Reviewerly/exHarmony huggingface dataset:
Kernel-Based Mixture Mapping for Image and Text Association

Modeling the relationship between multimodal media, including images, videos, and text, can reduce the gap between the modalities and promote cross-media retrieval, image annotation, etc. In this paper, we propose a new approach called kernel-based mixture mapping (KMM) to model the semantic correlations between web images and text. With this approach, we first construct latent high-dimensional feature spaces based on kernel theory to address the nonlinearity of both the data distributions in the input spaces and the cross-model correlation. Second, we present a probabilistic neighborhood model to describe the spatial locality of semantics by assuming that proximate examples in feature spaces generally have the same semantics and a conditional model to describe cross-modal conditional dependency. Finally, we build a probabilistic mixture model to jointly model the spatial locality of semantics and the conditional dependency between different modalities. By combining nonlinear transformation and probabilistic models, KMM can address the nonlinearity of cross-modal correlation, the complexity of semantic distributions at the global scale, and the continuity of semantic distributions at the local scale. We present a hybrid optimization algorithm to find the solution of KMM based on expectation-maximization and subgradient ascent; this algorithm avoids estimating the parameters of KMM in high-dimensional feature space and is proved to converge to an (local) optimal solution. We demonstrate the performance of KMM using four public datasets. The experimental results show that our approach outperforms the compared methods when modeling the relationships between images and text.
Modeling reverse thinking for machine learning

Human inertial thinking schemes can be formed through learning, which are then applied to quickly solve similar problems later. However, when problems are significantly different, inertial thinking generally presents the solutions that are definitely imperfect. In such cases, people will apply creative thinking, such as reverse thinking, to solve problems. Similarly, machine learning methods also form inertial thinking schemes through learning the knowledge from a large amount of data. However, when the testing samples are vastly different, the formed inertial thinking schemes will inevitably generate errors. This kind of inertial thinking is called illusion inertial thinking. Because all machine learning methods do not consider the illusion inertial thinking, in this paper we propose a new method that uses the reverse thinking to correct the illusion inertial thinking, which increases the generalization ability of machine learning methods. Experimental results on benchmark data sets validated the proposed method.
Scalable approximations for generalized linear problems

In stochastic optimization, the population risk is generally approximated by the empirical risk which is in turn minimized by an iterative algorithm. However, in the large-scale setting, empirical ...
Deliberation Learning for Image-to-Image Translation

Image-to-image translation, which transfers an image from a source domain to a target one, has attracted much attention in both academia and industry. The major approach is to adopt an encoder-decoder based framework, where the encoder extracts features from the input image and then the decoder decodes the features and generates an image in the target domain as the output. In this paper, we go beyond this learning framework by considering an additional polishing step on the output image. Polishing an image is very common in human's daily life, such as editing and beautifying a photo in Photoshop after taking/generating it by a digital camera. Such a deliberation process is shown to be very helpful and important in practice and thus we believe it will also be helpful for image translation. Inspired by the success of deliberation network in natural language processing, we extend deliberation process to the field of image translation. We verify our proposed method on four two-domain translation tasks and one multi-domain translation task. Both the qualitative and quantitative results demonstrate the effectiveness of our method.
Foreword to the Special Section on the International Conference on Artificial Reality and Telexistence &amp; Eurographics Symposium on Virtual Environments (ICAT-EGVE 2018)

• Importance of embodiment of virtual agents for exhibition guides. • Content-related representations to improve user experience in museum exhibits. • Importance of plausible physical-virtual airflow interaction in mixed reality. • Environment-aware behavior for mixed reality agents to improve social presence.
The national solar radiation data base (NSRDB) for CSP applications

This study examines the differences in direct normal irradiance (DNI) in two versions of the National Solar Radiation Database (NSRDB) produced by the National Renewable Energy Laboratory (NREL). NSRDB V3 of the NSRDB includes significant changes to various parts of the radiative transfer model and inputs to the model compared to NSRDB V2. The changes in NSRDB V3 resulted in lower uncertainty than NSRDB V2. The study quantified the uncertainty and the spatial and temporal variability under clear-sky conditions. The uncertainty estimation was performed using a standardized method, The Guide to the Expression of Uncertainty in Measurement (GUM). The evaluation of the accuracy of the NSRDB was conducted using high-quality ground-measurements for seven National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) stations for 1998–2015.
APA: Adaptive Pose Alignment for Robust Face Recognition

