Reviewerly/exHarmony huggingface dataset:
Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network

With the development of artificial intelligence technology, intelligent fringe processing is a goal of relevant researchers in optical interferometry. We propose an intelligent method to achieve fully automated extraction of the fringe skeletons in electronic speckle pattern interferometry (ESPI) based on U-Net convolutional neural network. In the proposed method, the network is first trained by the samples that consist of the noisy ESPI fringe patterns and the corresponding skeleton images. After training, the other multiframe ESPI fringe patterns are fed to the trained network simultaneously; the corresponding skeleton images can be obtained in batches. Using our method, it is not necessary to process fringe patterns frame by frame. Our method is especially suitable for multiframe fringe patterns processing. We apply the intelligent method to one computer-simulated and one real-dynamic ESPI measurement, respectively. For the simulated measurement, it takes just 40 s to obtain the skeleton images of 20 noisy ESPI fringe patterns using our method. Even for low-quality experimental obtained ESPI fringe patterns, our method can also give desired results.
Total Variation Regularization of Pose Signals With an Application to 3D Freehand Ultrasound

Three-dimensional freehand imaging techniques are gaining wider adoption due to their ?exibility and cost ef?ciency. Typical examples for such a combination of a tracking system with an imaging device are freehand SPECT or freehand 3D ultrasound. However, the quality of the resulting image data is heavily dependent on the skill of the human operator and on the level of noise of the tracking data. The latter aspect can introduce blur or strong artifacts, which can signi?cantly hamper the interpretation of image data. Unfortunately, the most commonly used tracking systems to date, i.e. optical and electromagnetic, present a trade-off between invading the surgeon's workspace (due to line-of-sight requirements) and higher levels of noise and sensitivity due to the interference of surrounding metallic objects. In this work, we propose a novel approach for total variation regularization of data from tracking systems (which we term pose signals) based on a variational formulation in the manifold of Euclidean transformations. The performance of the proposed approach was evaluated using synthetic data as well as real ultrasound sweeps executed on both a Lego phantom and human anatomy, showing signi?cant improvement in terms of tracking data quality and compounded ultrasound images. Source code can be found at https://github.com/IFL-CAMP/pose_regularization.
Privacy preserving serial publication of transactional data

The continuous release of data, also called serial publication is critical for data analytics but it can lead to severe privacy disclosures via composition attacks. The serial publication often consists of several corpora and each corpus is an update of the previous one. While each individually published corpus may be privacy preserving, when considered together the whole serial publication may be at risk of privacy disclosures. Existing solutions addressing this problem often afford the privacy guarantees of k-anonymity and l-diversity which are prone to attribute disclosures via skewness attacks, and they focus only on relational data. This paper addresses the serial publication problem in the transactional data setting. First, we model the privacy disclosure risks associated with serially published data probabilistically. We then develop a rigorous privacy guarantee and a serial publication method Sanony that satisfies the privacy guarantee without excessive utility loss. We evaluate our method on two benchmark datasets and the results show our framework affords stronger privacy with much lower perturbation rates than existing state-of-the-art techniques.
An innovative approach to autocorrecting grammatical errors in Arabic texts

Natural Language Processing (NLP) has been a growing area of research in computer and cognitive sciences, using experimental approaches. Morphology and syntax play specifically a vital role in the correct interpretation of a sentence. In this paper, we will present a syntactic error correction system based on the automatic generation of correct sentences in Arabic. First, we extract the words from the considered sentence and we then generate all the possible sentences that are syntactically correct; based on a logical description of the rules of Arabic grammar in the ontology. We will afterwards compare the original sentence with the generated sentences to detect any eventual errors followed by the correction phase. In case the system has not found a sentence that looks similar to the original sentence, the correct alternative sentences are automatically offered. The use of the Arabic syntactic corrector can increase productivity and improve the quality of the text for anyone who writes in the Arabic language. Successful tests have been performed using a set of Arabic sentences. The implemented system achieved a precision rate of about 92% and a recall rate of about 84%. By observing the achieved results, it is concluded that our approach is promising.
In-Plane Movement Measurement Technique Using Digital Sampling Moiré Method

