Reviewerly/exHarmony huggingface dataset:
Json-GUI—A module for the dynamic generation of form-based web interfaces

Json-GUI is an AngularJS front-end module that dynamically generates form-based web interfaces. Starting from a formal JSON configuration object describing a list of inputs, Json-GUI is able to build a form frame interface at runtime, with standard and personalized validation rules, giving the possibility to define constraints between input fields. Validated data are stored as Json objects or text files. Json-GUI has been exploited by scientific communities to effectively reduce the development and maintenance of customized user interfaces in science gateways. Moreover, Json-GUI can also be employed in the development of general-purpose Web forms.
Fast Eclat Algorithms Based on Minwise Hashing for Large Scale Transactions

The Eclat algorithm is one of the most widely used frequent itemset mining methods. In the normal Eclat algorithm and its variants, it is inefficient to calculate the intersection size of itemsets by sequentially comparing elements, especially for large scale transactions. In this paper, we propose the fast Eclat algorithms that can quickly calculate the intersection size of multiple itemsets by using minwise hashing and the estimators. Minwise hashing is used to calculate the Jaccard similarity coefficient by mapping the elements of the sets to those of smaller sets. Two estimators are used to estimate the intersection size of itemsets based on the Jaccard similarity coefficient. Due to the “imperfect” hash function, minwise hashing may obtain a biased Jaccard similarity, which results in error between the real value and the estimated value of the intersection size. Thus, we proposed the HashEclat which uses the maximum of |A| and |B| to represent the union size |A U B|, and proposed the Sim-Eclat which uses the minimum of |A| and |B| to represent the intersection size |A fl B|. Furthermore, we use a boundary error E for better performance as follows: if E is large, the intersection size is determined by a traditional method, and the result is more accurate but takes longer to compute; otherwise, it will be the opposite. Both the theoretical analysis and experimental results show that the proposed algorithms can obtain almost all frequent itemsets with higher speed and less memory usage than other algorithms.
Distance-based customer detection in fake follower markets

As the Online Social Networks (OSNs) have become popular, more and more people want to increase their influence not only in the real world but also in the OSNs. However, increasing the influence in OSNs is time-consuming job, so some users want to find a shortcut to increase their relationships. The demand for quick increasement of relationship has led to the growth of the fake follower markets that cater to customers who want to grow their relationships rapidly. However, customers of fake follower markets cannot manipulate legitimate user's relationship perfectly. Existing approaches explore node's relationships or features to detect customers. But none of them combines the relationships and node's features directly. In this article, we propose a model that directly combines the relationship and node's feature to detect customers of fake followers. Specifically, we study the geographical distance for 1-hop-directional links using the nodes geographical location. Motivated by the difference of a distance ratio for 1-hop directional links, the proposed method is designed to generate a 1-hop link distance ratio, and classifies a node as a customer or not. Experimental results on a Twitter dataset demonstrate that the proposed method achieves higher performance than other baseline methods.
Risk Evaluation for Bridge Engineering Based on Cloud-Clustering Group Decision Method

A combined analytic hierarchy process (AHP) method, Delphi method, and cloud model, i.e., a cloud clustering–based group decision method, is proposed. The risk probability and risk loss of the main risk factors are scored by experts. A backward cloud generator is used to calculate the digital characteristics of the sample from the experts' scoring. A forward cloud generator is used to generate the cloud droplets according to the scoring samples from the experts. An iteration method is approved to quantify the coherence. Introduction of the expert authority, group decision risk probability, and risk loss can be obtained based on the cloud model. Similarly, the group decision weight of risk resources can also be obtained by the cloud-clustering group decision method. According to the risk resource weight, risk probability, and risk loss, risk level can be determined based on the risk judgment matrix. The study results show that the risk level of the example cable-stayed bridge is Level II, which is very similar to the computation achieved by a method derived from the current safety risk assessment guide in China. However, cloud clustering can ensure higher coherence for the samples. Consequently, the risk evaluation result will be more stable. The real bridge example and comparison analysis show that the cloud-clustering group decision method is reasonable and can objectively estimate the bridge risk.
Weighted Convolutional Motion-Compensated Frame Rate Up-Conversion Using Deep Residual Network

