Reviewerly/exHarmony huggingface dataset:
A non-smooth-contact-dynamics analysis of Brunelleschi’s cupola: an octagonal vault or a circular dome?

The cupola (dome) of Santa Maria del Fiore in Florence was ingeniously constructed by Brunelleschi using a conical bricklaying, radial-oriented toward a focus point on the central axis. Therefore, the dome is built as a surface of revolution but with parts cut away to leave the octagonal cluster vault form. This circular arrangement is compared with an octagonal horizontal corbelling in models where the dome is schematized as an assembly of rigid-blocks in frictional contact, analyzed with a Non-Smooth-Contact-Dynamics approach. The high indeterminacy of the contact reactions implies considerable difficulties in their determination, which are faced via a regularization procedure by adding a compliance at the contact points in representation of the deformability of the mortar joints. Numerical experiments, performed with a custom software, highlight the uniform flow of forces in the Brunelleschi arrangement, but evidence the disturbances induced by the herringbone spirals, mainly used for construction purposes, which are overloaded along the meridians and very weak in the direction of the parallels. This is due to the vertical narrow disposal of the blocks, which increases the stiffness in meridional direction, but diminishes the capacity of the friction-induced forces to equilibrate the hoop stress.
Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

The multiple model tracking methods for hypersonic gliding vehicles (HGVs), which improves the design of tracking model-set is proposed in this paper.Different from the traditional tracking method with dynamic pressure models, the proposed method refines the model-set by modeling and analyzing the specific aerodynamic characteristics of the HGVs.Based on the aerodynamic model of the vehicle, the novel tracking models are established and the multiple model tracking methods are adopted.The uniform modelsets are designed by determining the representative points from the constrained characteristic parameters to ensure the stability of the tracking system.Moreover, the model-sets are then augmented by the real-time updates of the motion estimations to further improve the tracking accuracy.The simulation studies indicate that the proposed tracking method outperforms the method with dynamic pressure models in both position and velocity estimations, the position estimation accuracy is increased by about 23%-53% and the velocity estimation accuracy is increased by about 42%-56% in the given examples.
Multiple Description Coding Based on Convolutional Auto-Encoder

Deep learning, such as convolutional neural networks, has been achieved great success in image processing, computer vision task, and image compression, and has achieved better performance. This paper designs a multiple description coding frameworks based on symmetric convolutional auto-encoder, which can achieve high-quality image reconstruction. First, the image is input into the convolutional auto-encoder, and the extracted features are obtained. Then, the extracted features are encoded by the multiple description coding and split into two descriptions for transmission to the decoder. We can get the side information by the side decoder and the central information by the central decoder. Finally, the side information and the central information are deconvolved by convolutional auto-encoder. The experimental results validate that the proposed scheme outperforms the state-of-the-art methods.
Assessing reliability of Big Data Knowledge Discovery process

Abstract Extracting knowledge from Big Data is the process of transforming this data into actionable information. The exponential growth of data has initiated a myriad of new opportunities, and made data become the most valuable raw material of production for many organizations. Mining Big Data is coupled with some challenges, known as the 3V’s of Big Data: Volume, Variety and Velocity. However, a major challenge that needs to be addressed, and often is ignored in the literature, concerns reliability. Actually, data is agglomerated from multiple disparate sources, and each of Knowledge Discovery (KDD) process steps may be carried out by different organizations. These considerations lead us to ask a critical question that is weather the information we have at each step is reliable enough to proceed to the next one? This paper therefore aims to provide a framework that automatically assesses reliability of the knowledge discovery process. We focus on Linked Open Data (LOD) as a source of data, as it constitutes a relevant data provider in many Big Data applications. However, our framework can also be adapted for unstructured data. This framework will assist scientists to automatically and efficiently measure the reliability of each KDD process stage as well as detect unreliable steps that should be revised. Following this methodology, KDD process will be optimized and therefore produce knowledge with higher quality.
Adapting Jake Knapp’s Design Sprint Approach for AR/VR Applications in Digital Heritage

