Reviewerly/exHarmony huggingface dataset:
9. Springing the Floor for a Different Kind of Dance

Edmond, Fischer, Romary, and Tasovac begin this chapter by exploring what infrastructure means in different contexts before going on to consider ‘digital infrastructure’ as not only a tool that needs to be built but also understood. They examine practices and theories in an attempt to define infrastructure for the arts and humanities in the digital age: firstly, considering infrastructure as knowledge spaces; secondly, considering why the arts and humanities need research infrastructure and; thirdly, establishing why a community approach should be adopted and what baseline requirements should be met. They focus on the case study of DARIA ERIC and its dual hierarchical and marketplace structure to optimise knowledge sharing and in-flow from within its community. They argue that infrastructures today not only represent a different model for supporting knowledge creation but are also developing new models for creating knowledge. They conclude that DARIAH ERIC harnesses the best of two communities — research infrastructures as originally conceived of in the sciences, as well as the arts and humanities research base.
Two-finger exoskeleton with force feedback for a mobile robot teleoperation

In this work, the design, manufacturing, instrumentation, and application of a two-finger exoskeleton with force feedback are presented. The exoskeleton is based on remote center of motion mechanisms in order to avoid mechanical interference with the user’s fingers and is manufactured by three-dimensional printing. The developed exoskeleton is applied in a mobile robot teleoperation by mapping the finger movements in forward and turning commands for the robot. The presence of obstacles detected by the robot is sensed by the user by means of a feedback force. The problem of simultaneously communicating a data acquisition card and the robot hardware by MATLAB ® Simulink® was solved by using an external Wi-Fi module. The result is a lightweight exoskeleton which is able to communicate bidirectionally with a mobile robot by a personal computer for teleoperation tasks. The success of the system implementation is proven by a set of experiments presented in the final part of the article.
Sarcasm Detection Using Deep Learning-Based Techniques

Sarcasm is a figure of speech in which the speaker says something that is outwardly unpleasant with an intention of insulting or deriding the hearer and/or a third person. Designing a model for successfully detecting sarcasm has been one of the most challenging task in the field of natural language processing (NLP) because sarcasm detection is heavily dependent on the context of the utterance/statement and sometimes, even human beings are not able to detect the underlying sarcasm in the utterance. In this chapter, we design features for detecting sarcasm using pragmatic features that take into account the context of the utterance. The approach is based on a linguistic model that describes how humans distinguish between different types of untruths. We then train various machine-learning-based classifiers and compare their accuracies.
Chapter 5. The annotation of list structures

This chapter presents phenomena we call “piles” or “lists”, which are characterized by the fact that a list of elements piles up in the same syntactic position. We therefore group the analysis of coordination together with the analysis of other phenomena such as reformulation, disfluency, partial answer, or negotiation. The elements of a pile are linked to one another by a relation that is both syntagmatic (they follow one another) and paradigmatic (they fill the same syntactic slot with respect to their common governor). The syntactic analysis of the other elements – junctors, paradigmatic adverbs, and list completers – is discussed. We also propose a typology of the different cases of pile structure and introduce the seven subcases of paradigmatic links taken into account in the annotation.
A Secure and Efficient Identity-Based Proxy Signcryption in Cloud Data Sharing

As a user in modern societies with the rapid growth of Internet environment and more complicated business flow processes in order to be effective at work and accomplish things on time when the manager of the company went for a business trip, he/she need to delegate his/her signing authorities to someone such that, the delegatee can act as a manager and sign a message on his/her behalf.In order to make the delegation process more secure and authentic, we proposed a secure and efficient identity-based proxy signcryption in cloud data sharing (SE-IDPSC-CS), which provides a secure privilege delegation mechanism for a person to delegate his/her signcryption privilege to his/her proxy agent.Our scheme allows the manager of the company to delegate his/her signcryption privilege to his/her proxy agent and the proxy agent can act as a manager and generate signcrypted messages on his/her behalf using special information called "proxy key".Then, the proxy agent uploads the signcrypted ciphertext to a cloud service provider (CSP) which can only be downloaded, decrypted and verified by an authorized user at any time from any place through the Internet.Finally, the security analysis and experiment result determine that the proposed scheme outperforms previous works in terms of functionalities and computational time.
A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

