Reviewerly/exHarmony huggingface dataset:
Using First-Order Logic to Compress Sentences

Sentence compression is one of the most challenging tasks in natural language processing,which may be of increasing interest to many applicationssuch as abstractive summarization and text simplification for mobile devices.In this paper, we present a novel sentence compression model based on first-order logic, using Markov Logic Network.Sentence compression is formulated as a word/phrase deletion problem in this model.By taking advantage of first-order logic, the proposed method is able to incorporate local linguistic features and to capture global dependencies between word deletion operations. Experiments on both written and spoken corpora show that our approach produces competitive performance against the state-of-the-art methods in terms of manual evaluation measures such as importance, grammaticality, and overall quality.
Predicted Affinity Based Virtual Machine Placement in Cloud Computing Environments

In cloud data centers, an appropriate Virtual Machine (VM) placement has become an effective method to improve the resource utilization and reduce the energy consumption. However, most current solutions regard the VM placement as a bin-packing problem and each VM is seen as a single object. None of them have taken the relationships between VMs into consideration, which supplies us with a kind of new perspective. In this paper, we provide a model which explores the relationships for every two VMs based on the resource requirement provided by ARIMA prediction. This model evaluates the volatility of resource utilization after putting the two VMs on the same host and we call this model as affinity model. Based on the affinity model, VMs will be placed on those hosts that have the highest affinity with them. Therefore, we call it as Predicted Affinity based Virtual Machine Placement Algorithm (PAVMP). The advantages of PAVMP are showed by comparing it with other VM placement algorithms on CloudSim simulation platform with the PlanetLab and Google workload trace.
Underwater Hyperspectral Imaging Using a Stationary Platform in the Trans-Atlantic Geotraverse Hydrothermal Field

Underwater hyperspectral imaging is a relatively new method for characterizing seafloor composition. To date, it has been deployed from moving underwater vehicles, such as remotely operated vehicles and autonomous underwater vehicles. While moving vehicles allow relatively rapid surveying of several 10-1000 m2, they are subjected to short-term variations in vehicle attitude that often compromise image acquisition and quality. In this study, we tested a stationary platform that was landed on the seabed and used an underwater hyperspectral imager (UHI) on a vertical swinging bracket. The imaged seafloor areas have dimensions of 2.3 m × 1 m and are characterized by very stable UHI data of high spatial resolution. The study area was the Trans-Atlantic Geotraverse hydrothermal field at the Mid-Atlantic Ridge (26° N) in water depths of 3530-3660 m. UHI data were acquired a 12 stations on an active and an inactive hydrothermal sulfide mound. Based on supervised classification, 24 spectrally different seafloor materials were detected, including hydrothermal and non-hydrothermal materials, and benthic fauna. The results show that the UHI data are able to spectrally distinguish different types of surface materials and benthic fauna in hydrothermal areas, and may therefore represent a promising tool for high-resolution seafloor exploration in potential future deep-sea mining areas.
A Novel Weight-Shared Multi-Stage CNN for Scale Robustness

Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness to the parallel shift of objects in images as well as their numerous parameters and the resulting high expression ability. However, CNNs have a limited robustness to other geometric transformations such as scaling and rotation. This limits the performance improvement of the deep CNNs, but there is no established solution. This study focuses on scale transformation and proposes a network architecture called the weight-shared multi-stage network (WSMS-Net), which consists of multiple stages of CNNs. The proposed WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet and enables them to acquire robustness to object scaling. Experimental results on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for image classification tasks with only a minor increase in the number of parameters and computation time.
Modeling the Assessment of Definition of a Smart University Infrastructure Development Level

A smart university is an element of the smart education educational environment, a sort of a smart platform or an infrastructure providing implementation, use, and development of the smart technologies in education. The purpose of this research is to develop and test models of defining a smart university infrastructure development level. Within the framework of the study, the authors of this paper considered the smart education development trends, defined the main elements of a smart university infrastructure, and studied the approaches to defining the level of its development based on the data models and the mathematical apparatus of the methods of structural analysis and synthesis, mathematical statistics, the expert assessment of the obtained results. Given the diversity and the amount of quantitative and qualitative indicators of a smart university infrastructure, we propose to measure the infrastructure level using the data models (network, hierarchical, and ER models) and the Educational Data Mining (EDM) tools. Universities can use the obtained models to form the development strategy or to select the funding directions, to prepare for the accreditation process or to level the smart infrastructure of certain subdivisions.
Unsupervised Learning of PCFGs with Normalizing Flow