Modern digital devices offer huge potential for the delivery of engaging heritage experiences to visitors, offering a better visitor experience, higher visitor numbers, and opportunities for increased tourism income. However, all software development entails risk, including the risk of developing a product which few will want, or be able, to use. Identifying user experience priorities and problems at an early stage is therefore extremely important. This chapter describes work in progress on a shortened version of Jake Knapp's Design Sprint approach, and its application to designing VR/AR solutions for a specific heritage case study.
Compatible natural gradient policy search

Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a standard exponential policy distribution in combination with compatible value function approximation. Moreover, we show that standard natural gradient updates may reduce the entropy of the policy according to a wrong schedule leading to premature convergence. To control entropy reduction we introduce a new policy search method called compatible policy search (COPOS) which bounds entropy loss. The experimental results show that COPOS yields state-of-the-art results in challenging continuous control tasks and in discrete partially observable tasks.
Multiparametric amplification and qubit measurement with a Kerr-free Josephson ring modulator

Josephson-junction based parametric amplifiers have become a ubiquitous component in superconducting quantum machines. Although parametric amplifiers regularly achieve near-quantum limited performance, they have many limitations, including low saturation powers, lack of directionality, and narrow bandwidth. The first is believed to stem from the higher order Hamiltonian terms endemic to Josephson junction circuits, and the latter two are direct consequences of the nature of the parametric interactions which power them. In this work, we attack both of these issues. First, we have designed a new, linearly shunted Josephson Ring Modulator (JRM) which nearly nulls all 4th-order terms at a single flux bias point. Next, we achieve gain through a pair of balanced parametric drives. When applied separately, these drives produce phase-preserving gain (G) and gainless photon conversion (C), when applied together, the resultant amplifier (which we term GC) is a bi-directional, phase-sensitive transmission-only amplifier with a large, gain-independent bandwidth. Finally, we have also demonstrated the practical utility of the GC amplifier, as well as its' quantum efficiency, by using it to read out a superconducting transmon qubit.
Catch Me If You Can: Rogue Access Point Detection Using Intentional Channel Interference

In this paper, we introduce a powerful hardware-based rogue access point (PrAP), which can relay back and forth traffic between a legitimate AP and a wireless station, and act as a man-in-the-middle attacker. Our PrAP is built of two dedicated wireless routers interconnected physically, and can relay traffic rapidly between a station and a legitimate AP. Through experiments, we demonstrate that the state-of-the-art time-based rogue AP (rAP) detectors cannot detect our PrAP, although perhaps effective against software-based rAP. In demonstrating that, we unveil new insight into fundamentals of time-based detectors for software-based rAPs and their operation: such techniques are only capable of detecting rAPs due to the speed of wireless AP bridging. To address the threat of such PrAPs, we propose a new tool for network administrators, a PrAP-Hunter based on intentional channel interference. Our PrAP-Hunter is highly accurate, even under heavy traffic scenarios. Using a high-performance (desktop) and low-performance (mobile phone) experimental setups of our PrAP-Hunter in various deployment scenarios, we demonstrate close to 100 percent of detection rate, compared to 60 percent detection rate by the state-of-the-art. We show that our PrAP-Hunter is fast (takes 5-10 seconds), does not require any prior knowledge, and can be deployed in the wild by real-world experiments at 10 coffee shops.
A novel incremental one-class support vector machine based on low variance direction

