Reviewerly/exHarmony huggingface dataset:
Finite mixture modeling using shape mixtures of the skew scale mixtures of normal distributions

This paper introduces the shape mixtures of the skew scale mixtures of normal distribution which are contained additional shape parameters to regulate skewness and kurtosis. We present a finite mixture model for this new family of distributions, which is a novel model-based tool for clustering heterogeneous data in the presence of skewed and heavy-tailed outcomes. The maximum likelihood estimates of the parameters of the proposed models are obtained by developing an Expectation Conditional Maximization Either algorithm. The numerical performance of the proposed methodology is illustrated through simulated and real data examples.
An Inspiration from Border Crossing: Principle of Boundary Activity for Integrating Learning in the Formal and Informal Spaces

With the recognition of the value of learning in informal spaces, it has been proposed that learning in the informal spaces should be an integral part of the formal schooling. The ubiquitous use of mobile technology creates various opportunities for connecting learning in the formal and informal contexts. In seamless learning, different efforts have been devoted to improving the synergy of these two learning contexts supported by mobile technology, but challenges still exist in the design of seamless learning scenarios that involve the pedagogical integration of learning in formal and informal spaces with active boundary interaction. To promote the mutual interaction of learning contexts in seamless learning, and improve the smooth transformation of students’ cognition in crossing borders, we elaborate on the conception of “boundary object” which is borrowed from science education and learning sciences and inspired by the notion of border crossing from the cultural perspectives, as the knot for tightening learning in different contexts. In this chapter, we will summarize the origins of the boundary objects and discuss their application in improving the boundary interactions in seamless learning. Based on these, the principle of boundary activity-based learning (BABL) is articulated for improving the design of seamless learning activities. A BABL lesson exemplar is illustrated for BABL application, and the initial results of a pilot study are discussed. The BABL principle and the research will inform the pedagogical design of technology-supported STEM education and science learning in a seamless learning context.
Spherical Panorama Image Watermarking Using Viewpoint Detection

Even though interest in spherical panorama content has increased rapidly, few studies have examined watermarking techniques for this content. We present a new watermarking technique to protect spherical panorama images as well as view-images that are rendered with a specific viewpoint. Solving the watermark synchronization problem in the detection process requires finding the viewpoint of a view-image. Scale Invariant Feature Transform (SIFT) and Euclidea transformation matrix are used to find viewpoint information of a detection target view-image. Using the viewpoint information, a view-image can be recovered to a source image and then we can detect watermark from it. The experimental results show robustness against several attacks such as JPEG compression, Gaussian filter, and noise addition attack.
An Enhanced Opinion Retrieval Approach on Arabic Text for Customer Requirements Expansion

Recently, most companies market their products on the web to recognize their customers’ requirements and to improve their services’ quality according to the customers’ feedback and opinions. A huge amount of reviews and opinions are posted daily on products. Obtaining and quickly analyzing these opinions become a difficult task. These opinions might lead to a tendency or disinclination to a specific point of view. To represent the products’ opinions from customers’ perspectives, opinion retrieval becomes a demanding and essential task for automatically extracting, analyzing, and summarizing customers’ reviews. Usually, online products are offered by several suppliers in e-commerce. Therefore, to keep up the competitiveness among suppliers, the need for innovative requirements is required. This paper proposed an enhanced opinion retrieval approach depending on the explicit feature based opinion mining. The proposed approach expands the initial products’ requirements using extended heuristics and linguistic patterns of the Arabic opinions. Besides the relevant score, several factors, like features’ weight, the opinion importance, and the sentiment polarity are used to rank the retrieved results. The experimental results show the capability of the proposed approach to automatically extract more innovative features compared to the conditional random field (CRF) results.
An Investigation into Healthcare-Data Patterns

Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%.
Quantum Domain Design of Clifford+T-Based Bidirectional Barrel Shifter

The prime objective behind the synthesis of quantum circuits in the field of quantum computing is to obtain so-called quantum supremacy where many intractable problems can be solved with high accuracy and fidelity for which no classical algorithm exists as of now. To suppress inherent noise, the fault tolerant circuit becomes an unavoidable feature. Fault tolerant quantum circuit demands high threshold quantum error correction code, such as surface code. The Clifford+T group with surface code provides universal gates to design quantum circuits. On the other hand, many key computational operations like arithmetic and logical operation, address decoding and indexing requires shifting, rotating of data in unidirectional as well as bidirectional way, which can be achieved by using shift resistor with multiple cycles. However, the barrel shifter can be used to shift or rotate multiple bits in one cycle that leads to faster computation. In this conjecture, we have proposed a fault tolerant quantum barrel shifter using Clifford+T which will be relevant to quantum computing. Towards the end of this research paper, we have also evaluated some cost parameters like T-depth and T-count associated with the performance of quantum circuit.
Transform Learning Based Sparse Coding for LiDAR Data Denoising

