Reviewerly/exHarmony huggingface dataset:
Concurrence of multiqubit bound entangled states constructed from unextendible product bases

The multiqubit bound entangled states that are uniformly mixed over the subspaces complementary to unextendible product bases (UPBs) are interesting members from the peculiar family of bound entangled states. Their entanglement is delocalized and they look separable with respect to any partition into a single party and all remaining parties. Only one example of quantifying the entanglement of this type of states in the case of three-qubit states has been studied [C. Branciard, H. Zhu, L. Chen, and V. Scarani, Evaluation of two different entanglement measures on a bound entangled state, Phys. Rev. A 82, 012327 (2010)]. We provide a systematic procedure for generating decompositions of such states in terms of minimally entangled states. This procedure enables us to measure entanglement in terms of the concurrence for a wide range of families of these bound entangled (BE) states with each family containing an infinite number of states. In particular, we study one family of three-qubit states in addition to six inequivalent families of four-qubit bound entangled states. These studied families are examples that represent all the known 1446 families of four-qubit UPB-based bound entangled states, which means that our method is applicable to all the 1446 families. In addition, a discussion on the matter of applicability of the proposed strategy to additional families is provided. Specifically, the proposed method looks promising for measuring entanglement of other families of UPB-based BE states in addition to some families of symmetric multipartite positive under partial transposition BE states.
The Research on Bridge Engineering Risk Management and Assessment Model Based on BP Neural Network

Abstract Bridge engineering risk research is an important part of bridge engineering management. This paper establishes the risk indicators system of bridge project, and then introduces BP neural network theory into bridge risk evaluation. By using the neural network toolbox of MATLAB, a risk assessment model of BP neural network is bulit. The validity and feasiblility of the risk evaluation model is verified by a practical example.
Supervision as a model of inclusive education retraining and professional advancement of the school community

In this article, we describe the model of supervision in support of the Education Reform in Ukraine and the retraining of the school’s key stakeholders in an inclusion: the case of Kryvyi Rih. Empirical study has introduced a model of retraining through a socio-political and educational description of the current situation in Kryvyi Rih. That is why 124 key stakeholders from 20 schools with inclusive education were tested to plan the implementation model of supervision (teachers, tutors, staff, psychologists and parents from 7 districts of Kryvyi Rih were invited to participate). In this article, we present a different approach to supervisory interagency groups and understanding, with the focus on the model supervision retraining of inclusive education professionals. The supervision consists of four stages: case report, exchange, conceptualization, summary. For successful work, we propose in the article an adaptation of an unstructured interview by a specialist who describes the situation of training supervision. The analysis of the organizational, methodological and information retraining system, the implementation of the pilot project of supervisory groups for the psycho-educational support team in inclusive education allowed to offer the supervision program: I. Training of the team members to work with the goals of the individual child development plan: working with tools for preparation of the case; II. The main features of observing the lesson; III. Analysis of environmental factors; IV. Oversee team support.
Novel Feature Extraction Strategies Supporting 2D Shape Description and Retrieval

Acute shape characterization in image retrieval tasks remain a persisting issue in computer vision determining their retrieval performance. This chapter contributes and relatively compares three such descriptors that are further tested for shape classification by employing a supervised machine learning mechanism. The core objective of this chapter is the effective exploitation of simple computing concepts for realizing shape descriptors aiding retrieval. Accordingly, simple and novel shape descriptors with its performance analysis are presented in this chapter. The potency of these methods is investigated using the Bull’s Eye Retrieval (BER) rate on benchmarked datasets such as the Kimia, MPEG-7 CE Shape-1 part B and Tari-1000. Consistent BER greater than 90% attained across the diverse datasets affirms the descriptors efficacy, consequently signifying the robustness of these descriptors towards diverse affine transformations thereby, making it suitable for dynamic CBIR applications.
Yes/No Question Answering in BioASQ 2019