Low variance direction of the training dataset can carry crucial information when building a performant one-class classifier. Covariance-guided One-Class Support Vector Machine (COSVM) emphasizes the low variance direction of the training dataset which results in higher accuracy. However, in the case of large scale datasets, or sequentially obtained data, it shows a serious performance degradation and requires a large memory and an important training time. Thus, in this paper, we investigate the effectiveness of using the low variance directions in an incremental approach. In fact, incremental learning is more effective when dealing with dynamic or important amount of data. More precisely, we control the possible changes of support vectors after the addition of new data points, while emphasizing the low variance directions of the training data, in order to improve classification performance. An extensive comparison of the incremental COSVM to contemporary batch and incremental one-class classifiers on artificial and real-world datasets demonstrates the advantage and the superiority of our proposed model.
Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data

Abstract Given the confirmed effectiveness of the survey‐based consumer sentiment index (CSI) as a leading indicator of real economic conditions, the CSI is actively used in making policy judgments and decisions in many countries. However, although the CSI offers qualitative information for presenting current conditions and predicting a household's future economic activity, the survey‐based method has several limitations. In this context, we extracted sentiment information from online economic news articles and demonstrated that the Korean cases are a good illustration of applying a text mining technique when generating a CSI using sentiment analysis. By applying a simple sentiment analysis based on the lexicon approach, this paper confirmed that news articles can be an effective source for generating an economic indicator in Korea. Even though cross‐national comparative research results are suited better than national‐level data to generalize and verify the method used in this study, international comparisons are quite challenging to draw due to the necessary linguistic preprocessing. We hope to encourage further cross‐national comparative research to apply the approach proposed in this study.
Assessing Intercultural Competence of Engineering Students and Scholars for Promoting Academic Mobility

This article provides an assessment of the intercultural competence of engineering university students and professors as an essential tool for the development of academic mobility. EU states and the US are the leading countries in international academic mobility. In recent years, however, China also scores high results in this field and has developed a number of efficient mechanisms for promoting intercultural competence. The study of its experience can be of great importance for higher education in other countries. Thus the primary purpose of the research is to present and assess the mechanisms of intercultural competence development for academic mobility purposes of the engineering students and scholars in China and justify the possibilities of using them in higher education of other countries. To implement this an expert survey was conducted among university professors.
A QoS-based approach for cloud-service matchmaking, selection and composition using the Semantic Web

Purpose Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web. Design/methodology/approach In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction. Findings This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks. Originality/value As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.
Poster: Teachers-Researchers Training at Technological University

The article focuses on a currently important issue: teachers-researchers training at Technological University. It presents the educational goals, the structure of the content, educational and control methods and means that provide formation and development of a new professional competence: ability to organize the process of professional training basing on the methodology, theory and technologies of the contemporary educational science. The training addresses all the post-graduate student of the university.
All on the same boat? Voting for pirate parties in comparative perspective

The rise of pirate parties in Europe since 2006 has been tempestuous and uneven. One may question whether the reasons citizens vote for these parties are the same between different countries. This article identifies two reasons citizens may vote for these parties: political distrust and concerns about privacy. This article tests which of these two underlies support for pirate parties. It uses six surveys covering 11 countries with a total 43,786 respondents. These analyses show that political distrust primarily drives support for pirate parties.
Retro-Remote Sensing: Generating Images From Ancient Texts

The data available in the world come in various modalities, such as audio, text, image, and video. Each data modality has different statistical properties. Understanding each modality, individually, and the relationship between the modalities is vital for a better understanding of the environment surrounding us. Multimodal learning models allow us to process and extract useful information from multimodal sources. For instance, image captioning and text-to-image synthesis are examples of multimodal learning, which require mapping between texts and images. In this paper, we introduce a research area that has never been explored by the remote sensing community, namely the synthesis of remote sensing images from text descriptions. More specifically, in this paper, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, to generate equivalent remote sensing images. From a methodological perspective, we propose to rely on generative adversarial networks (GANs) to convert the text descriptions into equivalent pixel values. GANs are a recently proposed class of generative models that formulate learning the distribution of a given dataset as an adversarial competition between two networks. The learned distribution is represented using the weights of a deep neural network and can be used to generate more samples. To fulfill the purpose of this paper, we collected satellite images and ancient texts to train the network. We present the interesting results obtained and propose various future research paths that we believe are important to further develop this new research area.
A relaxed Newton–Picard like method for Huber variant of total variation based image restoration