Frame rate up-conversion (FRUC) usually suffers from unreliable motion vectors due to the absence of the current frame to be interpolated. In addition, since the majority of video sequences are usually compressed by various coding standards to reduce the data volume, the quality of the generated frames in the FRUC will be further impaired. To address this problem, we proposed two FRUC algorithms based on deep residual network. We first present a deep residual network for the FRUC (DRNFRUC), which consists of feature extraction, feature recursive analysis, and image restoration parts with a skip connection between the input and the output of the network. The proposed DRNFRUC takes the result of an arbitrary existing FRUC method as the input and is able to significantly reduce the edge blurring and blocking artifacts when the motion of the block is violent. In addition, we proposed a deep residual network with weighted convolutional motion compensation (DRNWCMC) for the FRUC, where the convolution operations can be embedded into the motion compensation interpolation (MCI) in any existing MCI-based FRUC method. In DRNWCMC, we first devise two convolutional neural networks corresponding to the forward and backward motion compensated frames, respectively. And then, the adaptive interpolation coefficients for motion compensation are designed as two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times1$ </tex-math></inline-formula> convolutional kernels. Finally, the interpolation result of WCMC is fed into another convolutional neural network to further improve the performance. All the parameters involved in the DRNWCMC are trained simultaneously under the same cost function. The experimental results show that the two proposed algorithms can remarkably improve both the objective and subjective quality of the interpolated frames.
A multi-aperture optical flow estimation method for an artificial compound eye

An artificial compound eye (ACE) is a bio-inspired vision sensor which mimics a natural compound eye (typical of insects). This artificial eye is able to visualize large fields of the outside world through multi-aperture. Due to its functioning, the ACE is subject to optical flow, that is an appare nt motion of the object visualized by the eye. This paper proposes a method to estimate the optical flow based on capturing multiple images (multi-aperture). In this method, based on descriptors-based initial optical flows, a unified global energy function is presented to incorporate the information of multi-aperture and simultaneously recover the optical flows of multi-aperture. The energy function imposes a compound flow fields consistency assumption along with the brightness constancy and piecewise smoothness assumptions. This formula efficiently binds the flow field in time and space, and further enables view-consistent optical flow estimation. Experimental results on real and synthetic data demonstrate that the proposed method recovers view-consistent optical flows crossed multi-aperture and performs better than other optical flow methods on the multi-aperture images.
Two Constructions of Asymptotically Optimal Quasi-Complementary Sequence Sets

An (M, K, N, δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> )-quasi-complementary sequence set (QCSS) is referred to as a set of M two-dimensional matrices of size K × N with maximum periodic correlation magnitude δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> . It can be applied to a multi-carrier code-division multiple-access communication system to achieve low-interference performance. Compared with perfect complementary sequence sets, QCSSs with maximum periodic correlation magnitudes achieving or asymptotically achieving the correlation lower bounds have the advantage of supporting more users. In this paper, two constructions of periodic QCSSs from additive characters and multiplicative characters of finite fields are developed. In the first construction, new QCSSs with constituent sequence length N = p are proposed by using cyclic classes, where p is a prime. In the second construction, QCSSs with constituent sequence length N = q - 1 and N = r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 1 are presented by employing almost difference sets and special sets proposed by Katz, respectively, where q > 5 is an odd prime power and r is a prime power. Notably, the parameters of QCSSs derived from the second construction are flexible and have not been covered in the literature. In addition, all the proposed periodic QCSSs are asymptotically optimal with respect to the correlation lower bounds.
New Technology for the Classroom

New developments in educational technologies are making it possible for teachers to bring technology into the classroom more than ever before. This chapter provides an overview of educational technologies related to mobile devices, artificial intelligence, intelligent tutoring systems, and robotics. As developments in computer technologies advance over time educators will need to cultivate better understanding and skills for using technology with a well conceptualized instructional design. Concepts are offered here to further discussions on how new technologies will support persistent and personalized learning in the not too distant future. Educational leaders must begin to think about on how to incorporate different technologies as well as potential resource constraints that school systems need to consider in order to achieve broader educational technology integration goals.
Co-PoeTryMe: Interactive poetry generation