The development of usable visualization solutions is essential for ensuring both their adoption and effectiveness. User-centered design principles, which involve users throughout the entire development process, have been shown to be effective in numerous information visualization endeavors. We describe how we applied these principles in scientific visualization over a two year collaboration to develop a hybrid in situ/post hoc solution tailored towards combustion researcher needs. Furthermore, we examine the importance of user-centered design and lessons learned over the design process in an effort to aid others seeking to develop effective scientific visualization solutions.
e-EMOTION CAPSULE

This paper describes the process of creating the multidimensional artifact "e-EMotion-Capsule" that explores immersiveness to generate emotions through the creation of impactful environments. E-EMotion-Capsule's multidimensional perspective allows participants to experience different environments created using immersive technologies such as 360 video, virtual reality environments and mixed reality platforms. The participant puts on RV goggles to interact with the e-EMotion-Capsule artifact, becoming himself part of the immersive universe in which he became involved. Their own emotions trigger the cognitive functions of perceptive, symbolic and logical processing that determine their interpretation. The artifact uses the innovative 360-degree narrative concept in which body movement provides interactive, autonomous and personalized experimentation, coupled with visual and sound stimuli that fuse with the participant himself. Digital art incorporating this type of narrative induces the participant to assume the role of the main character, unleashing in this the duality between reacting and acting, between observing and provoking, breaking indifference and generating unrest. The construction of the artifact was validated with a set of tests that focused on programming, interface design and measurement of the ability to generate emotions. The experience within the universe created by e-EMotion-Capsule allows each person to build their own experimentation, navigating the artistic space guided by their own cognitive sense.
Quadratic and symmetric bilinear forms over finite fields and their association schemes

Let 𝒬(m,q) and 𝒮(m,q) be the sets of quadratic forms and symmetric bilinear forms on an m-dimensional vector space over 𝔽 q , respectively. The orbits of 𝒬(m,q) and 𝒮(m,q) under a natural group action induce two translation association schemes, which are known to be dual to each other. We give explicit expressions for the eigenvalues of these association schemes in terms of linear combinations of generalised Krawtchouk polynomials, generalising earlier results for odd q to the more difficult case when q is even. We then study d-codes in these schemes, namely subsets X of 𝒬(m,q) or 𝒮(m,q) with the property that, for all distinct A,B∈X, the rank of A-B is at least d. We prove tight bounds on the size of d-codes and show that, when these bounds hold with equality, the inner distributions of the subsets are often uniquely determined by their parameters. We also discuss connections to classical error-correcting codes and show how the Hamming distance distribution of large classes of codes over 𝔽 q can be determined from the results of this paper.
User Characteristics, Trait vs. State Immersion, and Presence in a First-Person Virtual World

Virtual reality and virtual worlds (VWs) are powerful technologies currently helping to define the digital world. These technologies are characterized by user control, immersion, and a sense of presence or “being there.” They have been examined from a variety of theoretical perspectives, technical and user variables, and psychological approaches. The purpose of this study was to extend VW research by relating the roles of user age and gender directly to the VW-critical features of immersion and perceived presence, an approach that has not been widely addressed in previous research. This study used a photo-real, on-screen, first-person VW in which users “enter” and “walk through” a VW via mouse navigation, viewing it through their own eyes rather than through an avatar. It used a quasi-experimental design with 35 adult subjects who reported their age and gender and were tested for perceived immersion and presence in a VW showing a 360-degree city panorama. Data were analyzed using t-tests, chi-squares, Pearson correlations, and interview comments to examine relationships among age and gender, immersion, and presence in the VW. Findings supported conclusions relating to the relevance of age and gender as user variables in VWs and the role of technology characteristics in VWs’ effective use. The study also opened a potential new line of inquiry by raising previously unaddressed questions about the importance of the psychological trait vs. state nature and measurement of immersion and presence in VWs. Suggestions for further research are offered.
Intelligent Visual Acuity Estimation System With Hand Motion Recognition

