Reviewerly/exHarmony huggingface dataset:
High accurate three-dimensional shape measurement of discontinuous specular surfaces by stereo direct phase-measuring deflectometry

With the fast development of integrated circuits, photovoltaics, automobile industry, advanced manufacturing, and astronomy, it is particularly important to obtain the three-dimensional (3D) shape data of specular objects quickly and accurately. Owing to the advantages of large dynamic range, non-contact operation, full-field and fast acquisition, high accuracy, and automatic data processing, phase-measuring deflectometry (PMD, also called fringe reflection profilometry) has been widely studied and applied in many fields. Direct PMD (DPMD) can directly establish the relationship between phase and depth data without gradient integration process, so it can be used for 3D shape measurement of specular objects having discontinuous surfaces. However, compared with gradient data, depth data is more sensitive to measurement noise, so the measurement accuracy obtained by gradient field integration is higher. In order to make use of the anti-noise property of gradient measurement structure, some PMD techniques usually abandon the height data obtained from the intermediate process and use the gradient data to complete the surface reconstruction. Stereo deflectometry is a typical representative to retrieve 3D shape of the measured object according to the gradient integration. It calculates the gradient field based on the uniqueness of the normal vector of the object points by using two cameras. Although obtaining high-precision measurement results, this method cannot be used for the measurement of discontinuous objects. In this paper, a new stereo DPMD method is proposed to obtain 3D shape of specular objects having discontinuous surfaces. Some experimental results on measuring discontinuous specular objects verify the high precision of the proposed stereo DPMD method.
CPHD Tracking Algorithm Based on BPF for Multiple Group Targets

Abstract Aim to solve the tracking of multiple group targets in strong clutter environment, a rectangular box is used to deal with extension state of group targets. Combining the box particle filter algorithm and the cardinalized probability hypothesis density (CPHD) algorithm, a CPHD algorithm for multiple group targets tracking based on box particle filter (BPF), which is called BPF-CPHD, is proposed in this paper. Compared with the existed algorithm of multiple group targets tracking, the proposed algorithm can improve the tracking accuracy and efficiency greatly. Simulation results confirm the effectiveness of the BPF-CPHD.
An innovative approach for assessing the quality of images by using certain objective technique

Determining the eminence of the image is a difficult and firm procedure since human's belief is posh by substantial and emotional limitation. Numerous methods are anticipated for evaluating the calibre of the image but no one of it is measured to be ideal for finding the quality. Image quality evaluation acts a essential component in the area of image processing. This paper focused on no-reference method present in objective technique to judge the value of image. As No-reference image quality assessment (NR-IQA) is essential yet tough assignment in abstraction in reference to a computer vision area. To exhibit the efficacy of our move towards, broad tests are assessed on well-liked image quality assessment standard. This work functions well when compared other method stated in the comparative study.
Standard Deviation Clustering Combined with Visual Psychological Test Algorithm for Image Segmentation

Detection of the visual salient image area for image segmentation, image recognition, and adaptive compression application is beneficial. It makes an object, a person, or some pixels stand out against the background of the image and provide support for image recognition and target detection. The detection can simplify the process of computer visual image processing and improve the effect and efficiency of computer visual inspection. This paper introduces a kind of salient detection method, without any manual intervention, and uses the method of decomposing brightness, color space, negative map solution, and standard deviation to find the super-distance pixel in the image. The method of clustering is used to separate the region of objects and image background, and output RGB color salient objects image. Moreover, it can accurately highlight the object contour and internal pixels. This method studies the characteristics of the original pixels such as brightness or color and utilizes the image basis features to achieve the image saliency detection. It has high adaptive detection ability, low time complexity and high computational efficiency.
Generation of Efficient Rules for Associative Classification

Associative classification is a mining technique that integrates classification and association rule mining for classifying unseen data. Associative classification has been proved that it gives more accurate than traditional classifiers and generates useful rules which are easy to understand by a human. Due to inheriting from association rule mining, associative classification has to face a sensitive of minimum support threshold that a huge number of rules are generated when a low minimum support threshold is given. Some of the rules are not used for classification and need to be pruned. To eliminate unnecessary rules, this paper proposes a new algorithm to find efficient rules for classification. The proposed algorithm directly generates efficient rules. A vertical data representation technique is adopted to avoid the generation of unnecessary rules. Our experiments are conducted to compare the proposed algorithm with well-known algorithms, CBA and FACA. The experimental results show that the proposed algorithm is more accurate than CBA and FACA.
Modes of Acquisition and Models of Licensing