In recent years, sparse coding (SC) has been exploited for light detection and ranging (LiDAR) data restoration, and promising results have been reported. However, such methods are usually time consuming, because much computational resource has been devoted to solving batches of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> or ℓ<;span style="font-size: 11.25px;"> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> <;/span>-norm optimization problems iteratively. More recently, fast SC method has been proposed to achieve nearly real-time performance at the expense of applicability. In this letter, we propose a transform learning based SC method for LiDAR data denoising. Moreover, we present a detailed evaluation for our series of SC methods with different models, and together with concluding remarks. Such remarks can be applied as a guide to apply appropriate SC model for specific application.
Performance Data Analysis for Parallel Processing Using Bigdata Distribution

The following document presents metrics and pointers for datacenter performance evaluation, whose production workflow will be improved by a parallel computing software, each cluster instance was virtualized providing for scalability and availability for every person who access to the system at different locations. Apache spark will be used as parallel processing distribution through different scenarios, each one will handle workload on physical and virtual nodes, after the collection of time response a comparations will be realized for determinate if the parallel distribution is an ideal solution for guarantee processing requirements.
Simultaneous Clustering and Feature Selection Using Nature-Inspired Algorithm

Clustering means partitioning of data set into individual clusters where the similarity among the data exists and the procedure requires more substantial methodology when the dimension of the input data set is very high as well as we have to select more relevant dimensions or features which are necessary enough for clustering. Nature-inspired algorithm like firefly gives a promising result in function optimization and clustering. The proposed work will represent a new feature selection cum clustering algorithm called iterative firefly k-means features selection (FKM_FS) algorithm by minimizing the inter-cluster distance as well as maximizing the intra-cluster distance and maximizing the average relevance of the particular feature to the clustering. We define a methodology based on variance of observation in a cluster with respect to global variance to identify relevant feature subset. Finally, the algorithm will run both on real and data set.
Generation of pseudo random number sequence from discrete oscillating samples of equally spread objects and application for stream cipher system

Summary The demand on security privileged information has widely increased due to the sensitivity of the exchanged information over public communication channels. Nowadays, almost all organizations, mainly, business, financial, and government related areas, and also other regions are computerized, and sometimes, the data will be highly confidential. Since some areas dealing with confidential data, it is very much essential to provide the security for the data to avoid unauthorized access. Multimedia data security and security related issues have attracted many researchers towards it. Cryptography is necessary to protect the confidential information transmitting over the network. The strength of the cryptographic system, especially, stream cipher, is not only depending upon its encryption/decryption algorithm but also depends upon the randomness properties of the key sequence such as length, uniformity, independence, etc. In this work, a novel method of generating the pseudo random number that is based on the discrete equally spread oscillating points viewed orthogonally as an envelope has been considered for the generation. Depending on the number of samples, it is possible to get the higher length of the sequence. The random patterns generated by the discrete points of a moving wave are used to generate non binary random numbers. These numbers are used as key in stream cipher system. These are having greater advantages like one time padding to achieve perfect security in cryptographic application. The same non binary key sequence generated is applied for the cryptographic application, and the security parameters of the result obtained from cryptographic applications are also evaluated to observe the strength of security level.
A Distributed Support Vector Machine Using Apache Spark for Semi-supervised Classification with Data Augmentation

One of the popular and extensively used classification algorithms in the data mining and the machine learning technique is the support vector machine (SVM). Yet, conversely they have been traditionally applied to a small dataset or to an extent medium dataset. The current requirement and demand to scale up with the evolving size of the datasets have fascinated the research notice and attention such that new techniques and implementations can be carried out for the SVM, and as a result can scale well with large datasets and tasks. Recently, the distributed SVM is studied by the researchers, but the data augmentation with semi-supervised classification using the distributed SVM is not yet implemented. In this paper, a distributed implementation of support vector machine along with the data augmentation upon the SparkR, which is a recent and effective platform for performing distributed computation, is introduced and analyzed. This framework—A Distributed Support Vector Machine under Apache Spark for Semi-supervised Classification with Smart Data Augmentation—is implemented with a large-scale dataset with more than million data points. The results and analysis show that the proposed approach greatly enhances the predictive performance of the method in terms of execution time and faster processing.
Structure-preserving video super-resolution using three-dimensional convolutional neural networks