Visual acuity (VA) measurement is utilized to test a subject’s acuteness of vision. Conventional VA measurement requires a physician’s assistance to ask a subject to speak out or wave a hand in response to the direction of an optotype. To avoid this repetitive testing procedure, different types of automatic VA tests have been developed in recent years by adopting contact-based responses, such as pushing buttons or keyboards on a device. However, contact-based testing is not as intuitive as speaking or waving hands, and it may distract the subjects from concentrating on the VA test. Moreover, problems related to hygiene may arise if all the subjects operate on the same testing device. To overcome these problems, we propose an intelligent VA estimation (iVAE) system for automatic VA measurements that assists the subject to respond in an intuitive, noncontact manner. VA estimation algorithms using maximum likelihood (VAML) are developed to automatically estimate the subject’s vision by compromising between a prespecified logistic function and a machine-learning technique. The neural-network model adapts human learning behavior to consider the accuracy of recognizing the optotype as well as the reaction time of the subject. Furthermore, a velocity-based hand motion recognition algorithm is adopted to classify hand motion data, collected by a sensing device, into one of the four optotype directions. Realistic experiments show that the proposed iVAE system outperforms the conventional line-by-line testing method as it is approximately ten times faster in testing trials while achieving a logarithm of the minimum angle of resolution error of less than 0.2. We believe that our proposed system provides a method for accurate and fast noncontact automatic VA testing.
Process calculi as a tool for studying coordination, contracts and session types

We recall techniques, mainly based on the theory of process calculi, that we used to prove results in twenty years of research, spanning across the old and the new millennium, on the expressiveness of coordination languages and on behavioural contracts for Service-Oriented Computing. Then, we show how such techniques recently contributed to the clarification of aspects that were unclear about session types, in particular, asynchronous session subtyping that was considered decidable since 2009, while it was proved to be undecidable in 2017.
On efficiently storing huge property graphs in relational database management systems

Graph structured data can be found in an increasing amount of use-cases. While there exists a considerable number of solutions to store graphs in NoSQL databases, the combined storage of relationally stored data with huge graph structured data within the same relational database system is not well researched. We present a relational approach for storing and querying huge property graphs by combining NoSQL features, provided by nearly any state-of-the-art database system, and an adjacency table approach. Our approach is optimized for read-only queries but also performs well on update queries. Through an empirical evaluation we show that we achieve a 10 times higher throughput than previous works on a graph with up to 650 million edges. This way, we can use all the advantages of full-fledged relational database systems and seamlessly integrate classical relational data with graph-structured data in an efficient way.
On the Hamming Distance of Repeated-Root Cyclic Codes of Length 6<i>p<sup>s</sup> </i>

Let p be an odd prime, s, m be positive integers such that p m ≡ 2 (mod 3).In this paper, using the relationship about Hamming distances between simple-root cyclic codes and repeated-root cyclic codes, the Hamming distance of all cyclic codes of length 6p s over finite field F p m is obtained.All maximum distance separable (MDS) cyclic codes of length 6p s are established.
Quantum coherence and correlation dynamics of two-qubit system in spin bath environment*

The quantum entanglement, discord, and coherence dynamics of two spins in the model of a spin coupled to a spin bath through an intermediate spin are studied. The effects of the important physical parameters including the coupling strength of two spins, the interaction strength between the intermediate spin and the spin bath, the number of bath spins and the temperature of the system on quantum coherence and correlation dynamics are discussed in different cases. The frozen quantum discord can be observed whereas coherence does not when the initial state is the Bell-diagonal state. At finite temperature, we find that coherence is more robust than quantum discord, which is better than entanglement, in terms of resisting the influence of environment. Therefore, quantum coherence is more tenacious than quantum correlation as an important resource.
Data Trading with Differential Privacy in Data Market

In the big data era, data collection and exchange depend heavily on data trading through data market, where privacy protection, pricing and fairness is still an unanswered problem. To meet this need, in this paper, we propose a data-trading approach based on BlockChain in data market. We first redefine the request of data consumer. Then the data price is allocated according to the tolerance of error. Lastly, we utilize differential privacy to achieve the purpose of data privacy protection. Simulations, as well as experimental dataset, validate the effectiveness of our approach.
Interpretable Convolutional Neural Networks Using a Rule-Based Framework for Classification

A convolutional neural network (CNN) learning structure is proposed, with added interpretability-oriented layers, in the form of Fuzzy Logic-based rules. This is achieved by creating a classification layer based on a Neural Fuzzy classifier, and integrating it into the overall learning mechanism within the deep learning structure. Using this new structure, one can extract linguistic Fuzzy Logic-based rules from the deep learning structure directly, and link this information to input features, which enhances the interpretability of the overall system. The classification layer is realised via a Radial Basis Function (RBF) Neural-Network, that is a direct equivalent of a class of Fuzzy Logic-based systems. In this work, the development of the RBF neural-fuzzy system and its integration into the deep-learning CNN is presented. The proposed hybrid CNN RBF-NF structure can form a fundamental building block, towards building more complex deep-learning structures with Fuzzy Logic-based interpretability. Using simulation results on benchmark data (MNIST handwriting digits and MNIST Fashion) we show that the proposed learning structure maintains a good level of forecasting/prediction accuracy compared to CNN deep learning structures. Crucially, we also demonstrate in both cases the resulting interpretability, in the form of linguistic rules that link the classification decisions to the input feature space.
Normal faulting activated by hydraulic fracturing: A case study from the Barnett Shale, Fort Worth Basin

