Reviewerly/exHarmony huggingface dataset:
Machine Learning on Graphs with Kernels

Graphs are becoming a dominant structure in current information management with many domains involved, including social networks, chemistry, biology, etc. Many real-world problems require applying machine learning tasks to graph-structured data. Graph kernels have emerged as a promising approach for dealing with these tasks. A graph kernel is a symmetric, positive semidefinite function on the set of graphs. These functions extend the applicability of kernel methods to graphs. Graph kernels have attracted a lot of attention during the last 20 years. The considerable research activity that occurred in the field resulted in the development of dozens of kernels, each focusing on specific structural properties of graphs. The goal of this tutorial is to offer a comprehensive presentation of a wide range of graph kernels, and to describe their key applications. The tutorial will also offer to the participants hands-on experience in applying graph kernels to classification problems.
Multivariate Analysis of Variance

Tests such as the Z-test and t-test have been studied to test a mean value for one and two populations (with a single variable), while for the multivariate case, the Hotelling's test is employed to test the equality of means for one and two populations. These tests cannot be employed for testing the equality of means for more than two populations. A new technique is suggested for testing more than two populations; this technique is called analysis of variance (ANOVA) test for one response variable, and multivariate analysis of variance (MANOVA) for more than one response variable.
Face Recognition System Based on Modified Sparse Representation

This paper proposes a face recognition method based on modified sparse representation. Sparse representation is an advanced data analysis algorithm based on compressive sensing. Traditionally, the sparse representation is performed on the global dictionary formed by all the training classes. Afterwards, the classification is made based on the reconstruction errors. This method did not consider the individual representation capabilities of different classes. So, a modified sparse representation is designed in this study by conducting the sparse representation on the local dictionary formed by each training class. Then, the reconstruction error of each class is computed and compared to determine the label of the test sample. In the experiments, the AR and Yale-B face image databases are employed to investigate the performance of the proposed method. The results show its effectiveness and robustness.
Library: Library Notifications: Library notifications

Records notifications from publishers of times when resources will be unavailable due to site maintenance or technical difficulties.
Non-Linear Polynomial Filters for Edge Enhancement of Mammograms

Non-linear polynomial filtering (NPF) framework has been explored previously as a robust approach for contrast improvement of mammographic images. However, NPF ‘Prototypes: α and β′ have been performance limited; as the contrast improvement has been accompanied with a severe background suppression in mammograms. This affected the visualization of other anatomical structures and diagnostic features in the vicinity of the ROI; these features equally contribute towards diagnostic decision making by radiologists. On the other hand, it is equally difficult to improve the edge strength and sharpness of the ROI without compromising the background content.
Feasibility of Non-contact Smart Sensor-Based Falls Detection in a Residential Aged Care Environment

Few studies of sensor-based falls detection devices have monitored older people in long-term care settings. The present investigation has addressed this gap by trialing the feasibility and acceptability of a non-contact smart sensor system (NCSSS) to monitor behaviour and detect falls in an Australian residential aged care facility (RAC). Methods This investigation was undertaken using a mixed methods approach, comprising three phases: (1)Pilot study design and implementation at a RAC, using a purposive sampling approach; (2)Study evaluation and post-pilot interviews; and (3)Analysis and review of results. Results Data was collected for four RAC participants over four weeks of the NCSSS pilot study. Numerous feasibility challenges were encountered, for example, in the installation configuration, placement of sensors for optimal detection, network and connectivity issues, and maintenance requirements. Conclusion The area of smart sensor technologies in falls monitoring and detection remains a relatively emergent field of investigation, and presently there are few real-life studies of NCSSS in an Australian RAC setting reported in the literature. This study confirmed that NCSSS technology may have a role in falls and behaviour monitoring of elderly residents in RAC and home environments. However, feasibility factors may affect implementation and adherence.
No-reference image quality assessment based on an objective quality database and deep neural networks

Image quality assessment (IQA) has always been an active research topic since the birth of the digital image. Actually, the arrival of deep learning has made IQA more promising. However, most state-of-the-art no-reference (NR) IQA methods require regression training on distorted images or extracted features with subjective image scores, which makes them suffer from insufficient reference image content and training samples with subjectively scoring due to timeconsuming and laborious subjective testing. Furthermore, most convolutional neural networks (CNN)-based methods generally transform original images into patches to accommodate fixed-size input of CNN, which often alter the image's data and introduce noise into the neural network. This paper aims to solve the above problems by adopting new strategies and proposes a novel NRIQA method based on deep CNN. Specifically, first, we obtain image data with diverse image content, multiple image sizes, and reasonable distortion by crawling, filtrating, and degrading numerous publicly licensed high-quality images from the Internet. Then, we score all the images using an excellent full-reference (FR) IQA algorithm, thereby artificially construct a large objective IQA database. Next, we design a deep CNN, which can accept input images of original sizes from our database instead of patches, then we train the model with the FRIQA index as training objective thus propose the opinionunaware( OU) NRIQA method. Finally, the experiment results show that our method achieves excellent performance, which outperforms state-of-the-art OU-NRIQA models and is comparable to most of the traditional opinion-aware NRIQA methods, even some FRIQA methods on standard subjective IQA databases.
From requesting to alms-seeking. The politeness formula fare la carità in nineteenth-century Italy