The field of question answering has gained greater attention with the rise of deep neural networks. More and more approaches adopt paradigms which are based primarily on the powerful language representations models and transfer learning techniques to build efficient learning models which are able to outperform current state of the art systems. Endorsing this current trend, in this paper, we strive to take a step towards the goal of answering yes/no questions in the field of biomedicine. Specifically, the task is to give a short answer (yes or no) for a question written in natural language, finding clues including in a set of snippets that are related with this question. We propose three different deep neural network models, which are free of assumptions about predefined specific feature functions, while the key elements of these are the ELMo embeddings, the similarity matrices and/or sentiment information. The results have shown that incorporating the sentiment, we can improve the performance of a yes/no question answering system while the proposed learning models significantly outperform the BioASQ baseline.
Binary Pattern Descriptors for Scene Classification

Scene classification is a computer vision task that aims to identify the kind of scene (such as forest, mountain, beach) where a picture was taken. Scene classification has application in the development of automatic surveillance systems, robotic navigation, content-based image retrieval systems among other areas. According to how a scene is recognized, scene classification algorithms can be divided in two categories: based on object recognition and based on low-level image features obtained by applying descriptors. This paper proposes a new binary descriptor called Local Lineal Binary Pattern and a new framework that allows the combination of the new binary descriptor with another local and global descriptors in order to improve the automatic classification of scene images. This new binary descriptor increases the spatial support for its calculation allowing to add more spatial information than the traditional binary descriptors such as the Local Binary Pattern and the Modified Census Transform. Experiments conducted over indoor and outdoor scene datasets show that the new proposed descriptor and framework help to improve the results obtained by related works.
Computer-Supported Analysis of Arguments in Climate Engineering

Climate Engineering (CE) is the intentional large-scale intervention in the Earth’s climate system to counter climate change. CE is highly controversial, spurring global debates about whether and under which conditions it should be considered. We focus on the computer-supported analysis of a small subset of the arguments pro and contra CE interventions as presented in the work of Betz and Cacean (2012), namely those drawing on the “ethics of risk”; these arguments point out uncertainties in future deployment of CE technologies. The aim of this paper is to demonstrate and explain the application of higher-order interactive and automated theorem proving (utilizing shallow semantical embeddings) to the logical analysis of “real-life” argumentative discourse.
Collaborating for public access to scholarly publications: A case study of the partnership between the US Department of Energy and CHORUS

Key points The success of the CHORUS and DOE relationship is the result of nearly two decades of interactions between the DOE and a group of scientific publishers. The relationship between CHORUS and the US federal agencies required understanding of different motivations, operations, and philosophies. Although achieving public access was simple in principle, it required considerable effort to develop systems that satisfied all parties. Publishers had been working with federal agencies to achieve open access before the 2013 White House Office of Science and Technology Policy, but this helped to create a path for a more fruitful relationship.
Facial Emotion and Action Unit Recognition based on Bayesian Network

Understanding human facial expressions is one of the key steps to achieving human-computer interaction. In this paper, we report on advances we have made in building a Bayesian network (BN) for facial expressions recognition based on the CK+ datasets. One of the motivating factors for using a BN is that it can simulate the process of generation and cognition of facial expressions and action units (AUs) realistically. The constructed BN comprises five layers; besides commonly-used expressions, AUs, and feature layers, brain cognition and facial muscles layers are firstly proposed and innovatively designed for the recognition of facial expressions. Further, via deep analysis, we firstly select the most proper facial landmark points, and then fuse their displacement feature and texture features as low-level features. The results of experiments indicate that the two layer we proposed and the discriminative low-level feature we selected are effective to improve the recognition rate. It also indicates the combination of human brain mechanisms and computer statistical learning is successful.
Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration

With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research. However, the potential of such models is often hindered because they tend to be computationally expensive and consequently ill-fitting for uncertainty quantification. Furthermore, they are usually not calibrated with real-time observations. We develop a computationally efficient algorithm based on variational Bayes inference (VBI) for calibration of computer models with Gaussian processes. Unfortunately, the standard fast-to-compute gradient estimates based on subsampling are biased under the calibration framework due to the conditionally dependent data which diminishes the efficiency of VBI. In this work, we adopt a pairwise decomposition of the data likelihood using vine copulas that separate the information on dependence structure in data from their marginal distributions and leads to computationally efficient gradient estimates that are unbiased and thus scalable calibration. We provide an empirical evidence for the computational scalability of our methodology together with average case analysis and describe all the necessary details for an efficient implementation of the proposed algorithm. We also demonstrate the opportunities given by our method for practitioners on a real data example through calibration of the Liquid Drop Model of nuclear binding energies.
Radical Text Detection based on Stylometry