In this paper, we propose an effective iteration method for Huber variant of total variation based image restoration by exploiting the structure of the problem. We call the proposed method relaxed Newton–Picard like method. This method is easy to implement and cost-effective. We prove the convergence of the method by using the theory on semismooth functions. Experimental results show that the proposed method is more efficient than the alternating minimization method based on multiplicative half-quadratic reformulation and is competitive with the state of the art alternating direction method of multipliers.
Remote Sensing Image Compression Based on Adaptive Directional Wavelet Transform With Content-Dependent Binary Tree Codec

Remote sensing images provide a wealth of information for a variety of applications, but it is at the expense of huge data. In this paper, we present a novel compression method based on optimum adaptive directional lifting (OADL) with content-dependent binary tree codec. First, the OADL model is designed, which calculates the optimal prediction direction of each image block and performs the weighted directional adaptive interpolation during the process of lifting. The former aims to reduce the edge and texture energy of the non-horizontal and non-vertical directions in the high-frequency subbands, and the latter focuses on preserving the directional characteristics of remote sensing images as much as possible. Second, a binary tree codec with content-based adaptive scanning is introduced, which can provide different scanning orders and scanning manners among and within subbands, respectively. In addition, it can encode more significant coefficients at the same bit rate. Experimental results show that, compared with other scan-based compression methods, the proposed compression method can always provide better coding performance in terms of some evaluation indexes.
Twister2: Design of a big data toolkit

Summary Data‐driven applications are essential to handle the ever‐increasing volume, velocity, and veracity of data generated by sources such as the Web and Internet of Things (IoT) devices. Simultaneously, an event‐driven computational paradigm is emerging as the core of modern systems designed for database queries, data analytics, and on‐demand applications. Modern big data processing runtimes and asynchronous many task (AMT) systems from high performance computing (HPC) community have adopted dataflow event‐driven model. The services are increasingly moving to an event‐driven model in the form of Function as a Service (FaaS) to compose services. An event‐driven runtime designed for data processing consists of well‐understood components such as communication, scheduling, and fault tolerance. Different design choices adopted by these components determine the type of applications a system can support efficiently. We find that modern systems are limited to specific sets of applications because they have been designed with fixed choices that cannot be changed easily. In this paper, we present a loosely coupled component‐based design of a big data toolkit where each component can have different implementations to support various applications. Such a polymorphic design would allow services and data analytics to be integrated seamlessly and expand from edge to cloud to HPC environments.
Exposure to a pleasant odour may increase the sense of reality, but not the sense of presence or realism

Smell can increase the sense of presence, reality, and realism when exposed in a virtual environment. This effect has been found to be increased when the nature of the odour is concordant visually with the scene, i.e. exposure to an unpleasant odour in a filthy virtual kitchen. The objective of this project was to verify whether this effect could be generalised to pleasant odours. Participants were immersed in a virtual apartment with a kitchen where the visual scene suggested that cinnamon apple pies had recently been baked. Participants were randomly and blindly assigned to three conditions: exposition to the ambient air, to a pleasant odour of cinnamon apple pie, or an unpleasant odour of urine. The results indicated that while exposure to the visually concordant pleasant odour did increase the sense of reality in a statistically significant manner, it did not affect the sense of presence or realism. Results also suggested that the visual/olfactory concordance may have facilitated the detection of the pleasant odour. The potential implications of the results, potential explanations for the lack of effect on the sense of presence, as well as potential follow-up research projects are discussed.
Deterministic and non‐deterministic query optimization techniques in the cloud computing

Summary Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP‐hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non‐deterministic methods are two main categories to study these techniques. The deterministic and non‐deterministic query optimization methods can be further divided into three subcategories, cost‐based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future.