The advent of electronic sources of information (ESIs), e-resources, or electronic information resources (EIRs) has brought a revolution in the information landscape during 21st century. This has led to a paradigm shift not only in the modes and mechanism of storage, access, and delivery, but the acquisition of information has also witnessed a prominent change. This chapter is a theoretical overview of some newly emerging and popular modes of acquisition currently available for libraries to acquire and subscribe electronic information resources. Due to a visible change in the nature of information storage, access, and delivery, publishers have brought about certain changes in the policies governing the sale of their products in electronic form. As such, new license models have emerged, of which some popular ones have been enlisted for the benefit of libraries. The basic criteria needed to be set in force for prompt assessment of e-resources have also been highlighted at the end of this chapter.
A Coalgebraic Approach to Unification Semantics of Logic Programming

In the version of logic programming (LP) based on interpretations where variables occur in atoms, a goal reduction via unification can be seen as a transition labelled by the most general unifier. Categorically, it is thus natural to model a logic program as a coalgebra. In the paper we represent: (i) goals as the substitutive monoid freely generated by the predicate symbols; (ii) the LTS as the structured coalgebra defined by the SOS rules implicit in the LP semantics; (iii) the bisimulation semantics of a logic program as its image on the final coalgebra.
Selection of scientific articles according to the degree of proximity to the semantic pattern of the title and phrases of the abstract

Abstract The article is devoted to the problem of numerical evaluation of the proximity of a thematic text to the most rational (reference) language version of the description of the piece of knowledge it represents. This problem is relevant for the implementation of targeted selection of textual information without losing the useful semantic component. Examples of practical applications here can be the selection of articles for publication in scientific journals, as well as the development of training courses and educational portals. In the proposed solution, the basis for assessing the proximity of a text to a semantic pattern (i.e. sense standard) is the division of the words of each phrase into classes according to the value of the TF-IDF measure relative to the texts of the corpus pre-formed by an expert. The analyzed texts considered in the paper are the abstracts of scientific articles along with their titles. At the same time, the semantic images of the texts closest to the standard determine the words with the highest TF-IDF values, which, being neighbors in a linear series, are most likely related by meaning and form key combinations. The proposed numerical estimate of the proximity to the standard makes it possible to rank articles according to the significance of the described fragments of knowledge with respect to a given subject area, as well as to the non-redundancy of the description itself.
A Novel Access Control for Cloud Services Using Trust Based Design

In recent years the progress of cloud computing has become a complex system with diverse set of services and its user requirements. Access control plays a crucial role for accessing these services and assets of an organization via cloud servers. In, this paper we presented a novel access control technique known as Trust-RBAC which is based on non-security-oriented trust that provides a new dimension to prevent from unauthorized access to the cloud resources. To prove the efficiency of our approach, extensive simulations are been carried out in a cloud environment to show its effectiveness in controlling the malicious requests to the resources.
Joining Items Clustering and Users Clustering for Evidential Collaborative Filtering

Recommender Systems (RSs) are supporting users to cope with the flood of information. Collaborative Filtering (CF) is one of the most well-known approaches that have achieved a widespread success in RSs. It consists in picking out the most similar users or the most similar items to provide recommendations. Clustering techniques can be adopted in CF for grouping these similar users or items into some clusters. Nevertheless, the uncertainty comprised throughout the clusters assignments as well as the final predictions should also be considered. Therefore, in this paper, we propose a CF recommendation approach that joins both users clustering strategy and items clustering strategy using the belief function theory. In our approach, we carry out an evidential clustering process to cluster both users and items based on past preferences and predictions are then performed accordingly. Joining users clustering and items clustering improves the scalability and the performance of the traditional neighborhood-based CF under an evidential framework.
Isotropic Total Variation Minimization for Sub-Pixel Mapping

Hyperspectral imaging is an important source of land cover information by virtue of its spectral richness. However, this type of imagery is typically known by its coarse spatial resolution, that is a limiting factor for end-users. Although spectral unmixing techniques can provide subpixellic information by means of abundance fractions for each class in mixed pixels, the spatial distribution of these classes within each pixel is still unknown. Sub-pixel mapping techniques address the above mentioned problem. Nevertheless, the traditional sub-pixel mapping algorithms based on spatial dependence assumptions cannot solve these problems efficiently. Spatial regularization methods have recently been proposed in a way that they can treat each abundances map separately and do not consider spatial correlation between classes. In order to improve sub-pixel mapping accuracy and, consequently, enhance hyperspectral image classification, we propose a sub-pixel mapping method based on isotropic total variation minimization within and between pixels for different classes simultaneously. Experimental results with synthetic data sets show the attributes of using total variation as a prior model, which leads to improve sub-pixel mapping of different classes together.
Image Synthesis Using Machine Learning Techniques

