Reviewerly/exHarmony huggingface dataset:
An agent composition framework for the J-Park Simulator - A knowledge graph for the process industry

Digital twins, Industry 4.0 and Industrial Internet of Things are becoming ever more important in the process industry. The Semantic Web, linked data, knowledge graphs and web services/agents are key technologies for implementing the above concepts. In this paper, we present a comprehensive semantic agent composition framework. It enables automatic agent discovery and composition to generate cross-domain applications. This framework is based on a light-weight agent ontology, OntoAgent, which is an adaptation of the Minimal Service Model (MSM) ontology. The MSM ontology was extended with grounding components to support the execution of an agent while keeping the compatibility with other existing web service description standards and extensibility. We illustrate how the comprehensive agent composition framework can be integrated into the J-Park Simulator (JPS) knowledge graph, for the automatic creation of a composite agent that simulates the dispersion of the emissions of a power plant within a selected spatial area.
Maximum correntropy generalized high-degree cubature Kalman filter with application to the attitude determination system of missile

For SINS/CNS integrated navigation system, the CKF can perform well for state estimation in Gaussian noise. However, its performances are likely to degrade significantly under non-Gaussian noise conditions. To improve the robustness of the CKF against non-Gaussian noise, we propose an improved cubature Kalman filter, called the maximum correntropy generalized high-degree CKF (MCGHCKF). In the MCGHCKF, the generalized high-degree cubature rule is used to improve the filtering performance, and the maximum correntropy criterion is utilized to reduce the influence of non-Gaussian noise on state estimation. Simulation experiments illustrate the effectiveness and robustness of our algorithm.
Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
Neural-symbolic integration and the Semantic Web

Symbolic Systems in Artificial Intelligence which are based on formal logic and deductive reasoning are fundamentally different from Artificial Intelligence systems based on artificial neural networks, such as deep learning approaches. The difference is not only in their inner workings and general approach, but also with respect to capabilities. Neural-symbolic Integration, as a field of study, aims to bridge between the two paradigms. In this paper, we will discuss neural-symbolic integration in its relation to the Semantic Web field, with a focus on promises and possible benefits for both, and report on some current research on the topic.
J-Park Simulator: An ontology-based platform for cross-domain scenarios in process industry

The J-Park Simulator (JPS) acts as a continuously growing platform for integrating real-time data, knowledge, models, and tools related to process industry. It aims at simulation and optimization in cross-domain and multi-level scenarios and relies heavily on ontologies and semantic technologies. In this paper, we demonstrate the interoperability between different applications in JPS, introduce new domain ontologies into the JPS, and integrate live data. For this, we utilize a knowledge graph to store and link semantically described data and models and create agents wrapping the applications and updating the data in the knowledge graph dynamically. We present a comprehensive industrial air pollution scenario, which has been implemented as part of the JPS, to show how knowledge graphs and modular domain ontologies support the interoperability between agents. We show that the architecture of JPS increases the interoperability and flexibility in cross-domain scenarios and conclude that the potential of ontologies outweighs additional wrapping efforts.
AN EFFICIENT SECURITY FRAMEWORK FOR DATA MIGRATION IN A CLOUD COMPUTING ENVIRONMENT

Cloud computing is advantageous in several applications. Data migration is constantly carried out to hybrid or public cloud. Certain large enterprises will not move their business-critical data and applications to the cloud. This is due to the concerns regarding data security and privacy protection. In this paper, we provide a data security analysis and solution for privacy protection framework during data migration. A Secure Socket Layer (SSL) is established and migration tickets with minimum privilege is introduced. Further, data encryption is done using Prediction Based Encryption (PBE). This system will be of use for healthcare systems and e-commerce systems that can store data regarding credit card details. We provide a strict separation between sensitive and non-sensitive data and provide encryption for the sensitive data.
Sustainable Construction Safety Knowledge Sharing: A Partial Least Square-Structural Equation Modeling and A Feedforward Neural Network Approach