Convolutional neural networks (CNN) have given rise to a new generation of video super-resolution (SR) technique. However, most existing CNN-based video SR algorithms treat the consecutive frames as a series of feature maps, just as the procedure performed in single image SR algorithms. We propose an end-to-end three-dimensional (3-D) CNN video SR framework. The input frames are considered as a cube in our framework. 3-D convolution is performed on it to extract features along spatial and temporal dimension. Image prior knowledge, such as optical flows, is introduced in reconstruction. A combination of mean square error loss and multiscale structure similarity index (MS-SSIM) loss is used to optimize the model. Experimental results show that the proposed method reconstructs high-resolution frames with more accurate and visually pleasant structures compared with state-of-the-art video SR algorithms. We also achieve comparable PSNR/SSIM results with less computation time.
A Novel Trajectory-VLAD Based Action Recognition Algorithm for Video Analysis

Recognition of human actions in videos has been an active research area in computer vision community due to its important theoretical research significance and extensive practical application value. As the most commonly used local feature descriptor of video, dense trajectories were shown superior performance for action recognition on a variety of datasets. However, the high computational complexity and huge storage space requirements of the algorithm limit its application scenarios. This paper optimizes the action recognition algorithm based on improved dense trajectories feature, we use Vector of Locally Aggregated Descriptors (VLAD) for feature encoding, which can greatly reduce the computational complexity and avoid expensive hard disk access, at the same time, can also effectively reduce the loss of feature information and improve the recognition accuracy.
Interactive Contour Extraction via Sketch-Alike Dense-Validation Optimization

We propose an interactive contour extraction method inspired by a skill often adopted in sketching: an artist usually sketches an object by first drawing lots of short, directional, and redundant strokes, then following these small strokes to draw the final outline of the object. Our method simulates this process. To extract a contour, our method relies on user interaction, which provides us with a narrow band containing the target contour. Then, we densely sample sub-bands from the whole band, with each sub-band containing a local segment of the target contour. We design a curve-centered coordinate system in which a dynamic programming algorithm is proposed to extract the local segment in each sub-band. The local segment is guaranteed to be as evident and smooth as possible, to mimic the strokes sketched by the artist. Finally, we integrate all local segments of all sub-bands together to obtain the whole target contour based on the weighted principal component analysis. Our method can extract high-quality object contours due to the dense validations among local segments. That is, even if one segment deviates from the right location, several other segments in its local neighborhood can correct it in the integration stage. Both quantitative experiments and a user study demonstrate the effectiveness of the proposed method.
Why Are Repeated Auctions in RaaS Clouds Risky?

The world of cloud computing is progressing from the concept of securing resources by predefined units to dynamically allocating resources using economic mechanisms. New mechanisms offer better utilization of the hardware by sharing it among multiple users. However, they allow new types of economic attacks. We introduce two new economic attacks performed by malicious users. These attacks harm the aggregate utility of Resource-as-a-Service (RaaS) clouds. Our first attack aims at raising bills in the system, and causing victims to pay more for the same amount of resources. Over time the attack may cause victims to exhaust their budget, thus lowering their demand for resource allocation, and allowing the attacker to acquire the freed resources at a negligible cost. Our second attack is designed to hinder the victim's performance at specific points in time by outbidding them for a single round. For resources of high regaining costs or that their full utilization takes time (e.g., RAM), even a single round without the resource may significantly hinder the performance. In this work we demonstrate on a simple representative example how the first attack reduces the victim's profit sevenfold and the second attack causes damage of $290–$630 for every dollar spent on the attack.
A Set of New Hermite Kernel Functions in Kernel Extreme Learning Machine and Application in Human Action Recognition