Unsupervised PCFG inducers hypothesize sets of compact context-free rules as explanations for sentences.These models not only provide tools for low-resource languages, but also play an important role in modeling language acquisition (Bannard et al., 2009;Abend et al., 2017).However, current PCFG induction models, using word tokens as input, are unable to incorporate semantics and morphology into induction, and may encounter issues of sparse vocabulary when facing morphologically rich languages.This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information 1 .Linguistically motivated similarity penalty and categorical distance constraints are imposed on the inducer as regularization.Experiments show that the PCFG induction model with normalizing flow produces grammars with state-of-the-art accuracy on a variety of different languages.Ablation further shows a positive effect of normalizing flow, context embeddings and proposed regularizers.
An Identity Framework for Providing Access to FIWARE OAuth 2.0-Based Services According to the eIDAS European Regulation

Secure electronic identification (eID) is one of the key enablers of data protection, privacy, and the prevention of online fraud.However, until now, the lack of common legal basis prevented European Member States from recognizing and accepting eIDs issued in the other Member States.The electronic identification and trust services (eIDAS) regulation provides a solution to these issues by ensuring the cross-border mutual recognition of eIDs.FIWARE is a European initiative that provides a rather simple yet powerful set of application programming interfaces (APIs) that ease the development of smart applications in multiple vertical sectors and oriented to the future internet.In this paper, we propose a model that enables the connection of FIWARE OAuth 2.0-based services with the eID authentication provided by eIDAS reference.Thanks to this model, services already connected with an OAuth 2.0 identity provider can be automatically connected with eIDAS nodes for providing eID authentication to European citizens.For validating the proposed model, we have deployed an instance of the FIWARE identity manager connected to the Spanish eIDAS node.Then, we have registered two services, a private videoconferencing system, and a public smart city deployment, and extended their functionalities for enriching the user experience leveraging the eID authentication.We have evaluated the integration of both services in the eIDAS network with real users from seven different countries.We conclude that the proposed model facilitates the integration of generic and FIWARE-based OAuth 2.0 services to the eIDAS infrastructure, making the connection transparent for developers.
A Functional Representation for Graph Matching

Graph matching is an important and persistent problem in computer vision and pattern recognition for finding node-to-node correspondence between graphs. However, graph matching that incorporates pairwise constraints can be formulated as a quadratic assignment problem (QAP), which is NP-complete and results in intrinsic computational difficulties. This paper presents a functional representation for graph matching (FRGM) that aims to provide more geometric insights on the problem and reduce the space and time complexities. To achieve these goals, we represent each graph by a linear function space equipped with a functional such as inner product or metric, that has an explicit geometric meaning. Consequently, the correspondence matrix between graphs can be represented as a linear representation map. Furthermore, this map can be reformulated as a new parameterization for matching graphs in Euclidean space such that it is consistent with graphs under rigid or nonrigid deformations. This allows us to estimate the correspondence matrix and geometric deformations simultaneously. We use the representation of edge-attributes rather than the affinity matrix to reduce the space complexity and propose an efficient optimization strategy to reduce the time complexity. The experimental results on both synthetic and real-world datasets show that the FRGM can achieve state-of-the-art performance.
Improved Generalization Bounds for Robust Learning.

We consider a model of robust learning in an adversarial environment. The learner gets uncorrupted training data with access to possible corruptions that may be effected by the adversary during testing. The learner's goal is to build a robust classifier, which will be tested on future adversarial examples. The adversary is limited to $k$ possible corruptions for each input. We model the learner-adversary interaction as a zero-sum game. This model is closely related to the adversarial examples model of Schmidt et al. (2018); Madry et al. (2017). Our main results consist of generalization bounds for the binary and multiclass classification, as well as the real-valued case (regression). For the binary classification setting, we both tighten the generalization bound of Feige, Mansour, and Schapire (2015), and are also able to handle infinite hypothesis classes. The sample complexity is improved from $O(\frac{1}{\epsilon^4}\log(\frac{|\mathcal{H}|}{\delta}))$ to $O\big(\frac{1}{\epsilon^2}(\sqrt{k \mathrm{VC}(\mathcal{H})}\log^{\frac{3}{2}+\alpha}(k\mathrm{VC}(\mathcal{H}))+\log(\frac{1}{\delta})\big)$ for any $\alpha > 0$. Additionally, we extend the algorithm and generalization bound from the binary to the multiclass and real-valued cases. Along the way, we obtain results on fat-shattering dimension and Rademacher complexity of $k$-fold maxima over function classes; these may be of independent interest. For binary classification, the algorithm of Feige et al. (2015) uses a regret minimization algorithm and an ERM oracle as a black box; we adapt it for the multiclass and regression settings. The algorithm provides us with near-optimal policies for the players on a given training sample.
GestureVLAD: Combining Unsupervised Features Representation and Spatio-Temporal Aggregation for Doppler-Radar Gesture Recognition

