Reviewerly/exHarmony huggingface dataset:
An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing

In this paper, we investigate the aspect of structured output modeling for the state-ofthe-art graph-based neural dependency parser (Dozat and Manning, 2017).With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match.However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.
Knowledge Graph Embedding for Ecotoxicological Effect Prediction

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this paper we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignment techniques. Our experimental results show that the knowledge graph based approach improves the selected baselines.
Potential of VIS-NIR spectroscopy to characterize and discriminate topsoils of different soil types in the Triffa plain (Morocco)

Abstract This study aims to identify the influence of soil organic matter (OM) content and calcium carbonates (CaCO 3 ) on soil reflectance and select the optimum spectral bands for discriminating between topsoils of different soil types situated in the irrigated perimeter of the Triffa plain (Morocco) using VIS-NIR reflectance spectroscopy. Soil samples were collected from the plow layer in 26 sampling sites. The spectral measurements were conducted in the field using an ASD Fieldspec portable spectroradiometer (350–2500 nm), while the soil samples were analyzed in the laboratory. The spectral data were pre-processed to remove the noise effects and then analyzed with the CovSel (selected covariance) method, validated by linear discriminant analysis in order to select the most optimal spectral variables to discriminate between topsoils of different soil types in the plain. The results of the soils reflectance curves showed that low reflectance intensity marked the soils with high OM contents throughout the VIS spectrum. The influence of the soil OM content was very apparent in the VIS range (between 580–750 nm). Regarding the CaCO 3 content, it was noted that the soil samples with a high percentage of CaCO 3 increase the reflectance in all spectral domains situated between 350 and 2500 nm. The spectral bands of 1999, 686, 1280, 2340 and 1951 nm were the most optimal for the soil discrimination in the Triffa plain. This study concluded that the VIS-NIR spectroscopy demonstrates an excellent ability to characterize and discriminate between topsoils in the Triffa plain.
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

In this paper, a deep mixture of diverse experts algorithm is developed to achieve more efficient learning of a huge (mixture) network for large-scale visual recognition application. First, a two-layer ontology is constructed to assign large numbers of atomic object classes into a set of task groups according to the similarities of their learning complexities, where certain degrees of inter-group task overlapping are allowed to enable sufficient inter-group message passing. Second, one particular base deep CNNs with <inline-formula><tex-math notation="LaTeX">$M+1$</tex-math></inline-formula> outputs is learned for each task group to recognize its <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> atomic object classes and identify one special class of “not-in-group”, where the network structure (numbers of layers and units in each layer) of the well-designed deep CNNs (such as AlexNet, VGG, GoogleNet, ResNet) is directly used to configure such base deep CNNs. For enhancing the separability of the atomic object classes in the same task group, two approaches are developed to learn more discriminative base deep CNNs: (a) our deep multi-task learning algorithm that can effectively exploit the inter-class visual similarities; (b) our two-layer network cascade approach that can improve the accuracy rates for the hard object classes at certain degrees while effectively maintaining the high accuracy rates for the easy ones. Finally, all these complementary base deep CNNs with diverse but overlapped outputs are seamlessly combined to generate a mixture network with larger outputs for recognizing tens of thousands of atomic object classes. Our experimental results have demonstrated that our deep mixture of diverse experts algorithm can achieve very competitive results on large-scale visual recognition.
Oblivious resampling oracles and parallel algorithms for the lopsided lovász local lemma