Abstract – This contribution focuses on one particular politeness formula for requests, fare la carita di ‘be so good as to (give)’. The aim of the paper was to reconstruct the meanings and contexts for the usage of fare la carita di in nineteenth-century Italy. The following sources were used: conduct books, dictionaries, novels and diachronic corpora. The essay first looked at politeness metadiscourse and examined the available language advice for requests in CGIO, a corpus of 51 nineteenth-century Italian conduct books. However, as there is none available for fare la carita di , three nineteenth-century dictionaries were consulted. These findings from the metadiscourse were then read against those from a qualitative analysis covering all the examples found in two of the most influential novels of that period, Alessandro Manzoni’s I promessi sposi , ‘The Betrothed’, 1840, and Carlo Collodi’s Pinocchio , 1883. Finally, quantitative data from the historical corpora DiaCORIS and MIDIA were provided. This combined analysis (using metadiscourse alongside qualitative discourse analysis and quantitative data) produced the following results: the formula is used in two contexts, to make a (sometimes forceful) request and to beg for a handout. Both appear in roughly equal proportions until the end of World War II. After that, fare la carita di disappears from DiaCORIS. The paper tentatively concludes that the absence of the formula from the advice on requests in conduct books may partly be explained by its use as a specialised marker for alms-seeking, since conduct books fiercely criticise almsgiving to the undeserving poor.
Strongly Equivalent Epistemic Answer Set Programs

Epistemic answer set programming ($$\mathsf {EASP}$$) is a recent epistemic extension of answer set programming ($$\mathsf {ASP}$$), endowed with the epistemic answer set (EAS) semantics. EASs propose a straightforward generalisation of ASP’s original answer set semantics. Moreover, they provide intended results both for cyclic and acyclic programs, possibly containing arbitrary constraints. Epistemic here-and-there logic ($$\mathsf {EHT}$$) is also a recent epistemic extension of a well-known nonclassical logic called here-and-there logic ($$\mathsf {HT}$$), which is intermediate between classical logic and intuitionistic logic. In this paper, we discuss a strong equivalence characterisation for $$\mathsf {EASP}$$ programs, which is identified on $$\mathsf {EHT}$$.
Blind Audio Watermarking Scheme Based on Improved Cepstral Statistical Mean Modulation

Audio watermarking is one of the best solutions to prevent the pirate audio propagation. The human auditory system is more sensitive than the visual system, which makes the audio watermarking more challenging. Many approaches for audio information hiding have been proposed. This paper designs a robust blind audio watermarking scheme based on modifying coefficients in cepstrum domain. According to the perceptual characteristics of human acoustics, time domain energy is firstly computed to select the embedding frames. Then, the watermark information is embedded in the complex cepstrum domain through improved statistical mean modulation method. Arnold scrambling transform is applied to ensure the security of the watermarking scheme. The proposed scheme achieves blind watermarking, since only the index of selected frames and the modulation threshold value are needed as secret keys in extraction process. Experimental results show that the designed watermarking scheme is suitable for all kinds of audio signals, for example, speech, music, ringtone, etc. This proposed method has good security, imperceptibility and robustness for different kinds of audio signals.
Hue Modification Localization By Pair Matching

Hue modification is the adjustment of hue property on color images. Conducting hue modification on an image is trivial, and it can be abused to falsify opinions of viewers. Since shapes, edges or textural information remains unchanged after hue modification, this type of manipulation is relatively hard to be detected and localized. Based on the fact that small patches inherit the same Color Filter Array (CFA) configuration and demosaicing, any distortion made by local hue modification can be detected by patch matching within the same image. In this paper, we propose to localize hue modification by means of a Siamese neural network specifically designed for matching two inputs. By crafting the network outputs, we are able to form a heatmap which potentially highlights malicious regions. Our proposed method deals well not only with uncompressed images but also with the presence of JPEG compression, an operation usually hindering the exploitation of CFA and demosaicing artifacts. Experimental evidences corroborate the effectiveness of the proposed method.
Method to Design Pattern Classification Model with Block Missing Training Data

