis, which does not rely on training or tuning any parameter, has the potential to enable discoveries in a wide range of scientific domains. The source code is available on GitHub, and we provide an online interface at http://sequencer.org .
The effectiveness of blended learning model based on inquiry collaborative tutorial toward students’ problem-solving skills in physics

In this study, a collaborative inquiry-based blended learning model was developed. The model was supported by model books, learning instruments, and test instruments for problem-solving skills. The purpose of this study was to examine the effectiveness of the model to practice problem-solving skills for physics students. This research included research and development. The development model used uses the 4-D model (define, design, develop, and disseminate). The model and instruments were validated by the expert. The testing phase using the effectiveness test was carried out in one shot case study pre-post test design on three classes. The model implementation test was carried out on pre-service physics teachers at a private university in Mataram. Data collection was performed using observation sheets, problem-solving tests, and student response questionnaires. The data analysis technique used was descriptive, quantitative, and qualitative. The results showed that the inquiry collaborative tutorial-based blended learning model is effective in improving problem-solving skills. Students' problem-solving skills at the visualization step and describing the problem is high, while the next stage is still in the medium category. Students also gave positive responses to learning physics using this model.
Retrain or Not Retrain? - Efficient Pruning Methods of Deep CNN Networks

Nowadays, convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation. Very often CNN networks have from several to hundred stacked layers with several megabytes of weights. One of the possible techniques to reduce complexity and memory footprint is pruning. Pruning is a process of removing weights which connect neurons from two adjacent layers in the network. The process of finding near optimal solution with specified and acceptable drop in accuracy can be more sophisticated when DL model has higher number of convolutional layers. In the paper few approaches based on retraining and no retraining are described and compared together.
Talk to Papers: Bringing Neural Question Answering to Academic Search

We introduce Talk to Papers 1 , which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search.It's designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers.We present a large improvement over classic search engine baseline on several standard QA datasets, and provide the community a collaborative data collection tool to curate the first natural language processing research QA dataset via a community effort.
How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?

Many natural languages assign grammatical gender also to inanimate nouns in the language.In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun's gender.We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender.While "embedding debiasing" methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words' context when training word embeddings is effective in removing it.Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and crosslingual settings.We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
A Summary of the Development of Cooperative and Intelligent Technology for Multi-UAV Systems

In the process of formation cooperative autonomous flight and mission execution, multi-UAV systems have the characteristics of large disturbance, strong coupling of multi variables, high degree of uncertainty and complex cooperative factors, so there are many difficulties in the design of cooperative and intelligent control system. How to effectively develop the cooperative and intelligent technology of multi-UAV systems has become an urgent problem in the field of unmanned system and artificial intelligence. This paper first introduces the key technologies involved in UAV system at present, and then analyzes the challenges faced by the development of UAV system. Finally, it focuses on the development trend of multi-UAV systems cooperation and intelligent technology in the future. The research results have strong theoretical guidance significance for the cooperation and intelligent development of multi-UAV systems in China.
Lipschitzness is all you need to tame off-policy generative adversarial imitation learning

Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success. We consider the case of off-policy generative adversarial imitation learning, and perform an in-depth review, qualitative and quantitative, of the method. We show that forcing the learned reward function to be local Lipschitz-continuous is a sine qua non condition for the method to perform well. We then study the effects of this necessary condition and provide several theoretical results involving the local Lipschitzness of the state-value function. We complement these guarantees with empirical evidence attesting to the strong positive effect that the consistent satisfaction of the Lipschitzness constraint on the reward has on imitation performance. Finally, we tackle a generic pessimistic reward preconditioning add-on spawning a large class of reward shaping methods, which makes the base method it is plugged into provably more robust, as shown in several additional theoretical guarantees. We then discuss these through a fine-grained lens and share our insights. Crucially, the guarantees derived and reported in this work are valid for any reward satisfying the Lipschitzness condition, nothing is specific to imitation. As such, these may be of independent interest.
On Adaptive Attacks Against Jao-Urbanik’s Isogeny-Based Protocol