In this paper we propose a novel framework to process Doppler-radar signals for hand gesture recognition. Doppler-radar sensors provide many advantages over other emerging sensing modalities, including low development costs and high sensitivity to capture subtle gestures with precision. Furthermore, they have attractive properties for ubiquitous deployment and can be conveniently embedded into different devices. In this scope, current recognition methods still rely in deep CNN-LSTM and 3D CNN-LSTM structures that require sufficient labelled data to optimize millions of parameters and significant amount of computational resources for inference; which limits their deployment. Indeed, subtle gestures recognition is a challenging task due to the high variability of gestures among different subjects. To overcome the challenges in the recognition task and the limitations of the current methods, we propose a shallow learning approach for gesture recognition, that is based on unsupervised range-Doppler features representation, along with a learnable pooling aggregation via NetVLAD. The proposed framework can encode extremely valuable information across time, and results in features that are highly discriminative for hand gesture recognition. Experimentation on publicly available Doppler-radar data shows that the proposed framework outperforms state-of-the-art approaches in terms of recognition accuracy and speed for sequence-level hand gesture classification.
Instruction2vec: Efficient Preprocessor of Assembly Code to Detect Software Weakness with CNN

Potential software weakness, which can lead to exploitable security vulnerabilities, continues to pose a risk to computer systems. According to Common Vulnerability and Exposures, 14,714 vulnerabilities were reported in 2017, more than twice the number reported in 2016. Automated vulnerability detection was recommended to efficiently detect vulnerabilities. Among detection techniques, static binary analysis detects software weakness based on existing patterns. In addition, it is based on existing patterns or rules, making it difficult to add and patch new rules whenever an unknown vulnerability is encountered. To overcome this limitation, we propose a new method—Instruction2vec—an improved static binary analysis technique using machine. Our framework consists of two steps: (1) it models assembly code efficiently using Instruction2vec, based on Word2vec; and (2) it learns the features of software weakness code using the feature extraction of Text-CNN without creating patterns or rules and detects new software weakness. We compared the preprocessing performance of three frameworks—Instruction2vec, Word2vec, and Binary2img—to assess the efficiency of Instruction2vec. We used the Juliet Test Suite, particularly the part related to Common Weakness Enumeration(CWE)-121, for training and Securely Taking On New Executable Software of Uncertain Provenance (STONESOUP) for testing. Experimental results show that the proposed scheme can detect software vulnerabilities with an accuracy of 91% of the assembly code.
SpeechBERT: Cross-Modal Pre-trained Language Model for End-to-end Spoken Question Answering.

While end-to-end models for spoken language understanding tasks have beenexplored recently, there is still no end-to-end model for spoken questionanswering (SQA) tasks, which would be catastrophically influenced by speechrecognition errors. Meanwhile, pre-trained language models, such as BERT, haveperformed successfully in text question answering. To bring this advantage ofpre-trained language models into spoken question answering, we proposeSpeechBERT, a cross-modal transformer-based pre-trained language model. As thefirst exploration in end-to-end SQA models, our results matched the performanceof conventional approaches that fed with output text from ASR and only slightlyfell behind pre-trained language models, showing the potential of end-to-endSQA models.
Ontology Extraction for Large Ontologies via Modularity and Forgetting