Missing data is a usual drawback in many real-world applications of pattern classification. Methods of pattern classification with missing data are grouped into four types: (a) deletion of incomplete samples and classifier design using only the complete data portion, (b) imputation of missing data and learning of the classifier using the edited set, (c) use of model-based procedures and (d) use of machine learning procedures. These methods can be useful in case of small amount of missing values, but they may be unsuitable in case of relatively large amount of missing values. We proposed a method to design pattern classification model with block missing training data. First, we separated submatrices from the block missing training data. Second, we designed classification submodels using each submatrix. Third, we designed final classification model using a linear combination of these submodels. We tested the classifying accuracy rate and data usage rate of the classification model designed by means of the proposed method by simulation experiments on some datasets, and verified that the proposed method was effective from the viewpoint of classifying accuracy rate and data usage rate.
Reconstruction of Anatomy Experiment Based on 3D Virtual Theory

In order to extract the contour feature of human anatomy experiment organ image effectively and realize 3D visualization reconstruction of 3D virtual anatomy experiment, an image extraction algorithm of human anatomy experiment organ based on Harris wavelet multi-scale segmentation is proposed. The reconstruction model of anatomical experiment based on three-dimensional virtual is constructed. The Splines biorthogonal wavelet is used to enhance the image of human anatomy experiment organ, and the 3D reconstruction model of the feature sequence and edge contour point of the human anatomy experiment is initialized after the enhancement processing. The affine transform feature detection technique is used to improve the traditional Snake algorithm to enhance the edge virtual information feature points of the image and extract the edge features of the human anatomy experimental organ image effectively. Then, the frame points are arranged according to the intensity of pheromone distribution, and the edge contour feature extraction result of the previous image is used as the initial point of edge extraction in the next image of human anatomy experiment organ, and then the frame points are arranged according to the intensity of pheromone distribution. Achieve the human anatomy experiment organ image edge detection batch processing. The simulation results show that the proposed algorithm can effectively extract the edge contour of the human anatomy experiment organ image, and the edge contour point is closer to the real organ edge of the human anatomy experiment, and the reconstruction of the anatomy experiment can be realized effectively.
Identity-Based Encryption Resilient to Continual Leakage Without Random Oracles

In general, the security of identity-based encryption schemes has been considered under the ideal circumstances, where the adversaries do not acquire the secret internal state of the schemes. However, the adversaries can obtain partial information for the secret key through the various key leakage attacks in reality. In order to further describe the continual leakage attack, we formally define a secure model for identity-based encryption. The adversary is allowed to continuously acquire part of the secret information through the continual leakage attack in the secure model. Then we give a new type identity-based encryption scheme resilient to continual leakage. This scheme which is based on an identity-based key encapsulation mechanism is secure against chosen-ciphertext attack under the hardness of the computational bilinear Diffie-Hellman problem in the standard model. This proposed scheme enhances the continual leakage-resilient property and enjoys less computation cost.
A L0 regularized framelet based model for high-density mixed-impulse noise and Gaussian noise removal

Images are often corrupted by impulse noise due to transmission errors, malfunctioning pixel elements in camera sensors, faulty memory locations in the imaging process. This paper proposes a new method for removing the mixed impulse noise and gaussian noise. The proposed method has two-phase, and the first phase is to identify candidate pixels existing impulse noise by using median filtering. The second phase processes the regions with impulse noise and leaves the others free with a mask generated by the previous phase. In order to protect the sharp image, we propose a L0 regularized framelet with a L1 fidelity term to recover the images. Numerical results demonstrate that the proposed method is a significantly advance over several state-of-the-art techniques on restoration performance.
Application Research of XML Parsing Technology Based on Android

Based on the research of XML core technology, this paper introduces the current mainstream XML parsing methods, XML DOM and SAX methods. For the current android system, which is used in android operating system. XML is researched in the android layout file, which is combined with the current android platform. The paper introduce application of XML pull parsing process, first study the parsing performance of XML, the second through an example to discuss these three methods, the last analysis XML file Size and compares these three methods.
Efficient Stratified Sampling Graphing Method for Mass Data