The k-SIDH protocol is a static-static isogeny-based key agreement protocol. At Mathcrypt 2018, Jao and Urbanik introduced a variant of this protocol which uses non-scalar automorphisms of special elliptic curves to improve its efficiency. In this paper, we provide a new adaptive attack on Jao-Urbanik's protocol. The attack is a non-trivial adaptation of Galbraith-Petit-Shani-Ti's attack on SIDH (Asiacrypt 2016) and its extension to k-SIDH by Dobson-Galbraith-LeGrow-Ti-Zobernig (IACR eprint 2019). Our attack provides a speedup compared to a naïve application of Dobson et al.'s attack to Jao-Urbanik's scheme, exploiting its inherent structure. Estimating the security of k-SIDH and Jao-Urbanik's variant with respect to these attacks, k-SIDH provides better efficiency.
The 'obvious' stuff: exploring the mundane realities of students' digital technology use in school

This paper explores the ways in which students perceive digital technology as being helpful and/or useful to their schooling. Drawing upon survey data from students (n=1174) across three Australian high schools, the paper highlights seventeen distinct digital ‘benefits’ in domains such as information seeking, writing and composition, accessing prescribed work, scheduling and managing study tasks. While these data confirm the centrality of such technologies to students’ experiences of school, they also suggest that digital technology is not substantially changing or ‘transforming’ the nature of schools and schooling per se. Instead, students were most likely to associate digital technologies with managing the logistics of individual study and engaging with school work in distinctly teacher-led linear and passive ways. As such, it is concluded that educationalists need to temper enthusiasms for what might be achieved through digital technologies, and instead develop better understandings of the realities of students’ instrumentally-driven uses of digital technology.
An Interactive Soundscape to Assist Group Walking Mindfulness Meditation

Developing interactive soundscapes for walking meditation has the potential to bring new opportunities of research in human-computer interaction and design. In this paper, we present our case study Sound of Mind, an interactive ambient soundscape that is transformed by one of the participant's brainwave data in a group walking meditation practice.
Variational quantum state preparation via quantum data buses

We propose a variational quantum algorithm to prepare ground states of 1D lattice quantum Hamiltonians specifically tailored for programmable quantum devices where interactions among qubits are mediated by Quantum Data Buses (QDB). For trapped ions with the axial Center-Of-Mass (COM) vibrational mode as single QDB, our scheme uses resonant sideband optical pulses as resource operations, which are potentially faster than off-resonant couplings and thus less prone to decoherence. The disentangling of the QDB from the qubits by the end of the state preparation comes as a byproduct of the variational optimization. We numerically simulate the ground state preparation for the Su-Schrieffer-Heeger model in ions and show that our strategy is scalable while being tolerant to finite temperatures of the COM mode.
Efficient Reversible Data Hiding Simultaneously Exploiting Adjacent Pixels

As an effective means to content authentication and privacy protection, reversible data hiding (RDH) permits us to hide a payload such as authentication data in a media file.The resulting marked content will not introduce noticeable artifacts.In order to achieve superior payload-distortion performance, the conventional RDH algorithms often exploit the smooth content for data embedding.Since RDH allows both the embedded payload and the raw content to be perfectly reconstructed, it is required that, the altered smooth regions within the cover should be identified without error by a data receiver.Therefore, a core work in RDH is to design an efficient content-aware algorithm that can enable a data hider to take advantages of the smooth cover elements as much as possible while the detection procedure for the marked elements should be invertible to a data receiver.This has motivated the authors to present a novel patch-level selection and breadth-first prediction strategy for efficient RDH in this paper.However, different from many conventional RDH works, the proposed approach allows a data hider to preferentially and simultaneously use adjacent smooth elements as many as possible, which can benefit data embedding procedure a lot.Experiments show that our work significantly outperforms a part of advanced RDH algorithms in terms of the payload-distortion performance, which has demonstrated the superiority and applicability.
Stabilizer extent is not multiplicative