The Internet has become an effective tool for terrorist and radical groups to spread their propaganda. One of the current problems is to detect these radical messages in order to block them or promote counter-narratives.In this work, we propose the use of stylometric methods for characterizing radical messages. We have used a machine learning approach to classify radical texts based on a corpus of news from radical sources such as the so-called ISIS online magazines Dabiq and Rumiyah, as well as news from general newspapers. The results show that stylometric features are effective for radical text classification.
Teaching VR in Higher Education with Collaborative Projects

This paper presents a didactical concept to introduce students from various disciplines to digital technology and specifically to Virtual Reality technology. The concept is embedded into a constructivist learning environment to increase active involvement, self-directed learning and eventually motivation of students. Furthermore, it is based on a homogenous introductory framework in order to allow for a common knowledge baseline for the subsequent discovery learning within diverse contexts. Two applications are presented: a university teaching module in tourism management as well as in business administration. First qualitative and quantitative evaluations of the concept show positive results not only with regard to knowledge acquisition but also in terms of social, emotional and active involvement of students.
From the total survey error framework to an error framework for digital traces of humans

The digital traces of hundreds of millions of people offer increasingly comprehensive pictures of both individuals and groups on different platforms, but also allow inferences about broader target populations beyond those platforms. Studying the errors that can occur when digital traces are used to learn about humans and social phenomena is essential. Many similar errors also affect survey estimates, which survey designers have been addressing for decades, most notably using the Total Survey Error Framework (TSE). In this tutorial, we first introduce the audience to the concepts and guidelines of the TSE and how they are applied by survey practitioners in the social sciences. Second, we introduce our own conceptual framework to diagnose, understand, and avoid errors that may occur in studies that are based on digital traces of humans.
Narrative Knowledge Representation and Blockchain: A Symbiotic Relationship

Notwithstanding that the Semantic Web improves the information interoperability, however, the spatio-temporal knowledge representation, the description of relatively complex events – like those that can involve within smart contracts – security, reliability and trust are still difficult to achieve. In addition, Blockchain technology promotes information sharing and is supplying secure communication architecture for distributed systems. Therefore, intertwine an ontology language with Blockchain allows information interoperability and provides message-integrity checking and resolve the trust in all kinds of applications (IoT, robotic, industry 4.0, health record, etc.). In fact, ontologies facilitate the understanding and sharing of intrinsic components –smart contracts, transaction, knowledge- and relationship within a Blockchain. The main goal of this approach is to promote common data exchange, the design of decentralized, and the chronological semantic relationships on the Blockchain and Ethereum Smart Contracts making use of the narrative semantic representation foundations allowing therefore the web to survive and Blockchain technology to prosper.
Symbolic Computation of Tight Causal Bounds

Causal inference involves making a set of assumptions about the nature of things, defining a causal query, and attempting to find estimators of the query based on the distribution of observed variables. When causal queries are not identifiable from the observed data, it still may be possible to derive bounds for these quantities in terms of the distribution of observed variables. We develop and describe a general approach for computation of bounds, proving that if the problem can be stated as a linear program, then the true global extrema result in tight bounds. Building upon previous work in this area, we characterize a class of problems that can always be stated as a linear programming problem; we describe a general algorithm for constructing the linear objective and constraints based on the causal model and the causal query of interest. These problems therefore can be solved using a vertex enumeration algorithm. We develop an R package implementing this algorithm with a user friendly graphical interface using directed acyclic graphs, which only allows for problems within this class to be depicted. We have implemented additional features to help with interpreting and applying the bounds that we illustrate in examples.
Speeding-up CNN inference through dimensionality reduction

Computational complexity of CNNs makes their integration in embedded systems with low power consumption requirements a challenging task, which requires the joint design and adaptation of hardware and algorithms. In this paper, we propose a new general CNN compression method, allowing to reduce both the number of parameters and operations. This method is applied to a binary face detection network which is then implemented and evaluated on hardware.
Multi person pose estimation based on improved openpose model