Digital sampling moiré (DSM) method is a vision-based technique that uses phase differences of the moiré fringes to measure two-dimensional (2D) translations. However, it assumes no in-plane rotation is present, and its accuracy would be affected if rotation is involved with translation. To decompose translation and rotation, we propose an enhanced DSM (EDSM) method in this study. First, the EDSM method used the derivatives of phase values to calculate the in-plane rotation angle, and then employed the 2D rigid transformation algorithm to compute the translation. We performed laboratory tests to compare the proposed EDSM method with the DSM method and the photogrammetric method using the Harris corner detector. The results revealed that the EDSM method outperforms the DSM method when rotation is applied in the motion, and the EDSM method is more robust to the image scaling factor compared with the photogrammetric method. To verify its applicability, we then applied the EDSM method to track displacements of a steel beam and a bamboo bridge. This study provides an accurate and robust method for structures’ in-plane motion measurement.
Fine-grained Type Inference in Knowledge Graphs via Probabilistic and Tensor Factorization Methods

Knowledge Graphs (KGs) have been proven to be incredibly useful for enriching semantic Web search results and allowing queries with a well-defined result set. In recent years much attention has been given to the task of inferring missing facts based on existing facts in a KG. Approaches have also been proposed for inferring types of entities, however these are successful in common types such as 'Person', 'Movie', or 'Actor'. There is still a large gap, however, in the inference of fine-grained types which are highly important for exploring specific lists and collections within web search. Generally there are also relatively fewer observed instances of fine-grained types present to train in KGs, and this poses challenges for the development of effective approaches. In order to address the issue, this paper proposes a new approach to the fine-grained type inference problem. This new approach is explicitly modeled for leveraging domain knowledge and utilizing additional data outside KG, that improves performance in fine-grained type inference. Further improvements in efficiency are achieved by extending the model to probabilistic inference based on entity similarity and typed class classification. We conduct extensive experiments on type triple classification and entity prediction tasks on Freebase FB15K benchmark dataset. The experiment results show that the proposed model outperforms the state-of-the-art approaches for type inference in KG, and achieves high performance results in many-to-one relation in predicting tail for KG completion task.
An exploratory analysis approach for understanding variation in stochastic textured surfaces

Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochastic nature. One challenge is that it is not straightforward how to define distances or dissimilarities between surface samples. An approach is developed for understanding variation in stochastic textured surfaces by deriving a new pairwise dissimilarity measure between surface samples and using these dissimilarities within a manifold learning framework to discover a low-dimensional parameterization of the surface variation patterns. Visualizing how the stochastic nature of the surfaces changes as the manifold parameters are varied helps build an understanding of the individual physical characteristic of each variation pattern. The approach is demonstrated with simulation and textile examples, in which the physical characteristics of the variation patterns are clearly revealed. The computer codes for implementing the approach are available in the spc4sts R package.
Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.
Experience and Perspectives of Electrodeposition from Electrolytes-Colloids of Nickel Plating

The investigation and use of colloid-electrolyte baths from the time of first, publications had proceeded mainly in the field of working out highly efficient baths with low concentrations of electrolytes in connection with acute ecological necessity, saving energy and profit. We call briefly colloid-electrolytes such plating baths, where metal deposits are formed by reducing not only metal ions but also colloids and fine dispersions of this metal compounds. This dispersions in the field of the cathode (in the part of diffusion layer where the rate of stirring usually tends to zero) may be treated in certain optimal cases as the systems of mobile pores, giving rise to electrokinetic phenomena, equilibrium and nonequilibrium and thus to efficient stirring this part of diffusion layer, difficulties of the electrochemically reacting species transfer through which are the main obstacle for the cathode processes intensification. It results in reducing or eliminating the diffusion control and gives the essential rise of the limiting current densities. There was observed in some cases for chloride colloid electrolytes the correlation between decrease of polarization in comparison with conventional baths and degree of operating current densities increase. In chloride solutions colloids and fine dispersions, especially protected ones, are more stable than in solutions, where multi charged anions prevail. The polarization's decrease in chloride electrolytes was investigated also. We tend to explain the referred to phenomena by lessening the diffusion control in presence of mobile pore's systems of reducing to metal at the cathode colloids and fine dispersions of that metal's compounds.
DPavatar: A Real-Time Location Protection Framework for Incumbent Users in Cognitive Radio Networks

Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both US and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time, and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU's location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms.
A Web Browser Fingerprinting Method Based on the Web Audio API

Abstract Web Browser Fingerprinting is a process in which the users are, with high likelihood, uniquely identified by the extracted features from their devices, generating an identifier key (fingerprint). Although it can be used for malicious purposes, especially regarding privacy invasion, Web Browser Fingerprinting can also be used to enhance security (e.g. as a factor in two-factor authentication). This paper investigates the use of Web Audio API as a Web Browser Fingerprinting method capable of identifying the devices. The idea is to prove or not if audio can provide features capable to identify users and devices. Our initial results show that the proposed method is capable of identifying the device’s class, based on features like device’s type, web browser’s version and rendering engine.
The long path to narrative generation

Narrative generation, understood as the task of constructing computational models of the way in which humans build stories, has been shown to involve a number of separate processes, related to different purposes to which it can be applied, and focusing on specific features that make stories valuable. This paper reviews a set of story generation systems developed by the authors of this contribution, each focusing on different aspects and functions of stories. These systems provide an initial breakdown of how the term “storytelling” might be either instantiated or broken down into component processes. The systems cover functionalities such as generating valid plot structures, simulating character's behaviors or the evolution of affinities between them, either reporting or fictionalizing events observed in real life, and revising a story draft to maximize the suspense it induces in its readers. These functionalities are not intended to exhaust the set of possible operations involved in storytelling, but they constitute an initial set to understand the complexity of the task. The paper also includes two proposals—one theoretical and one technological—for understanding how a set of such functionalities might be composed into a broader operational process that produces more elaborate stories.
Automated Recommendation of Related Model Elements for Domain Models

Domain modeling is an important activity in the early stages of software projects to achieve a common understanding of the problem area among project participants. Domain models describe concepts and relationships of respective application fields using a modeling language and domain-specific terms. Creating these models requires software engineers to have detailed domain knowledge and expertise in model-driven development. Collecting domain knowledge is a time-consuming manual process that is rarely supported in current modeling environments. In this paper, we describe an approach that supports domain modeling through formalized knowledge sources and information extraction from text. On the one hand, domain-specific terms and their relationships are automatically queried from existing knowledge bases. On the other hand, as these knowledge bases are not extensive enough, we have constructed a large network of semantically related terms from natural language data sets containing millions of one-word and multi-word terms and their quantified relationships. Both approaches are integrated into a domain model recommender system that provides context-aware suggestions of model elements for virtually every possible domain. We report on the experience of using the recommendations in various industrial and research environments.
Geochemical modelling and mapping of Cu and Fe anomalies in soil using combining sequential Gaussian co-simulation and local singularity analysis: a case study from Dedeyazı (Malatya) region, SE Turkey

Mapping and modelling of the spatial distribution of geochemical anomalies is a key task for geochemical exploration. This case study explores mapping and modelling of Cu and Fe geochemical anomalies combining the Sequential Gaussian Co-simulation (SGCS) method and Local Singularity Analysis (LSA) in the Malatya region, SE Turkey. A total of 652 topsoil samples were collected on a regular grid design from the study area. A cell declustering technique was applied to the raw data. Both geometric and zonal anisotropy exist in the directional variogram of Cu and Fe. SGCS was applied to create maps representing an equally probable spatial distribution of Cu and Fe geochemical anomalies. SGCS results have been validated by a variety of tests including the reproduction of the variograms, histograms, descriptive statistics and contour plots. LSA is based on a sliding window estimation approach applied to the grid data created for SGCS realization. LSA was carried out and mapped via the results of the realization on the same grid layout. Critical anomaly threshold values of the variables were identified using the singularity and quantile plot. Integration of SGCS and LSA results provides quantification of the uncertainty of spatial distribution and determination of Cu and Fe critical thresholds.
Solar Radiation Modeling from Satellite Imagery