We are interested in the computation of ontology extracts based on forgetting from large ontologies in real-world scenarios. Such scenarios require nearly all of the terms in the ontology to be forgotten, which poses a significant challenge to forgetting tools. In this paper we show that modularization and forgetting can be combined beneficially in order to compute ontology extracts. While a module is a subset of axioms of a given ontology, the solution of forgetting (also known as a uniform interpolant) is a compact representation of the ontology limited to a subset of the signature. The approach introduced in this paper uses an iterative workflow of four stages: (i)~extension of the given signature and, if needed partitioning, (ii)~modularization, (iii)~forgetting, and (iv)~evaluation by domain expert. For modularization we use three kinds of modules: locality-based, semantic and minimal subsumption modules. For forgetting three tools are used: NUI, LETHE and FAME. An evaluation on the SNOMED CT and NCIt ontologies for standard concept name lists showed that precomputing ontology modules reduces the number of terms that need to be forgotten. An advantage of the presented approach is high precision of the computed ontology extracts.
Trends of Augmented Reality Applications and Research throughout the World: Meta-Analysis of Theses, Articles and Papers between 2001-2019 Years

Our aim in this research was to analyze studies in the area of Augmented Reality applications and research throughout the world using meta-analysis methods in order to determine trends in the area. For the purpose of the study, a total of 1008 pieces of research, published between 2001 and 2019 and selected by purposeful sampling method were analyzed. Trends of Augmented Reality applications and research throughout the world were examined under 13 criteria. These criteria were; index, year of publication, number of authors, country of research, area of research, method, education grade, sample group, sample number, data collection method, bibliography number, analysis tech-niques, purpose of research, and research trends. These data were interpreted based on percentage and frequency. In Augmented Reality, technologies are integrated in many fields such as education technology, engineering arts, visual arts education and special education.
Health Professional and Workers Attitudes Towards the Use of Social Robots for Older Adults in Long-Term Care

The aim of this study was (a) to modify, translate and validate the Chinese version of attitudes towards the use of social robot (ATTUSR-C) questionnaire for use with Taiwanese health personnel; and (b) investigate the attitudes of Taiwanese health personnel in long-term care towards the use of social robots for older adults. The attitudes of health personnel towards social robots can affect the acceptability of social robots for older adults. An investigation of health personnel’s ATTUSRs and the development of a validated Chinese questionnaire is needed. A cross-sectional design was used to conduct this multi-phase study. Data collection was from November 2017 to May 2018. Content validity, internal consistency reliability, and factor analysis of the ATTUSR-C questionnaire were evaluated. Purposive sampling was used. All recruited participants received an email containing study information and a URL link to the survey. The ATTUSR-C questionnaire had good validity and reliability. A total of 416 health professionals responded to the online survey. Most health personnel had positive ATTUSRs in long-term care facilities as they viewed social robots as beneficial and practical in psychosocial care for older adults. Positive ATTUSRs can increase acceptance and utilisation of social robots. This study strives to support nursing work by providing insights into health personnel’s perceptions of social robots, in order to integrate social robots into the care and lives of older adults.
A Theory of Usable Information under Computational Constraints

We propose a new framework for reasoning about information in complex systems. Our foundation is based on a variational extension of Shannon’s information theory that takes into account the modeling power and computational constraints of the observer. The resulting predictive F-information encompasses mutual information and other notions of informativeness such as the coefficient of determination. Unlike Shannon’s mutual information and in violation of the data processing inequality, F-information can be created through computation. This is consistent with deep neural networks extracting hierarchies of progressively more informative features in representation learning. Additionally, we show that by incorporating computational constraints, F-information can be reliably estimated from data even in high dimensions with PAC-style guarantees. Empirically, we demonstrate predictive F-information is more effective than mutual information for structure learning and fair representation learning.
Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.
General bounded corner states in the two-dimensional Su-Schrieffer-Heeger model with intracellular next-nearest-neighbor hopping

We investigate corner states in a photonic two-dimensional (2D) Su-Schrieffer-Heeger (SSH) model on a square lattice with zero gauge flux. By considering intracelluar next-nearest-neighbor (NNN) hoppings, we discover a broad class of corner states in the 2D SSH model and show that they are robust against certain fabrication disorders. Moreover, these corner states are located around the corners but not at the corner points. We analytically identify that these corner states are induced by the intracelluar NNN hoppings (long-range interactions) and split off from the edge-state bands. Thus, we refer to them as general bounded corner states. Our paper shows a simple way to induce unique corner states by the long-range interactions and offers opportunities for designing novel photonic devices.