PoeTryMe is a poetry generation system that autonomously produces poems from a set of initial parameters. After using it, creative writers and other users of this system expressed the desire to partake in the creative process by directly interacting with PoeTryMe. This paper is motivated by the previous impressions. It illustrates some of the challenges of automatic poetry generation, highlights limitations of PoeTryMe, and reports on recent efforts to address user feedback and provide alternative ways of using this system. First, making some functionalities available via a web API enabled their exploitation by other systems. Those include the generation of single lines as well as retrieval of related words, allowing for additional constraints on the number of syllables, sentiment and rhyme. On top of this API, a co-creative interface, Co-PoeTryMe, has been developed. Co-PoeTryMe allows users to either start from scratch or from a generated draft. Extensive editing functionality has been incorporated, in particular, allowing lines and words to be switched or replaced by newly generated alternatives. Co-PoeTryMe, its interface and the co-creative process for producing poetry are described, along with a user study that provided useful feedback. Users pointed out the strengths of this new system, including its capacities in providing inspiration, and giving the user the ability to customize an existing draft and visualize the changes, but also pointed out weaknesses, primarily by identifying potential improvements in the user interface.
Graph-based sparse linear discriminant analysis for high-dimensional classification

Linear discriminant analysis (LDA) is a well-known classification technique that enjoyed great success in practical applications. Despite its effectiveness for traditional low-dimensional problems, extensions of LDA are necessary in order to classify high-dimensional data. Many variants of LDA have been proposed in the literature. However, most of these methods do not fully incorporate the structure information among predictors when such information is available. In this paper, we introduce a new high-dimensional LDA technique, namely graph-based sparse LDA (GSLDA), that utilizes the graph structure among the features. In particular, we use the regularized regression formulation for penalized LDA techniques, and propose to impose a structure-based sparse penalty on the discriminant vector β. The graph structure can be either given or estimated from the training data. Moreover, we explore the relationship between the within-class feature structure and the overall feature structure. Based on this relationship, we further propose a variant of our proposed GSLDA to utilize effectively unlabeled data, which can be abundant in the semi-supervised learning setting. With the new regularization, we can obtain a sparse estimate of β and more accurate and interpretable classifiers than many existing methods. Both the selection consistency of β estimation and the convergence rate of the classifier are established, and the resulting classifier has an asymptotic Bayes error rate. Finally, we demonstrate the competitive performance of the proposed GSLDA on both simulated and real data studies.
Swarm Economy: A Model for Transactions in a Distributed and Organic IoT Platform

The Internet of Things (IoT) is steadily growing and migrating to decentralized architectures. In this context the Swarm approach, based on organic, edge-centric IoT networks, proposes one step further toward cooperation among devices, through the creation of a communication mediator called Swarm Broker. An important aspect in creating a cooperation network in the Swarm is the economical domain: the Swarm economy that guarantees fair rules to stimulate resource sharing among participants. This economic aspect is even more challenging in networks with distributed governance and motivates the adoption of a technology for a trustworthy and immutable public registry, such as blockchain, to store transaction information. In this paper, we aim to create an economic model that regulate the cooperation among the participants of the Swarm, by implementing strategies, to fulfill the restrictions posed by the decentralized network and the presence of resource-constrained devices that integrate the Swarm. We created a microeconomic model for resource sharing in the Swarm and a transaction model, based on the price and reputation of participants. We created a blockchain-based system for storing currency, contract, and reputation information in a distributed way. We included the resulting models and implementation into the Swarm network, by adding a module into the Swarm Broker.
Cointegrated linear processes in Bayes Hilbert space

Abstract A cointegrated linear process in Bayes Hilbert space is isomorphic to a cointegrated linear process in a Hilbert space of centered square-integrable real functions. We illustrate the use of this isomorphism for modeling nonstationary time series of probability densities.
Comparison of efficiency of techniques for delineating uni-element anomalies from stream sediment geochemical landscapes

This paper compares analyses of raw geochemical data and processed stream sediment geochemical data (i.e., background-corrected and dilution-corrected via the sample catchment basin approach (SCBA)). Both sets of analyses, based on multifractal approach, aimed to identify geochemical anomalies in three study regions in Iran with different geological characteristics and mineralization styles. The results of both analyses were validated for robustness by using the concept of normalized density (ND), which simultaneously considers both mineralized locations and area of anomalous zones. The comparison of the results emphasizes the efficiency of background and dilution correction procedures, which deal with stream sediment geochemical landscapes in the search for mineral deposits. This paper also presents results of discrete (catchment-based) and continuous (interpolation-based) field modelling of stream sediment geochemical dataset per region study. Although interpolation-based field modelling is rapid, time efficient and simple for delineation of geochemical anomalies, the results of discrete-based field modelling of geochemical anomalies are more superior. Therefore, regardless of the mineralization styles and geological settings, the SCBA to identify geochemical anomalies for mineral exploration is efficient.
RankGAN: A Maximum Margin Ranking GAN for Generating Faces