Surface microseismic arrays enable long-term field-scale monitoring over multiple stimulations during the life of an unconventional field. In this study, we show highly economic methods of monitoring with sparse surface arrays in the Barnett Shale and develop an alternatative method of processing to enable good vertical and horizontal resolution of located events. We show that sparse surface monitoring arrays enable not only the detection and location of high numbers of microseismic events but also source mechanism characterization. This case study illustrates how hydraulic fracturing activated normal faulting at a distance of approximately 1 mile from stimulated wells. We show that the source mechanism enables us to resolve between newly created hydraulic fractures and activated faults. The differences in source mechanisms and b-values of newly created fractures and activated faults are consistent with independently processed temporary star-like arrays, which are also deployed over the same stimulation.
Survey of Apache spark optimized job scheduling in big data

Big data have acquired big attention in recent years. As big data makes its way into companies and business so there are some challenges in big data analytics.  Apache spark framework becomes very popular for using in distributed data processing. Spark is an analytic machine for big data processing with various modules for SQL, streaming, graph processing and machine learning. Different scheduling algorithms vary with its behavior, design and also the goal required solving a problem like data locality, energy and time. The main goal in this research is to represent a comprehensive survey on job scheduling modes using in spark, the types of different scheduler, and existing algorithms with advantages and issues. In this paper, various adaptive ways to schedule jobs on spark and development algorithms to improve performance in Spark will be discussed, analyzed and evaluated. A comparison between different scheduling algorithms, strength and weakness points of them are provided. This can aid to the researchers understanding of which scheduling mechanisms best applied for Big Data.
Deep Window Detection in Street Scenes

Windows are key components of building facades.Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision.Early methods try to solve window detection by using hand-crafted features and traditional classifiers.However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs.Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances.Unfortunately, directly training them for challenging window detection cannot achieve satisfying results.In this paper, we propose an approach for window detection.It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module.Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture.Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date.Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.
An Evaluation of Local Binary Descriptors for Facial Emotion Classification

Feature descriptors are vitally important in the broad domain of computer vision. In software systems for face recognition, local binary descriptors find wide use as feature descriptors. Because they give more robust results in varying conditions such as pose, lighting and illumination changes. Precision depends on the correctness of representing the relationship in the local neighbourhood of a digital image into small structures. This paper presents the performance analysis of various binary descriptors such as local binary pattern (LBP), local directional pattern (LDP), local directional number pattern (LDNP), angular local directional pattern (ALDP), local optimal-oriented pattern (LOOP), support vector machine (SVM), K-nearest neighbour (KNN) and back propagation neural network (BPNN) are used for emotion classification. The results indicate that ALDP + Polynomial SVM on MUFE, JAFFE and Yale Face databases gives better accuracy with 96.00%, 94.44% and 89.00%, respectively.
KALE: When Energy-Based Learning Meets Adversarial Training.

We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability mass on the learned support. Both the energy function and base jointly constitute the final model, unlike GANs, which retain only the base distribution (the generator). GEBMs are trained by alternating between learning the energy and the base. We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base. Samples from the posterior on the latent space of the trained model can be obtained via MCMC, thus finding regions in this space that produce better quality samples. Empirically, the GEBM samples on image-generation tasks are of much better quality than those from the learned generator alone, indicating that all else being equal, the GEBM will outperform a GAN of the same complexity. When using normalizing flows as base measures, GEBMs succeed on density modelling tasks, returning comparable performance to direct maximum likelihood of the same networks.
Optimization of Functions of Two Variables by Deformed Stars Method

this paper is devoted to the consideration of optimization problem for function of two variables, which, in the general case, is poly-extremal and undifferentiated. One of the most commonly used methods of solving this problem are evolutionary algorithms. A deformed stars method was developed, where the population of solutions is 3-, 4-, and 5points groups. The advantages of the developed method in comparison of genetic algorithms, evolutionary strategies and differential evolution as the most typical evolutionary algorithms are shown.