Most studies focused on the introduction of new technologies have not investigated the psychological factors affecting the willingness to use them or conducted empirical studies to explore whether willingness and actual construction safety knowledge-sharing behavior are associated with fewer construction incidents. We conducted face-to-face and LinkedIn open-ended interviews as well as a global survey to study the willingness and actual behavior to share construction knowledge via social software Web 2.0, Internet of Things (IoT) and mobile apps. Then, the Partial Least Square-Structural Equation Model (PLS-SEM) for willingness and actual knowledge-sharing behavior, as well as the Multilayer Perceptron (MLP) Neural Network were used to illustrate the effect of various factors on predicting the willingness to share knowledge via Web 2.0, mobile apps and IoT. Results of the interviews found that practitioners use IoT for knowledge sharing, mainly because they do not want to fall behind the curve. PLS-SEM and MLP revealed that practitioners share construction safety knowledge are not driven by safety-related reasons such as safety awareness enhancement but perceived organization support from their companies. Employees who agree that their organization cared about their employees’ well-being was the strongest predictor in influencing people’s decision to use tools for knowledge sharing. Moreover, many respondents claimed that factors such as monetary rewards have little impact on motivating people to use tools for knowledge sharing.
ActiTouch

Contemporary AR/VR systems use in-air gestures or handheld controllers for interactivity. This overlooks the skin as a convenient surface for tactile, touch-driven interactions, which are generally more accurate and comfortable than free space interactions. In response, we developed ActiTouch, a new electrical method that enables precise on-skin touch segmentation by using the body as an RF waveguide. We combine this method with computer vision, enabling a system with both high tracking precision and robust touch detection. Our system requires no cumbersome instrumentation of the fingers or hands, requiring only a single wristband (e.g., smartwatch) and sensors integrated into an AR/VR headset. We quantify the accuracy of our approach through a user study and demonstrate how it can enable touchscreen-like interactions on the skin.
Field trial of a three-state quantum key distribution scheme in the Florence metropolitan area

Abstract In-field demonstrations in real-world scenarios boost the development of a rising technology towards its integration in existing infrastructures. Although quantum key distribution (QKD) devices are already adopted outside the laboratories, current field implementations still suffer from high costs and low performances, preventing this emerging technology from a large-scale deployment in telecommunication networks. Here we present a simple, practical and efficient QKD scheme with finite-key analysis, performed over a 21 dB-losses fiber link installed in the metropolitan area of Florence (Italy). Coexistence of quantum and weak classical communication is also demonstrated by transmitting an optical synchronization signal through the same fiber link.
LiFF: Light Field Features in Scale and Depth

Feature detectors and descriptors are key low-level vision tools that many higher-level tasks build on. Unfortunately these fail in the presence of challenging light transport effects including partial occlusion, low contrast, and reflective or refractive surfaces. Building on spatio-angular imaging modalities offered by emerging light field cameras, we introduce a new and computationally efficient 4D light field feature detector and descriptor: LiFF. LiFF is scale invariant and utilizes the full 4D light field to detect features that are robust to changes in perspective. This is particularly useful for structure from motion (SfM) and other tasks that match features across viewpoints of a scene. We demonstrate significantly improved 3D reconstructions via SfM when using LiFF instead of the leading 2D or 4D features, and show that LiFF runs an order of magnitude faster than the leading 4D approach. Finally, LiFF inherently estimates depth for each feature, opening a path for future research in light field-based SfM.
Affinity and class probability-based fuzzy support vector machine for imbalanced data sets