Abstract In order to solve the problem of multi person pose estimation, a human body pose estimation model framework based on openpose from American Carnegie Mellon University is presented, which uses the COCO (Microsoft common objects in context) data set to augment the data, optimize the network structure and retrain, adjust the learning rate properly and the convergence speed in the early stage of the model, finally using the model to carry out research on human key point detection and visualizing analysis in a multi-person complex scene. It is found that the improved openpose model adopts a bottom to up detection strategy to avoid the influence of the number of human bodies on real-time performance, and has higher key point detection efficiency than the top to down method. The study provides a real-time position feedback scheme for human pose estimation in complex scenes.
Comparative Research on Terminology Databases in Europe and China

Terminology databases constitute an essential tool for academic research and practical translation. Both Europe and China have their special terminology databases for their actual need. The databases echo the integrated thought of Europe and China’s terminology management mechanisms. The IATE in Europe is an inter-active database constructed over many years. China has already created a large set of terminology databases, e.g. TERMONLINE. The present study will comparatively introduce and interview both IATE and TERMONLINE, analyze the background of construction and provide real and practical information for translation. It can be concluded that with the advancement of emerging technologies in the future, more and more terminology databases will be designed and established in Europe and China, thus further keeping the balance between human and knowledge interaction.
Pragmatic Software Maintainability Management Using a Multi-agent System Working in Collaboration with the Development Team

This paper introduces an ongoing PhD work that focuses on the design of a multi-agent system that would assess software maintainability in collaboration with human developers. Evaluation of the maintainability is difficult to implement on actual industrial projects as specific criteria cannot be easily adapted to the context of a project, and metrics are not always relevant to the architectural pattern in use. The metrics can be bypassed without improving the quality of the source code. We have the ambition to tackle this problem through a more modular, flexible and dynamic assessment system that will ultimately evolve through a dialogic interaction with the development team. Our approach will first lay the foundations of a shared vocabulary between the artificial agents and the human team. We will further develop a multi-agent system first by introducing feedback from the human team and second by moving towards a real dialogic interaction.
Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms: Theory, Implementations, and Complexity

We present a concise source-to-source transformation that introduces justifications for user-defined constraints into the rule-based Constraint Handling Rules (CHR) programming language. There is no need to introduce a new semantics for justifications. This leads to a conservative extension of the language, as we can show the equivalence of rule applications. A scheme of two rules suffices to allow for logical retraction (deletion, removal) of CHR constraints during computation. Without the need to recompute from scratch, these rules remove the constraint and also undo all its consequences. We prove a confluence result concerning the rule scheme. We prove its correctness in general and tighten the results for confluent programs. We give an implementation, show its correctness, present two classical examples of dynamic algorithms, and improve the implementation. The computational overhead of introducing justifications and of performing logical retraction, i.e. the additional time and space needed, is proportional to the derivation length in the original program. This overhead may increase space complexity, but does not change the worst-case time complexity.
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss. We train our network by gathering a large-scale dataset of hand action in YouTube videos and use it as a source of weak supervision. Our weakly-supervised mesh convolutions-based system largely outperforms state-of-the-art methods, even halving the errors on the in the wild benchmark. The dataset and additional resources are available at this https URL.
An automated ontology learning for benchmarking classifier models through gain-based relative-non-redundant feature selection: a case-study with erythemato-squamous disease

Erythemato-squamous disease (ESD) is one of the complex diseases in the dermatology field, the diagnosis of which is challenging, due to common morphological features and often leads to inconsistent results. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for prediction of erythemato-squamous disease through data mining techniques was believed to yield a clear representation of the relationships between the disease, symptoms and course of treatment. However, the classification accuracy required to be high in order to obtain a precise ontology. This required identifying the correct set of optimal features required to predict ESD. This paper proposes the Gain based Relative-Non-Redundant Attribute selection approach for diagnosis of ESD. This methodology yielded 98.1% classification accuracy with Adaboost algorithm that executed J48 as the base classifier. The feature selection approach revealed an optimal feature set comprising of 19 selected features.