Satellites have been observing the earth-atmosphere system and delivering data since the 1960s. They constitute a crucial tool for observing, measuring, and understanding meteorological phenomena and radiative transfer budgets. Satellite observations are usually incorporated into numerical weather predictions (NWP) models, via data assimilation algorithms, to produce the best estimate of the atmospheric state and to improve weather forecasting. In the field of solar radiation, satellite imagery provides effective information since onboard sensors actually measure the incoming radiance form the earth-atmosphere system. The radiance received by satellites is related to solar radiation incident at the earth’s surface since it results from the different interactions of the sun’s radiation with the earth-atmosphere system—scattering, absorption, and reflection. Therefore, it is reasonable to design methods and algorithms to infer surface solar irradiance from the radiance received by the satellites’ onboard instruments. The first algorithms were developed in the 1980s. They have significantly evolved since and have reached a high degree of maturity and accuracy thanks to continuous developments and improvements both in the methods and in onboard radiometric instrumentations (in particular, spectral and geographical resolution). Every solar energy project requires an accurate knowledge of the local solar resource. Although the number and geographical density of ground-based solar radiation sensors are continuously increasing, they can only supply the needed solar resource information in a handful of locations. In consequence, most solar energy projects rely on solar irradiance time series simulated from satellite imagery. Satellite-derived information can be processed into many forms useful to the solar community, including solar radiation maps, typical meteorological (or representative) years, long-term characterization, and solar plant performance modeling. This chapter presents a state-of-the-art review of the modeling of surface solar irradiance from satellite images.
Doppelgängers on the Dark Web: A Large-scale Assessment on Phishing Hidden Web Services

Anonymous network services on the World Wide Web have emerged as a new web architecture, called the Dark Web. The Dark Web has been notorious for harboring cybercriminals abusing anonymity. At the same time, the Dark Web has been a last resort for people who seek freedom of the press as well as avoid censorship. This anonymous nature allows website operators to conceal their identity and thereby leads users to have difficulties in determining the authenticity of websites. Phishers abuse this perplexing authenticity to lure victims; however, only a little is known about the prevalence of phishing attacks on the Dark Web.
Unsupervised texture reconstruction method using bidirectional similarity function for 3-D measurements

The developments of three-dimensional (3-D) imaging and display technology have created important demands for 3-D model with realistic texture. Due to texture misalignment and inconsistent illumination, texture reconstruction is subject to degradation like blurring, ghosting and color discontinuity. A composite weight texture blending approach is proposed to mitigate the artifacts. For inaccurate geometric model, bidirectional similarity function based multi-scale energy minimization method is proposed to correct texture misalignment. The average processing time of the proposed method is only 40% of existing method. The optical experiments demonstrate that photorealistic texture could be achieved for inaccurate geometric model and inconsistent illumination.
Diffusion-based cooperative space object tracking

We propose the diffusion-based enhanced covariance intersection cooperative space object tracking (DeCiSpOT) filter. The main advantage of the proposed DeCiSpOT algorithm is that it can balance the computational complexity and communication requirements between different sensors as well as improve track accuracy when measurements do not exist or are of low accuracy. Instead of using the standard covariance intersection in the diffusion step, the enhanced diffusion strategy integrates the 0-1 weighting covariance intersection strategy and the iterative covariance intersection strategy. The proposed DeCiSpOT algorithm also uses the global nearest neighbor and probabilistic data association for multiple space object tracking. Two typical scenarios including cooperative single and multiple space object tracking are used to demonstrate the performance of the proposed DeCiSpOT filter. Using simulated ground-based electro-optical (EO) measurements for multiple resident space objects and multiple distributed EO sensors, the DeCiSpOT archived results comparable to an optimal centralized approach. The results demonstrate that the DeCiSpOT is effective for space object tracking problem with results close to the optimal centralized cubature Kalman filter.