The learning problem from imbalanced data sets poses a major challenge in data mining community. Although conventional support vector machine can generally show relatively robust performance in dealing with the classification problems of imbalanced data sets, it treats all training samples with the same contribution for learning, which results in the final decision boundary biasing toward the majority class especially in the presence of outliers or noises. In this paper, we propose a new affinity and class probability-based fuzzy support vector machine technique (ACFSVM). The affinity of a majority class sample is calculated according to support vector description domain (SVDD) model trained only by the given majority class training samples in kernel space similar to that used for FSVM learning. The obtained affinity can be used for identifying possible outliers and some border samples existing in the majority class training samples. In order to eliminate the effect of noises, we employ the kernel k-nearest neighbor method to determine the class probability of the majority class samples in the same kernel space as before. The samples with lower class probabilities are more likely to be noises and their contribution for learning seems to be reduced by their low memberships constructed by combining the affinities and the class probabilities. Thus, ACFSVM can pay more attention to the majority class samples with higher affinities and class probabilities while reducing their effects of the ones with lower affinities and class probabilities, eventually skewing the final classification boundary toward the majority class. In addition, the minority class samples are assigned relative high memberships to guarantee their importance for the model learning. The extensive experimental results on the different imbalanced datasets from UCI repository demonstrate that the proposed approach can achieve better generalization performance in terms of G-Mean, F-Measure, and AUC as compared to the other existing imbalanced dataset classification techniques.
Try-on experience with augmented reality comforts your decision

Purpose The purpose of this paper is to understand how augmented reality (AR) try-on experiences facilitate consumers’ shopping decision. Focusing on the immersion and psychological ownership, the study investigated how the properties of AR experiences (environmental embedding (EE) and simulated physical control (SPC)) affect decision comfort. Design/methodology/approach This research theoretically and empirically analyzes how each property of AR experiences affects consequential psychological states and then further increases decision comfort by employing an existing AR try-on mobile application. A total of 99 valid responses were used for the partial least square structural equation modeling analysis. One’s prior AR try-on experience was predicted as a moderator and analyzed using SPSS-based PROCESS macro. Findings The results demonstrated that EE and SPC evoke immersion and the feeling of ownership of a virtual product, which increased decision comfort. The moderating effect of one’s prior AR try-on experience showed that the impact of EE and SPC on immersion was attenuated for those with prior experience. Further, immersion mediated the effect of EE but SPC on the feeling of ownership, which corroborated the direct effect of SPC on the feeling of ownership. Practical implications Firms must consider technological and user-experience features that can induce users to perceive high levels of AR characteristics such as EE and SPC. Practitioners should develop realistic content that can correctly place virtual products on users to enhance EE. Including more interactive features is encouraged to provide users with a feeling of control toward the virtual product that directly leads to ownership and positively affects decision making. Further, practitioners need to be cautious about consumers getting used to the new technology; retailers and marketers need to focus on creating new and innovative content to continually engage customers. Originality/value This study adopted EE and SPC to determine how each property of AR experience forms the consequential psychological states, particularly depending on one’s prior experience. Methodologically, the study provided external validity in conducting an experiment by adopting an existing AR mobile application available in the market and employing an objective measure of respondents (e.g. prior AR try-on experience).
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes

Existing 3D scene flow estimation methods provide the 3D geometry and 3D motion of a scene and gain a lot of interest, for example in the context of autonomous driving. These methods are traditionally based on a temporal series of stereo images. In this paper, we propose a novel monocular 3D scene flow estimation method, called Mono-SF. Mono-SF jointly estimates the 3D structure and motion of the scene by combining multi-view geometry and single-view depth information. Mono-SF considers that the scene flow should be consistent in terms of warping the reference image in the consecutive image based on the principles of multi-view geometry. For integrating single-view depth in a statistical manner, a convolutional neural network, called ProbDepthNet, is proposed. ProbDepthNet estimates pixel-wise depth distributions from a single image rather than single depth values. Additionally, as part of ProbDepthNet, a novel recalibration technique for regression problems is proposed to ensure well-calibrated distributions. Our experiments show that Mono-SF outperforms state-of-the-art monocular baselines and ablation studies support the Mono-SF approach and ProbDepthNet design.
Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks

Gaze redirection is the task of changing the gaze to a desired direction for a given monocular eye patch image. Many applications such as videoconferencing, films, games, and generation of training data for gaze estimation require redirecting the gaze, without distorting the appearance of the area surrounding the eye and while producing photo-realistic images. Existing methods lack the ability to generate perceptually plausible images. In this work, we present a novel method to alleviate this problem by leveraging generative adversarial training to synthesize an eye image conditioned on a target gaze direction. Our method ensures perceptual similarity and consistency of synthesized images to the real images. Furthermore, a gaze estimation loss is used to control the gaze direction accurately. To attain high-quality images, we incorporate perceptual and cycle consistency losses into our architecture. In extensive evaluations we show that the proposed method outperforms state-of-the-art approaches in terms of both image quality and redirection precision. Finally, we show that generated images can bring significant improvement for the gaze estimation task if used to augment real training data.
Fast and Practical Neural Architecture Search