Image synthesis is the generation of realistic images using a computer algorithm. This can be difficult and time-consuming. Image synthesis using machine learning aims to make this process easier and more accessible. The most prominent machine learning model for generating content is known as generative adversarial networks. This paper reviews and evaluates various generative model based on GANs. These various models are evaluated using inception score and Fréchet inception distance. These are common metrics for the evaluation of generative adversarial networks.
Recovery of phase modulation via residual neural network

An approach for recovering the phase information from the detected intensity was proposed in this work. Unlike the conventional approach based on the Gerchberg-Saxton algorithm, the proposed approach recovered the phase information via an alternative technique in the realm of deep learning, the residual neural network. The database we utilized to train the network was collected by a Michelson-based interferometer, where a spatial light modulator was implemented to provide the phase modulation as the phase object. As the result, the mean absolute error of each pixel was 0.0614&pi;.
Shared Print Initiatives

This paper provides an overview of the history, issues, challenges, opportunities, and obligations associated with shared print programs.Many of the library operational activities associated with participating in a shared print program have precedents or shared concerns with other operational work around collection development, preservation, and staffing in general.Activities at Emory University serve as examples for participating in shared print programs.
A three-dimensional PIV system based on camera array

A three-dimensional particle image velocimetry (PIV) system is introduced in this paper. Based on camera array light field technology and Tomographic PIV principle, this system is designed for the measurement of instantaneous or continuous three-dimensional velocity of the flow field section or for the measurement of instantaneous three-dimensional velocity within a volume. The camera array light field technology is capable of collecting four-dimensional light field data, and can flexibly adjust the observation field of view, focus position and depth of field through calculation processing. Therefore, combining the camera array light field technology with the Tomo-PIV a 3-dimensional PIV system is achieved. In this paper, the design and development of the system is presented, experimental results and analysis based on this system are discussed.
Database Rights in Big Data and the Cloud—Main Legal Considerations

This chapter delves into detail about the problems raised by database rights in the wake of the Internet and the Big Data era. Database rights, also known as sui generis rights, grant exclusive property protection to any creator of databases based on a qualitative or quantitative substantial investment.
optimized Reference Picture Selection for Light Field Image Coding

This paper proposes a new reference picture selection method for light field image coding using the pseudo-video sequence (PVS) format. State-of-the-art solutions to encode light field images using the PVS format rely on video coding standards to exploit the inter-view redundancy between each sub-aperture image (SAI) that composes the light field. However, the PVS scanning order is not usually considered by the video codec. The proposed solution signals the PVS scanning order to the decoder, enabling implicit optimized reference picture selection for each specific scanning order. With the proposed method each reference picture is selected by minimizing the Euclidean distance to the current SAI being encoded. Experimental results show that, for the same PVS scanning order, the proposed optimized reference picture selection codec outperforms HEVC video coding standard for light field image coding, up to 50% in terms of bitrate savings.
Ubiquitous Self

In 2013, the Oxford Dictionaries announced “selfie” as the word of the year. The dictionary defined it as “a photograph that one has taken of oneself, typically one taken with a smartphone or webcam and uploaded to a social media website.” Selfies are also a complex form of social interaction, an emerging aesthetics, thus having an irrevocable impact on self-portraiture. All visual culture revolves around the body and the body par excellence is the face. The 21st century portrait represents a kind of black mirror where we project ourselves into a kind of blindness. Mask and face are confused by an omnipresent multividuality in which the shield reveals itself and reveals other possible worlds. The face-mask melts in between Real and Virtual and the self becomes augmented.
The Selection and Optimization of a Negative comment Identification Method

Abstract This paper introduces the research background and significance of negative comments, and analyzes the present situation and development trend of negative comments identification at home and abroad. Several popular classification algorithms of naive Bayes, support vector machines and neural network are introduced. The word bag model, TF-IDF model and Word2Vec/Doc2Vec model feature model are also introduced. The data of feature extraction and classification algorithm in IMDB movie review website are simulated, and the results are compared and analyzed, and an optimal combination of feature extraction and classification algorithm is obtained.
A New Approach to Image Intensity Transformation Based on Equalizing the Distribution Density of Contrast