We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, the generators, with each subsequent stage without changing the network architecture. We call this proposed method the RankGAN. We first propose a margin-based loss for the GAN discriminator. We then extend it to a margin-based ranking loss to train the multiple stages of RankGAN. We focus on face images from the CelebA dataset in our work and show visual as well as quantitative improvements in face generation and completion tasks over other GAN approaches, including WGAN and LSGAN.
Reversible spatio-temporal perturbation for protecting location privacy

Ubiquitous deployment of low-cost mobile positioning devices and widespread use of high-speed wireless networks have resulted in a rapid growth of location-based applications. While location-based services provide a wide range of life enhancing experiences to users, the exposure of location information poses significant privacy risks that can invade the users’ location privacy. Location privacy risks can be mitigated using location anonymization techniques that perturb the raw location of users to make the location indistinguishable from that of a set of other users. A fundamental limitation of traditional location anonymization techniques is that they are developed as unidirectional techniques that fail to support multi-level control of access to location data when data users have different access privileges on the exposed location information. As a result, location information once perturbed cannot be reduced in terms of anonymity or degree of perturbation even when some data users have access to fine granular information in the exposed data. Recent techniques on reversible spatial cloaking techniques employ data anonymization keys to perturb a user’s location in a pseudo-random manner such that the anonymized location information can be de-anonymized later using the anonymization keys. While reversible spatial cloaking provides support for multi-level location privacy, their performance is limited by their adopted spatial cloaking model in which the location perturbation occurs solely in the spatial domain without considering the temporal domain. Hence, reversible spatial location cloaking techniques obtain lower success rate and lower spatial resolution of the perturbed location leading to unreliable anonymization and lower service quality. This paper presents a new suite of reversible cloaking techniques that reversibly perturb location information of users using a spatio-temporal cloaking model, allowing data perturbation to occur along both spatial and temporal dimensions while still ensuring that the spatio-temporal expansion process is reversible when suitable access keys are provided. The proposed model achieves higher success rate and higher spatial resolution compared to reversible spatial cloaking. We compare our techniques through extensive experiments on real road networks. The results show that our techniques offer better QoS performance than the existing approaches and demonstrate strong attack resilience against adversarial attacks.
Movement Modalities in Virtual Reality: A Case Study from Ocean Rift Examining the Best Practices in Accessibility, Comfort, and Immersion

The visceral immersion of virtual reality (VR) requires that developers rethink how we design, model, and interact with virtual worlds. One of the most important considerations is how the user moves around, and this has led to several movement modalities with various levels of abstraction. In this article, we explore movement modalities in the VR and examine how the various systems differ in terms of accessibility, comfort, and immersion. We then provide a case study on how we used these best practices in developing our underwater safari park experience Ocean Rift, which is one of the most popular VR applications across personal computers and mobile VRs.
DFP-ALC: Automatic video summarization using Distinct Frame Patch index and Appearance based Linear Clustering

Video summarization aims to create a succinct representation of videos for efficient browsing and retrieval. We propose an innovative method for the task. It includes two main steps: (i) the first step proposes a Distinct Frame Patch (DFP) index for selecting a set of good candidate frames, and (ii) the second step proposes a novel Appearance based Linear Clustering (ALC) to refine them for distinct ones. While the first step measures the content of frames, the second step considers to what extent one frame is different from another in both the spatial and temporal spaces. The experiments are performed over two publicly accessible datasets. The results show the effectiveness and efficiency of the proposed method when compared with other state-of-the-art techniques.
CNN-Based Multiple Path Search for Action Tube Detection in Videos

This paper presents an effective two-stream convolutional neural network (CNN)-based approach to detect multiple spatial-temporal action tubes in videos. A novel video localization refinement (VLR) scheme is first addressed to iteratively rectify the potentially inaccurate bounding boxes by exploiting the temporal consistency between adjacent frames. Then, to provide more faithful detection scores, a new fusion strategy is considered, which combines not only the appearance and the flow information of the two-stream networks but also the motion saliency, the latter of which is included to address the small camera motion. In addition, an efficient multiple path search (MPS) algorithm is developed to simultaneously identify multiple paths in a single run. In the forward message passing of MPS, each node stores information of a prescribed number of connections based on the accumulated scores determined in the previous stages. A backward path tracing is invoked afterward to find all multiple paths at the same time by fully reusing the information generated in the forward pass without repeating the search process. Thus, the complexity incurred can be reduced. The simulation results show that, together with VLR and the new fusion scheme, the proposed MPS, in general, can provide superior performance compared with the state-of-the-art works on four public datasets.