The Gottesman-Knill theorem states that a Clifford circuit acting on stabilizer states can be simulated efficiently on a classical computer. Recently, this result has been generalized to cover inputs that are close to a coherent superposition of logarithmically many stabilizer states. The runtime of the classical simulation is governed by the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">stabilizer extent</mml:mtext></mml:mrow></mml:math>, which roughly measures how many stabilizer states are needed to approximate the state. An important open problem is to decide whether the extent is multiplicative under tensor products. An affirmative answer would yield an efficient algorithm for computing the extent of product inputs, while a negative result implies the existence of more efficient classical algorithms for simulating largescale quantum circuits. Here, we answer this question in the negative. Our result follows from very general properties of the set of stabilizer states, such as having a size that scales subexponentially in the dimension, and can thus be readily adapted to similar constructions for other resource theories.
Prioritization of Influence Factors for Selecting E–Learning Systems

COVID-19 pandemic affects not only daily life activities but also traditional education systems. Based on the current developments, to stick by their academic calendars, most of the educational institutions continue their classes via online channels. Since the selection of the most appropriate e–learning platform depends on multi–criteria, the evaluation of this selection process can be dealt with decision support systems. In this study, cognitive mapping extended with intuitionistic fuzzy sets is introduced for prioritizing the e–learning platform selection factors under fuzzy environment based on the multi–expert judgments. Based on the results, infrastructure and ease of use are determined as the most effective factors. For further studies, a sensitivity analysis based on the initial vector determination can be studied to check its effect on the outputs.
On the application of nature-inspired grey wolf optimizer algorithm in geodesy

Abstract Nowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.
Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable. All source code and datasets are available online, at https://github.com/uclnlp/ctp.
Constructing Keyed Hash Algorithm Using Enhanced Chaotic Map with Varying Parameter

A keyed hash algorithm is proposed based on 1-D enhanced quadratic map (EQM) with varying parameter. Three measures, including assigning unique one-time keys, key expansion, and hash length extension, are taken to enhance its security. First, the message is transformed into a parameter sequence for the EQM to be absorbed, and then the extended keys are generated as the initial values of the EQM. Finally, the EQM is iterated with redundant loops to transform the variable values into a hash value. The algorithm is so flexible that it can generate hash value with different lengths of 256, 512, 1024, or more bits through a parameter switcher, and redundant loops can eliminate the transient effect of chaos and mitigate the increasing threat of the side-channel attack. Security evaluations and comparison demonstrated its practicability and reliability.
Best Fit of Mixture For Distributed Poisson Multi-Bernoulli Mixture Filtering

&lt;div&gt;The Poisson multi-Bernoulli mixture (PMBM) filter is extended for distributed implementation using a wireless sensor network. At the core of the proposed networking approach, the PMBM posterior is decomposed into two parts corresponding to the undetected and detected targets, respectively. Fusion is motivated to be performed with regard to the latter only which is represented by MBM based on a distributed flooding algorithm for internode communication, which iteratively shares the MBMs between neighbor sensors. Then, a suboptimal “best-fit-ofmixture” principle is followed at each local sensor to find a MBM that best fits the mixture of MBMs aggregated from distinct sensors, leading to an arithmetic average (AA) of these MBMs. We prove the exact closure of the MBM-AA fusion and discuss its sub-optimality and limitations. Simulation demonstrates the effectiveness and limitations of our approach. &lt;/div&gt;
Explanation-Guided Training for Cross-Domain Few-Shot Classification

Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the predictions of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae. The source code is available at https://github.com/SunJiamei/few-shot-lrp-guided
AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels

Authors of research papers in the fields of mathematics, and other math-heavy disciplines commonly employ the Mathematics Subject Classification (MSC) scheme to search for relevant literature. The MSC is a hierarchical alphanumerical classification scheme that allows librarians to specify one or multiple codes for publications. Digital Libraries in Mathematics, as well as reviewing services, such as zbMATH and Mathematical Reviews (MR) rely on these MSC labels in their workflows to organize the abstracting and reviewing process. Especially, the coarse-grained classification determines the subject editor who is responsible for the actual reviewing process. In this paper, we investigate the feasibility of automatically assigning a coarse-grained primary classification using the MSC scheme, by regarding the problem as a multi class classification machine learning task. We find that the our method achieves an $$F_{1}$$ -score of over 77%, which is remarkably close to the agreement of zbMATH and MR ( $$F_{1}$$ -score of 81%). Moreover, we find that the method’s confidence score allows for reducing the effort by 86% compared to the manual coarse-grained classification effort while maintaining a precision of 81% for automatically classified articles.