The kernel function has been successfully utilized in the extreme learning machine (ELM) that provides a stabilized and generalized performance and greatly reduces the computational complexity. However, the selection and optimization of the parameters constituting the most common kernel functions are tedious and time-consuming. In this study, a set of new Hermit kernel functions derived from the generalized Hermit polynomials has been proposed. The significant contributions of the proposed kernel include only one parameter selected from a small set of natural numbers; thus, the parameter optimization is greatly facilitated and excessive structural information of the sample data is retained. Consequently, the new kernel functions can be used as optimal alternatives to other common kernel functions for ELM at a rapid learning speed. The experimental results showed that the proposed kernel ELM method tends to have similar or better robustness and generalized performance at a faster learning speed than the other common kernel ELM and support vector machine methods. Consequently, when applied to human action recognition by depth video sequence, the method also achieves excellent performance, demonstrating its time-based advantage on the video image data.
Sensitivity and Monotonicity in Class Cohesion Metrics

Cohesion metrics have been explored widely in the literature for procedural programs and subsequently for object oriented programs. However, as it is commonly found with metrics based approaches, most attempts have been shown to have suffered from some or the other flaws. In this paper, in light of two problems associated with cohesion metrics namely sensitivity and monotonicity, an analysis of a few recent refined metrics is provided. A new metric called ISCOM (Improved Sensitive Cohesion Metric), is proposed. ISCOM addresses these problems capturing the strength of cohesion with a finer granularity and a higher sensitivity. A comparison is also made with the help of cohesion lattice based models to highlight the contributions of the new approach based on sensitivity and monotonicity. We also apply cohesion lattice theory to validate the approach.
Detection of Suspicious Transactions with Database Forensics and Theory of Evidence

The aim of enabling the use of illegally obtained money for legal purposes, while hiding the true source of the funds from government authorities has given rise to suspicious transactions. Illegal transactions are detected using data mining and statistical techniques with the input data like various suspicious reports or the data set of all transactions within a financial institution. The output obtained is the set of highly suspicious transactions or highly suspicious entities (e.g., persons, organizations, or accounts). In this paper, we propose a database forensics methodology to monitor database transactions through audit logs. The Rule-based Bayesian Classification algorithm is applied to determine undetected illegal transactions and predicting initial belief of the transactions to be suspicious. Dempster-Shafer’s theory of evidence is applied to combine different parameters of the transactions obtained through audit logs to verify the uncertainty and risk level of the suspected transactions. Thus a framework is designed and developed which can be used as a tool for the digital investigators.
Large Scale Semantic Indexing with Deep Level-wise Extreme Multi-label Learning

Domain ontology is widely used to index literature for the convenience of literature retrieval. Due to the high cost of manual curation of key aspects from the scientific literature, automated methods are crucially required to assist the process of semantic indexing. However, it is a challenging task due to the huge amount of terms and complex hierarchical relations involved in a domain ontology. In this paper, in order to lessen the curse of dimensionality and enhance the training efficiency, we propose an approach named Deep Level-wise Extreme Multi-label Learning and Classification (Deep Level-wise XMLC), to facilitate the semantic indexing of literatures. Specifically, Deep Level-wise XMLC is composed of two sequential modules. The first module, deep level-wise multi-label learning, decomposes the terms of a domain ontology into multiple levels and builds a special convolutional neural network for each level with category-dependent dynamic max pooling and macro F-measure based weights tuning. The second module, hierarchical pointer generation model merges the level-wise outputs into a final summarized semantic indexing. We demonstrate the effectiveness of Deep Level-wise XMLC by comparing it with several state-of-the-art methods on automatic labeling of MeSH, on literature from PubMed MEDLINE and automatic labeling of AmazonCat13K.
Optimization of Neural Network Training with ELM Based on the Iterative Hybridization of Differential Evolution with Local Search and Restarts

An Extreme Learning Machine (ELM) performs the training of a single-layer feedforward neural network (SLFN) in less time than the back-propagation algorithm. An ELM defines the input weights and biases of the hidden layer with random values, and then analytically calculates the output weights. The use of random values causes SLFN performance to decrease significantly. The present work carries out the adaptation of three continuous optimization algorithms of high dimensionality (IHDELS, DECC-G and MOS) and compares their performance to each other and with the state-of-the-art method, a memetic algorithm based on differential evolution called M-ELM. The results of the comparison show that IHDELS using a validation model based on retention (Training/Testing) obtains the best results, followed by DECC-G and MOS. All three algorithms obtain better results than M-ELM. The experimentation was carried out on 38 classification problems recognized by the scientific community, while Friedman and Wilcoxon nonparametric statistical tests support the results.