The Lovasz Local Lemma (LLL) is a probabilistic tool which shows that, if a collection of bad events B in a probability space are not too likely and not too interdependent, then there is a positive probability that no bad-events in B occur. Moser & Tardos (2010) gave sequential and parallel algorithms which transformed most applications of the variable-assignment LLL into efficient algorithms. A framework of Harvey & Vondrak (2015) based on extended this give very general sequential algorithms for other probability spaces satisfying the Lopsided Lovasz Local Lemma (LLLL).We describe a new structural property of resampling oracles which holds for all known resampling oracles, which we call obliviousness. Essentially, it means that the interaction between two bad-events B, B′ depends only on the randomness used to resample B, and not on the precise state within B itself.This property has two major consequences. First, it is the key to achieving a unified parallel LLLL algorithm, which is faster than previous, problem-specific algorithms of Harris (2016) for the variable-assignment LLLL algorithm and of Harris & Srinivasan (2014) for permutations. This new algorithm extends a framework of Kolmogorov (2016), and gives the first RNC algorithms for rainbow perfect matchings and rainbow hamiltonian cycles of Kn.Second, this property allows us to build LLLL probability spaces out of a relatively simple set of events. It was intuitively clear that existing LLLL spaces were built in this way; but the obliviousness property formalizes this and gives a way of automatically turning a resampling oracle for atomic events into a resampling oracle for conjunctions of them. Using this framework, we get the first sequential resampling oracle for rainbow perfect matchings on the complete s-uniform hypergraph Kn(s), and the first commutative resampling oracle for hamiltonian cycles of Kn.
Revisiting Controlled Query Evaluation in Description Logics

Controlled Query Evaluation (CQE) is a confidentiality-preserving framework in which private information is protected through a policy, and a (optimal) censor guarantees that answers to queries are maximized without violating the policy. CQE has been recently studied in the context of ontologies, where the focus has been mainly on the problem of the existence of an optimal censor. In this paper we instead consider query answering over all possible optimal censors. We study data complexity of this problem for ontologies specified in the Description Logics DL-LiteR and EL_bottom and for variants of the censor language, which is the language used by the censor to enforce the policy. In our investigation we also analyze the relationship between CQE and the problem of Consistent Query Answering (CQA). Some of the complexity results we provide are indeed obtained through mutual reduction between CQE and CQA.
Expressive markers in online teenage talk

This paper discusses the expression of emotional involvement in informal computer-mediated communication (CMC). While related research is quite fragmentary through its exclusive focus on a limited number of expressive markers or the inclusion of just one independent variable, the present study includes a wide range of expressive markers and three independent vari­ables. The data reveal strikingly consistent age and gender correlates across all expressive markers and a strong correlation between the preferences of younger adolescents and girls. Furthermore, the study highlights a major impact of medium type. It calls for a refinement of the operationalization of the variable medium, as apart from its inherent characteristics (private/public, synchronous/asynchronous), the nature and goal of the interaction (which is also partly related to the type of social media that people use) trigger specific linguistic practices.
A self-attentive model for knowledge tracing

Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are \textit{relevant} to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average.
Industrial Variant Management with pure::variants

The paper describes a demonstration of pure::variants, a commercial tool for variant and variability management for product lines. The demonstration shows how flexible product line (PL) architectures can be built, tested and maintained by using the modeling and integration capabilities provided by pure::variants. With pure::variants being available for a long time, the demonstration (and the paper) combines both basics of pure::variants, known to parts of the audience, and new capabilities, introduced within the last year.
Cross-Project Defect Prediction via Transferable Deep Learning-Generated and Handcrafted Features

Although the machine learning-based software defect prediction (SDP) method has shown promising value in software engineering, yet challenges remain.To improve the performance of SDP, some researchers have used deep learning algorithms to extract the semantic and structural features of the program.However, in more practical cross-project defect prediction (CPDP) tasks, whether deep learning-generated features can be directly used should be explored due to the data distribution shift that usually exists in different projects.In this paper, we propose a Transferable Hybrid Features Learning with Convolutional Neural Network (CNN-THFL) framework to conduct CPDP.Specially, CNN-THFL mines deep learning-generated features from token vectors extracted from programs' abstract syntax trees via convolutional neural network.Furthermore, CNN-THFL learns the transferable joint features simultaneously considering deep learning-generated and handcrafted features by applying a transfer component analysis algorithm.Finally, the features generated by CNN-THFL are fed to the classifier to train a defect prediction model.Extensive experiments verify that CNN-THFL can outperform referential methods on 72 pairs of CPDP tasks formed by 9 open-source projects.
Heterogeneous Graph Learning for Visual Commonsense Reasoning