In this paper, we propose a new face alignment method, called adaptive pose alignment (APA) which can greatly reduce the intra-class difference and correct the noise caused by the traditional method in the alignment process, especially in unconstrained settings. Instead of aligning all faces to the pre-defined, uniform frontal shape, we adaptively learn the alignment templates according to the facial poses and then align each face of training or testing sets to its related template. To further improve the face recognition performance, we propose a simple, yet effective feature normalization method which can generate more discriminative feature representation of a face or template combined with the APA method. Furthermore, we introduce a poseinvariant face recognition pipeline that sequentially applies APA based alignment, deep representation by Softmax or Arcface, and the effective feature normalization procedure. We empirically show that APA based images can accelerate the training of deep face recognition model by aligning all the images to the optimal templates. Moreover, experiments show that the proposed method achieves the state-of-theart performance on challenging IJB-A, IJB-C and CPLFW datasets.
The FWF’s Open Access Policy over the last 15 Years – Developments and Outlook

The report outlines the developments of the open access (OA) policy of the Austrian Science Fund (FWF) since its first release in 2004. Over the last 15 years, the FWF has not only continuously updated its OA policy but has also adapted its support mechanisms to include dedicated funding programmes and financial support structures for alternative publication venues and infrastructures. A brief analysis of the FWF’s open access funding will be provided in the second part of the article, which will conclude with an outlook of the upcoming revisions to the OA policy.
Manipulation through Online Sexual Behavior: Exemplifying the Importance of Human Factor in Intelligence and Counterintelligence in the Big Data Era

As we spend more and more time online, Internet-based virtual spaces are becoming a central component of our daily life and activities. This shift of human activities from offline to online spaces has major impacts for national security. Consequently, cyberspace became a new field of operation for intelligence and counter-intelligence services worldwide. While massive efforts are made to further strategies based on surveys and analyses of large datasets, cybersecurity protocols can be impacted tremendously by individual behaviors. This is particularly the case of online sexual behavior, which can be easily manipulated by malevolent agents. This paper will describe some of the general characteristics of sexual cyberbehaviors. We will then identify some of the main threats related to sexual cyberbehavior (specifically risks of blackmailing, risks associated with the use of online dating sites, and risks associated with the consumption of online pornography), as well as the main targets in terms of population from an intelligence/counter-intelligence perspective. Finally, we will propose some possible counter-measures, that could be implemented to reduce the security risks related to online sexual behavior.
Modelling vision angles of optical camera zoom using image processing algorithm

Abstract A challenge of adding a new feature to the optical properties of a camera is solved in this paper. Image processing gives the possibility to enhance the quality, resolution and details of images fast these days. Within this paper, the introduced algorithm used to determine the vision angles (horizontal and vertical) of a digital camera (Nikon). The quality and efficiency of the camera are evaluated using determine the spatial resolution of the captured images. A scale factor algorithm is used also in this paper. A mathematical model introduced to describe the camera angle vision. This model works with different zoom degrees and different camera-object distances. Thus, a general mathematical equation obtained which describes the real image. The comparisons between the real and resulted images give a strong matching with small error rate.
SVM and MLP Based Segmentation and Recognition of Text from Scene Images Through an Effective Binarization Scheme

Text Binarization from scene images plays a crucial taskGhoshal, Ranjit for any textBanerjee, Ayan segmentation scheme and therefore in the OCR performance. So, an effective image binarization method is required for text segmentation and recognition tasks. This work describes an effective image binarization scheme for segmentation and recognition task of text from images. To binarize the image, Canny’s edge information is incorporated into Otsu’s method. It generates numerous components which are analyzed for segmentation of probable text components. Further, a few features are considered for classification of text and non-text. For this problem, SVM is considered. For training SVM classifier, information from ground-truth images of text and our own made non-text components are used. Finally, Multilayer Perceptron (MLP) is used for recognition of the text symbols. The MLP classifier is trained using 13496 samples of 39 classes. We tested our schemes on the publicly available ICDAR Born Digital data set. The outcomes are quite acceptable.
Efficient Generation of Motion Plans from Attribute-Based Natural Language Instructions Using Dynamic Constraint Mapping