Intensity transformation is one of the basic approaches to enhance the image. However, known methods of intensity transformation have several disadvantages which significantly limit their use for image processing in automatic mode. In this paper, the problem of improving the efficiency of the intensity transformation of complex images in the automatic mode was considered. A new approach to the intensity transformation was proposed based on equalizing the distribution density of contrast in an image. The distribution of contrast is estimated based on bivariate distribution and brightness increments for pairs of pixels in the image. A new generalized description of the intensity transformation based on the joint distribution of brightness was proposed. It was shown that the traditional definition of histogram equalization is a particular case of the proposed generalized description. A new technique of parameter-free intensity transformation was proposed based on equalizing the distribution density of contrast in an image. The proposed technique provides an effective enhance the contrast of complex images without the appearance of unwanted artifacts has several advantages to the well-known histogram equalization technique. The results of experimental research confirm the effectiveness of the proposed approach to enhance images in automatic mode.
Tracking with deep neural networks

High Energy Physics experiments require fast and efficient methods to reconstruct the tracks of charged particles. Commonly used algorithms are sequential and the CPU required increases rapidly with a number of tracks. Neural networks can speed up the process due to their capability to model complex non-linear data dependencies and finding all tracks in parallel. In this paper we describe the application of the Deep Neural Network to the reconstruction of straight tracks in a toy two and three-dimensional models. It is planned to apply this tracking method to the experimental data taken by the MUonE experiment at CERN.
The Correlation between Nutritional Status, Genetic History and Non Genetic History with Mental Retardation Children at Sekolah Luar Biasa (SLB) Mandara in Kendari Municipality

Mental retardation is a mental disorder in lifelong, which is influenced by genetic and non-genetic factors, both before, during, and after birth.This study aimed to determine the correlation between nutritional status, genetic history, and non-genetic history with mental retardation children at SLB Mandara in Kendari Municipality.This study was an observational analytic study using a cross-sectional design.The population in this study was all of the children who have mental retardation at SLB Mandara Kendari as many as 54 students and the samples as many as 35 students.The sampling was done by using Stratified random sampling technique.Data analysis using a chisquare test with a confidence interval (CI) of 95% at a significance level of p <0.05.The results showed that there was a significant correlation between nutritional status (pvalue=0.031),genetic history (pvalue=0.048),maternal health history (pvalue=0.003),and pregnancy history (pvalue=0.016)with mental retardation children, and there was no correlation between the child's history (pvalue=0.089)and parenting history (pvalue=0.249)with mental retardation children at SLB Mandara in Kendari Municipality.
Exploring Pre-Service Teachers' Developmental Trajectories of Digital Literacies

Digital literacies are gaining popularity in teacher education over the past decade, but little research has been conducted on the developmental trajectories of pre-service teachers' digital literacies. Adopting a case study, four L2 Chinese pre-service teachers were examined during the spring semester of 2016 when they assisted teaching in a wiki writing classroom. Data was collected through semi-structured interviews and observation diaries. Drawing upon the framework of digital literacies, this chapter identifies the core components of digital literacies in a wiki-based collaborative writing context: attitudes towards technology and application (Thinking), task organization and feedback provision (Doing), modes and genres (Meaning), teacher-student relations (Relating), and roles and responsibilities (Being), and further explores pre-service teachers' developmental trajectories of digital literacies before concluding with a discussion of implications for instruction and teacher education.
Video Object Segmentation based on improved OSVOS

Abstract Video object segmentation is a mainstream branch of current image processing direction. How to achieve deep learning from complete supervision to unsupervised is the key problem that people are trying to solve. In this process, One-Shot Video Object Segmentation (OSVOS) can successfully tackle the task of semi-supervised video object segmentation. It transfers the general semantic information learned on ImageNet to the foreground segmentation task, and then learns the mapping of a single annotated object in sequence. In this paper, based on the concept of OSVOS, an improved neural network structure with dilated convolution, multi-scale convolution fusion and skip layer is proposed. Dilated convolution can increase the size of the receiving field. Multi-scale convolution can obtain multi-scale feature maps and fuse them. Skip layers can transfer feature information from low layer to upper layer. All of them help to improve the final accuracy. The experimental results show that all indicators has been improved.