Visual commonsense reasoning task aims at leading the research field into solving cognition-level reasoning with the ability to predict correct answers and meanwhile providing convincing reasoning paths, resulting in three sub-tasks i.e., Q->A, QA->R and Q->AR. It poses great challenges over the proper semantic alignment between vision and linguistic domains and knowledge reasoning to generate persuasive reasoning paths. Existing works either resort to a powerful end-to-end network that cannot produce interpretable reasoning paths or solely explore intra-relationship of visual objects (homogeneous graph) while ignoring the cross-domain semantic alignment among visual concepts and linguistic words. In this paper, we propose a new Heterogeneous Graph Learning (HGL) framework for seamlessly integrating the intra-graph and inter-graph reasoning in order to bridge the vision and language domain. Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement. Moreover, our HGL integrates a contextual voting module to exploit a long-range visual context for better global reasoning. Experiments on the large-scale Visual Commonsense Reasoning benchmark demonstrate the superior performance of our proposed modules on three tasks (improving 5% accuracy on Q->A, 3.5% on QA->R, 5.8% on Q->AR).
Disentangling Influence: Using disentangled representations to audit model predictions

Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can be direct (a direct influence on model outcomes) and indirect (model outcomes are influenced via proxy features). Feature influence can also be expressed in aggregate over the training or test data or locally with respect to a single point. Current research has typically focused on one of each of these dimensions. In this paper, we develop disentangled influence audits, a procedure to audit the indirect influence of features. Specifically, we show that disentangled representations provide a mechanism to identify proxy features in the dataset, while allowing an explicit computation of feature influence on either individual outcomes or aggregate-level outcomes. We show through both theory and experiments that disentangled influence audits can both detect proxy features and show, for each individual or in aggregate, which of these proxy features affects the classifier being audited the most. In this respect, our method is more powerful than existing methods for ascertaining feature influence.
Rethinking Data Augmentation: Self-Supervision and Self-Distillation

Data augmentation techniques, e.g., flipping or cropping, whichsystematically enlarge the training dataset by explicitly generating moretraining samples, are effective in improving the generalization performance ofdeep neural networks. In the supervised setting, a common practice for dataaugmentation is to assign the same label to all augmented samples of the samesource. However, if the augmentation results in large distributionaldiscrepancy among them (e.g., rotations), forcing their label invariance may betoo difficult to solve and often hurts the performance. To tackle thischallenge, we suggest a simple yet effective idea of learning the jointdistribution of the original and self-supervised labels of augmented samples.The joint learning framework is easier to train, and enables an aggregatedinference combining the predictions from different augmented samples forimproving the performance. Further, to speed up the aggregation process, wealso propose a knowledge transfer technique, self-distillation, which transfersthe knowledge of augmentation into the model itself. We demonstrate theeffectiveness of our data augmentation framework on various fully-supervisedsettings including the few-shot and imbalanced classification scenarios.
Cryptanalysis and Improvement of a Chaotic Map-Control-Based and the Plain Image-Related Cryptosystem

Due to the characteristics of chaotic systems, different cryptosystems based on chaos have been proposed to satisfy the security of multimedia data. A plain image-related chaotic algorithm is proposed by Luo et al. with high speed and efficiency. Security weaknesses of the cryptosystem are studied in this paper. It is found that the important secret key information is leaked because an important parameter can be obtained after an inverse operation in the last step of the cryptosystems without secret key. Meanwhile, the value zero is processed improperly in quantification algorithm. Based on the weaknesses, chosen plaintext attack on the cryptosystem is proposed, by which, an important parameter, equivalent to secret key, can be calculated with a specific chosen plain image. With the obtained parameter, the plain image of any ciphered image, encrypted by the cryptosystem, can be recovered. Then, an improvement is proposed to solve the problems after modifying the quantification algorithm. It is from the experiments that chosen plaintext attack is valid and improved algorithm possesses better performance.
Collaborative query reformulation in tourism information search