Sequentially linking data during polyline graphing of mass data (millions of points or more) generally results in poor graphing efficiency. Numerous curves are buried behind each pixel and cannot be displayed due to resolution limits of the width of the X-axis. Herein, a new efficient stratified sampling graphing method is proposed. The test results demonstrated that: (1) The full dataset is divided into <em>2X</em> subsets, where <em>X</em> is the width of the X-axis in pixels, and the maximum and minimum values of the data in each subset are respectively calculated and linked in order of appearance. This method yields <em>4X</em> sampled data graphs that are highly consistent with the full dataset graphs. (2) When the dataset is divided into <em>2X, 4X, 6X, 8X,</em> or more subsets (progressively increasing by even multiples), the similarity gradually increases. The average similarities can reach approximately 99.24% and 99.93% in the <em>2X</em> and <em>50X</em> subsets, respectively. We think that <em>2X</em> is the optimal subset allocation, which can achieve a high similarity, but also achieve the fastest sampling speed. (3) Compared with the speed of full dataset graphing, the overall speed of the “single-thread sampling + graphing” is increased by approximately 70 times, and that of the “threadPool sampling + graphing” was enhanced by approximately 200 times. The method employs the minimum amount of sampled data to obtain the full dataset graph that users expect to see, thereby significantly improving graphing speeds of mass data.
Improved nodule detection in chest X-rays using principal component analysis filters

Healthcare informatics deals with incorporation of computers and information technology into clinical workflow and to improve the quality of diagnosis in context of massive screening process. In the process computerized models are developed to measure the quality, safety, and efficiency of healthcare professionals. One particular innovation related to health informatics reported here is concerned with computer-aided chest radiography analysis for lung cancer public screening. Early detection of lung cancer is the most promising strategy to enhance a potential patient’s chance of survival. Chest X-ray provides an important diagnostic examination but they are complex in the sense that relevant diagnostic structures (soft tissues) are superimposed by irrelevant bony objects (ribs). Computer-aided detection (CAD) schemes are employed as a front end to further radiologist assessment of nodules in a given chest X-ray image. A major challenge in the current CAD scheme is the detection of nodules that are obstructed by ribs. Ribs are found to contribute large variance as compared to that of the soft tissue and this characteristic is exploited in suppressing ribs. It is proposed here if a data driven but sensitive to variance filtering technique is utilized like principal component analysis, the ribs can be identified and then suppressed to an acceptable level, greatly improving the chances of nodule detection hidden under a rib. The experiments are being conducted which provides validity of the proposed method. The rib suppression scheme can be embedded in an automated chest X-ray screening module that will greatly enhance its sensitivity in detecting small nodules.
Determining shelving accuracy via sampling in a community college library

During the Fall 2017 semester, staff at the Hudson County Community College (HCCC), Library began to notice that many books listed as available in the catalog were often not being found on the shelves when patrons attempted to retrieve them. This situation puzzled library leadership because HCCC had recently conducted an inventory and removed all missing items from its holdings. To determine the cause of this discrepancy, HCCC staff decided to sample the library’s collection to determine if books were available at the expected locations. From this, the library found that a high percentage of its books were not present where they were expected to be. In response, library staff implemented a variety of changes to HCCC’s shelving and access services operations.
An Energy Efficient and QoS Achieved Through MapReduce in Cloud Environment

This paper presents an energy-efficient and quality of service achieved through MapReduce in cloud environment. Although decrease in operating costs residue to be a key desire for movement to Cloud environments, Power consumption is a big thing for data centers and cloud service providers. Many big data applications execute on Hadoop MapReduce framework for handling large workloads. Cloud computing, Virtual machine allocation policy is an important aspect while dealing with multiple component architectures. Data allocation, privacy allocation, resource optimization are challenging task in any architecture. Traditional algorithm faces either a long computation time or high cost over the computation. In this approach which is Hadoop driven matrix based architecture is used for data processing. A hybrid combination of both the approach makes user able to process data effectively with time and cost. The approach is performed on Ubuntu 16.04, HDFS and Java API and maximum power consumption of 112 W as a result.
Building Own Blockchain

Own is a decentralized fundraising, digital trading and security token issuance platform that runs on a native, custom-built dual-blockchain infrastructure.
A proposed class model for integrate of block and conventional structure curriculum for supporting quality assurance system

Abstract Currently, the higher education curriculum has developed two systems, namely the block-based curriculum in the field of medicine and the semester credit-based curriculum. The block-based curriculum is currently applied in medical schools while the semester credit-based curriculum is applied in non-medical faculties. The presence of these two curriculum systems raises problems in the management of academic information systems. One of the problems that occur is that the existing information system has not been able to manage the block curriculum and semester credit simultaneously in one system. So that this causes the academic information system unable to manage the implementation of assessments on the block-based curriculum. This paper proposes a new model in the design of academic information systems that are able to integrate the needs of block-based curriculum and credit. The method used to solve this problem is an object-oriented approach using class diagrams as a model to describe the structure of academic information systems. The results of the development of the new model show that the structure of the block-based curriculum is a general form (generalization) of semester-based credit. Therefore the implementation of the two curriculums can use a similar model class structure to be more efficient and easier to maintain information system software in the future.