We present an algorithm for combining natural language processing (NLP) and fast robot motion planning to automatically generate robot movements. Our formulation uses a novel concept called Dynamic Constraint Mapping to transform complex, attribute-based natural language instructions into appropriate cost functions and parametric constraints for optimization-based motion planning. We generate a factor graph from natural language instructions called the Dynamic Grounding Graph (DGG), which takes latent parameters into account. The coefficients of this factor graph are learned based on conditional random fields (CRFs) and are used to dynamically generate the constraints for motion planning. We map the cost function directly to the motion parameters of the planner and compute smooth trajectories in dynamic scenes. We highlight the performance of our approach in a simulated environment and via a human interacting with a 7-DOF Fetch robot using intricate language commands including negation, orientation specification, and distance constraints.
A performance evaluation for Internet of Things based on Blockchain technology

In this paper, the performance evaluation for Internet of Things based on blockchain Technology is proposed. In a blockchain network, all IoT devices communicate and synchronize with each other and allow device to transact with each other directly without the need of trusted intermediary. IoT devices have a smart contract associate with them which are deployed on the blockchain network. User or device who wish to use a service can transact to smart contract that associated with IoT device. Moreover, all transactions of IoT device, device-to device or human-to-device, will not only permanently store on Blockchain network but also gain the security and privacy of them. In this implementation, a private Ethereum network is chosen to use as a blockchain platform and the raspberry pi act as a lightweight node to transact with a smart contract in a full node which install on PC desktop. In addition, the performance evaluation of two realistic traffic flow, store and access transactions, such as latency, execution time and throughput are highlight.
Realistic Cybersecurity Training via Scenario Progression Management

Cybersecurity training activities are being conducted worldwide in order to address the increase in security threats that is plaguing our network-centric society. However, most of these activities take place in restricted environments, such as military organizations, or require paying high training fees. In this paper we present an open-source architecture that makes it possible to easily conduct cybersecurity training, thus lowering the barrier to entry for a large number of students and IT professionals to benefit from such activities. The core element of our architecture is a set of mechanisms for automating the scenario progression management, thus enabling activities that combine attack, forensic and defense training in realistic scenarios. We present the design and implementation of the progression management module, and its interactions with the overall training framework into which it was integrated. Our evaluation of the architecture from both functionality and performance perspectives demonstrates that it meets the requirements for realistic training activities, and that progression management introduces only a low execution overhead.
Exploring convolutional auto-encoders for representation learning on networks

A multitude of important real-world or synthetic systems possess network structure. Extending learning techniques such as neural networks to process such non-euclidean data is therefore an important direction for machine learning research. However, till very recently this domain has received comparatively low levels of attention. There is no straight forward application of machine learning to network data as machine learning tools are designed for $i.i.d$ data, simple euclidean data or grids. To address this challenge the technical focus of this dissertation is on use of graph neural networks for Network Representation Learning (NRL) i.e. learning vector representations of nodes in networks. Learning vector embeddings of graph-structured data is similar to embedding complex data into low-dimensional geometries. After the embedding process is completed, drawbacks associated with graph structured data are overcome. The current inquiry proposes two deep learning auto-encoder based approaches for generating node embeddings. The drawbacks in existing auto-encoder approaches such as shallow architectures and excessive parameters are tackled in the proposed architectures using fully convolutional layers. Extensive experiments are performed on publicly available benchmark network data-sets to highlight the validity of this approach.
Predicting Scientific Impact via Heterogeneous Academic Network Embedding

Predicting the scientific impact of literatures and researchers has been widely studied for a long time, and prevailing graph-based methods mainly rely on the global structure of the academic network. However, systematically integrating the local structural information of academic network and the text information of articles into a unified model to jointly predict the future impact of published articles and future potential of researchers is relatively unexplored. In this paper, we focus on how to effectively leverage these aforementioned information to predict the future impact of articles and researchers. Specifically, we first design a novel network embedding model which can simultaneously learn the local structural information of heterogeneous academic network and the text information of articles into a low-dimensional vector. Then a multivariate random-walk model is proposed to mutually rank the future impact of articles and researchers by leveraging such learned embeddings. We conduct extensive experiments on the AMiner dataset and the ACL Anthology Network, and the results demonstrate that the proposed method significantly outperforms existing state-of-the-art ranking approaches.
A Better Understanding of the Interaction Between Users and Items by Knowledge Graph Learning for Temporal Recommendation