Purpose Collaborative information search (CIS) is a growing and significant research area. Query formulation and reformulation is an important search strategy in information search. However, limited research has investigated query behavior during CIS. The purpose of this paper is to characterize collaborative query reformulation (CQR) by exploring the sources of collaborative query (CQ) terms and the types and patterns of CQR in the context of tourism information search. Design/methodology/approach An empirical study was designed to investigate search query reformulation as tourists performed CIS on a devised interface. A total of 36 participants (in 18 pairs) took part in the study; data were documented in pre- and post-search questionnaires, search logs and chat logs. Findings The findings show that participants intermixed individual search and collaborative search during CIS. Participants constructed CQ terms mainly by selecting terms from individual search queries and discussion chat logs. Eight types of CQR were identified, with specialization (82 percent) accounting for the most used search tactics. At most times, participants were found to add terms to the previous query. Findings demonstrated 27 specific CQR patterns; in excess of two-third participants (69 percent) took only one move to reformulate CQ by adding terms, or replacing/using new words. Practical implications The results of this research can be used to inform the design of search systems supporting collaborative querying in CIS. Originality/value This study is highlighting an important research direction of CQ reformulation in collaborative search while previous studies of the topic are limited, comparing to the vast body of work on query reformulation in individual information search using regular search systems.
System F in Agda, for Fun and Profit

System F, also known as the polymorphic $$\lambda $$ -calculus, is a typed $$\lambda $$ -calculus independently discovered by the logician Jean-Yves Girard and the computer scientist John Reynolds. We consider $$F_{\omega \mu }$$ , which adds higher-order kinds and iso-recursive types. We present the first complete, intrinsically typed, executable, formalisation of System $$F_{\omega \mu }$$ that we are aware of. The work is motivated by verifying the core language of a smart contract system based on System $$F_{\omega \mu }$$ . The paper is a literate Agda script [14].
'Is the Time Right Now?'

Temporal coordination endures as a central topic in computer-supported cooperative work (CSCW) because information systems still struggle to adequately support varying representations of time in the context of collaborations that areboth temporally and geographically dispersed. Moreover, the adaptive practices of these broadly dispersed groups are still not well understood. We ask:How do globally distributed teams temporally coordinate to accomplish their work? We examine an extreme case of online temporal coordination: high-tempo information curation about the urgent humanitarian crisis following the 2017 Hurricane Maria landfall in Puerto Rico. Our analysis of synchronous chat transcripts and data artifacts produced by The Standby Task Force reveals how this digital humanitarian group establishes temporal coordination through different shared understandings of time relative to the crisis, the globally distributed work, and the collaborative information technologies. We make four contributions from our analysis: First, we developed a pluritemporal analytical framework that describes different forms of socially constructed time and disambiguates their meaning in talk. Next, we present empirical evidence of how this distributed team establishes shared temporal orders to collectively orient their work in time. Then, we describe how they reconcile multiple and conflicting instances of sociotemporal "disorder'' to enable productive work. Last, we reflect on the design implications for collaborative information systems informed by this work.
Generation and characterization of position-momentum entangled photon pairs in a hot atomic gas cell

Continuous-variable position-momentum entanglement (or Einstein-Podolsky-Rosen entanglement) of two particles has played important roles in the fundamental study of quantum physics as well as in the progress of quantum information. In this paper, we propose a scheme to generate Einstein-Podolsky-Rosen (EPR) position-momentum entangled photon pairs efficiently via spontaneous four-wave mixing (SFWM) process in a hot rubidium gas cell. The EPR entanglement between the photon pair is measured and characterized by using the ghost interference and the ghost imaging method. Due to the simplicity of the experimental setup and the high photon pair generation rate, our EPR entangled photon source may has potential applications in quantum imaging, hyperentanglement preparation and atomic ensemble based quantum information processing and quantum communication protocols.
Experiencing Waiting Time in Virtual Reality

This article investigates the impact of waiting in Virtual Reality (VR) on the perception of time. We manipulated the visual quality of a virtual room replicating a real one (360-picture vs. 3D-model) with and without avatar embodiment (no-avatar vs. avatar). We only observed a significant difference in the estimated time duration between the real and the virtual worlds when using no avatar within a 3D model of the room. Our early results suggest that a VR environment with an avatar and a simple 3D model or 360 picture room is not significantly perturbing time perception and thus could be used for diagnosis and therapy of psychiatric conditions related to altered time perception.