In this paper, we propose a fast and practical neural architecture search (FPNAS) framework for automatic network design. FPNAS aims to discover extremely efficient networks with less than 300M FLOPs. Different from previous NAS methods, our approach searches for the whole network architecture to guarantee block diversity instead of stacking a set of similar blocks repeatedly. We model the search process as a bi-level optimization problem and propose an approximation solution. On CIFAR-10, our approach is capable of design networks with comparable performance to state-of-the-arts while using orders of magnitude less computational resource with only 20 GPU hours. Experimental results on ImageNet and ADE20K datasets further demonstrate transferability of the searched networks.
An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system.
Distributed Vector-OLE

We investigate concretely efficient protocols for distributed oblivious linear evaluation over vectors (Vector-OLE). Boyle et al. (CCS 2018) proposed a protocol for secure distributed pseudorandom Vector-OLE generation using sublinear</>communication, but they did not provide an implementation. Their construction is based on a variant of the LPN assumption and assumes a distributed key generation protocol for single-point Function Secret Sharing (FSS), as well as an efficient batching scheme to obtain multi-point FSS. We show that this requirement can be relaxed, resulting in a weaker variant of FSS, for which we give an efficient protocol. This allows us to use efficient probabilistic batch codes that were also recently used for batched PIR by Angel et al. (S&P 2018). We construct a full Vector-OLE generator from our protocols, and compare it experimentally with alternative approaches. Our implementation parallelizes very well, and has low communication overhead in practice. For generating a VOLE of size $2^20 $, our implementation only takes $0.52$s on 32 cores.
Semi-supervised multi-view clustering with Graph-regularized Partially Shared Non-negative Matrix Factorization

Non-negative matrix factorization is widely used in multi-view clustering due to its ability of learning a common dimension-reduced factor. Recently, it is combined with the label information to improve the clustering, but the affection of the dimension-reduction to the classes of the labeled data is seldom considered. Motivated by that the graph constraint can keep the geometric structure of the data, it is employed to restrict the class variation of the data caused by the dimension reduction, and a semi-supervised method called Graph-regularized Partially Shared Non-negative Matrix Factorization (GPSNMF) is proposed for multi-view clustering in this paper. In our method, the affinity graph of each view is constructed to encode the geometric information, and the corresponding multiplication update algorithm based on alternative iteration rule is derived. In the experiments, two clustering approaches are tested based on the results of the proposed GPSNMF, and four real-world databases with different label proportions are performed to demonstrate the advantages of our method over the state-of-the-art methods.
High capacity adaptive image steganography with cover region selection using dual-tree complex wavelet transform

The importance of image steganography is unquestionable in the field of secure multimedia communication. Imperceptibility and high payload capacity are some of the crucial parts of any mode of steganography. The proposed work is an attempt to modify the edge-based image steganography which provides higher payload capacity and imperceptibility by making use of machine learning techniques. The approach uses an adaptive embedding process over Dual-Tree Complex Wavelet Transform (DT-CWT) subband coefficients. Machine learning based optimization techniques are employed here to embed the secret data over optimal cover-image-blocks with minimal retrieval error. The embedding process will create a unique secret key which is imperative for the retrieval of data and need to be transmitted to the receiver side via a secure channel. This enhances the security concerns and avoids data hacking by intruders. The algorithm performance is evaluated with standard benchmark parameters like PSNR, SSIM, CF, Retrieval error, BPP and Histogram. The results of the proposed method show the stego-image with PSNR above 50 dB even with a dense embedding of up to 7.87 BPP. This clearly indicates that the proposed work surpasses the state-of-the-art image steganographic systems significantly.