Recently the knowledge graph (KG) as extra auxiliary information is widely used to improve recommendation. Existing methods usually treat knowledge representation as characteristic information for addressing data sparsity and cold start issues. However, they ignore the implicit and explicit interaction between users and items, which may be gained by the relation extraction and knowledge reasoning, to lead to suboptimal performance. Thus, we believe that it is crucial to incorporate both relations and attributes of users and items into recommender system. That can better capture the extent that a user prefer to an item. In this paper, we propose a novel knowledge graph-based temporal recommendation (KGTR) model. Firstly, we design a lightweight KG on the basis of a single independent domains knowledge without extra supplement. We define three relationships to express interactions within/between users and items, including the interaction of a user browsing an item, the social relation of two users browsing one item, and the behavior of a user browsing items in the meantime. Different from previous knowledge translation-based recommendation methods, we embed interactions by adding them to the transformation from one entity to another in KG. Extensive experiments on real world dataset show that our KGTR outperforms several state-of-the-art recommendation methods.
Certification of Nonclausal Connection Tableaux Proofs

Nonclausal connection tableaux calculi enable proof search without performing clausification. We give a translation of nonclausal connection proofs to Gentzen’s sequent calculus LK and compare it to an existing translation of clausal connection proofs. Furthermore, we implement the translation in the interactive theorem prover HOL Light, enabling certification of nonclausal connection proofs as well as a new, complementary automation technique in HOL Light.
A Formally Verified Abstract Account of Gödel’s Incompleteness Theorems

We present an abstract development of Gödel’s incompleteness theorems, performed with the help of the Isabelle/HOL theorem prover. We analyze sufficient conditions for the theorems’ applicability to a partially specified logic. In addition to the usual benefits of generality, our abstract perspective enables a comparison between alternative approaches from the literature. These include Rosser’s variation of the first theorem, Jeroslow’s variation of the second theorem, and the Świerczkowski–Paulson semantics-based approach. As part of our framework’s validation, we upgrade Paulson’s Isabelle proof to produce a mechanization of the second theorem that does not assume soundness in the standard model, and in fact does not rely on any notion of model or semantic interpretation.
Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference

Natural Language inference is the task of identifying relation between two sentences as entailment, contradiction or neutrality.MedNLI is a biomedical flavour of NLI for clinical domain.This paper explores the use of Bidirectional Encoder Representation from Transformer (BERT) for solving MedNLI.The proposed model, BERT pre-trained on PMC, PubMed and fine-tuned on MIMIC-III v1.4,achieves state of the art results on MedNLI (83.45%) and an accuracy of 78.5% in MEDIQA challenge.The authors present an analysis of the attention patterns that emerged as a result of training BERT on MedNLI using a visualization tool, bertviz.
Cognitive Developing of Semiotic Data in Computer-Based Communication (Signs, Concepts, Discourse)

The paper considers the processes of semiotic data’s cognitive development which take place in computer-based communication. The immersion of signs-terminatives and different concepts into the computer discourse is observed. The research had been intended to understand the lingua-semiotic structure of the Semantic Web as well as to get aware of what concepts make the ground of this Web’s architecture. Another goal of the research conducted was to clear up the semiotic reasons of the inefficiency of computer-based communication together with forwarding the idea of mind-mapping as means of semiotic structuring the cognition of any type of data involved into computer discourse. The paper also suggests the creation of the semiotic model of interrelations of concepts, information objects and computer codes for easier cognitive mastering of semiotic data in the course of computer communication.
Semi-flat minima and saddle points by embedding neural networks to overparameterization

We theoretically study the landscape of the training error for neural networks in overparameterized cases. We consider three basic methods for embedding a network into a wider one with more hidden units, and discuss whether a minimum point of the narrower network gives a minimum or saddle point of the wider one. Our results show that the networks with smooth and ReLU activation have different partially flat landscapes around the embedded point. We also relate these results to a difference of their generalization abilities in overparameterized realization.
Named Entity Recognition in Portuguese Neurology Text Using CRF

Automatic recognition of named entities from clinical text lightens the work of health professionals by helping in the interpretation and easing tasks such as the population of databases with patient health information. In this study, we evaluated the performance of Conditional Random Fields, a sequence labelling model, for extracting entities from neurology clinical texts written in Portuguese. More than achieving F1-scores of about 73% or 80%, respectively for a relaxed or strict evaluation, the more discriminant features in this task were also analyzed.