Reviewerly/exHarmony huggingface dataset:
The Influence of Spatial Awareness on VR

A common practice of VR experiments is to create a 3D model replica of the experience room to ease the transition of the real world to the virtual environment. However, the biggest issue with this method is that different spaces would need lots of preparation of 3D models. We propose to use a semi-unified covered space so that the user won't be able to compare the real space and the virtual environment, and some methods to make users believe that the VR space is real. It helps the user to believe in the existence of virtual objects that are not in the real space, and increases the possibility of the existence of the 3D object in real space.
Key-Finding Based on a Hidden Markov Model and Key Profiles

Musicologists and musicians often would like to search by keys in a digital music library. In this paper, we introduce a new key-finding algorithm that can be applied to music in both symbolic and audio formats. The algorithm, which is based on a Hidden Markov Model (HMM), provides two stages of key-finding output; the first one referring to local keys and the second one to the global key.
Colorful Frontier-Based Search: Implicit Enumeration of Chordal and Interval Subgraphs

This paper considers enumeration of specific subgraphs of a given graph by using a data structure called a zero-suppressed binary decision diagram (ZDD). A ZDD can represent the set of solutions quite compactly. Recent studies have demonstrated that a technique generically called frontier-based search (FBS) is a powerful framework for using ZDDs to enumerate various yet rather simple types of subgraphs. We in this paper, propose colorful FBS, an enhancement of FBS, which enables us to enumerate more complex types of subgraphs than existing FBS techniques do. On the basis of colorful FBS, we design methods that construct ZDDs representing the sets of chordal and interval subgraphs from an input graph. Computer experiments show that the proposed methods run faster than reverse search based algorithms.
3C3R, an Image Encryption Algorithm Based on BBI, 2D-CA, and SM-DNA

Color image encryption has enticed a lot of attention in recent years. Many authors proposed a chaotic system-based encryption algorithms for that purpose. However, due to the shortcomings of the low dimensional chaotic systems, similar rule structure for RGB channels, and the small keyspace, many of those were cryptanalyzed by chosen-plaintext or other well-known attacks. A Security vulnerability exists because of the same method being applied over the RGB channels. This paper aims to introduce a new three-channel three rules (3C3R) image encryption algorithm along with two novel mathematical models for DNA rule generator and bit inversion. A different rule structure was applied in the different RGB-channels. In the R-channel, a novel Block-based Bit Inversion (BBI) is introduced, in the G-channel Von-Neumann (VN) and Rotated Von-Neumann (RVN)- based 2D-cellular structure is applied. In the B-channel, a novel bidirectional State Machine-based DNA rule generator (SM-DNA) is introduced. Simulations and results show that the proposed 3C3R encryption algorithm is robust against all well-known attacks particularly for the known-plaintext attacks, statistical attacks, brute-force attacks, differential attacks, and occlusion attacks, etc. Also, unlike earlier encryption algorithms, the 3C3R has no security vulnerability.
Application of Cloud Model to Evaluation of Forest Soil Fertility: A Case in Chinese Fir Plantations in Southern China

Soil nutrients are of great significance for maintaining forest growth and ensuring land productivity. A comprehensive scientific evaluation of soil fertility is helpful for sustainable forest management. There are many uncertainties in traditional evaluation methods, that is fuzziness and randomness, which often lead to a large deviation of the evaluation results. In order to comprehensively consider the fuzziness and randomness of soil fertility evaluation, the cloud model was introduced to evaluate the soil fertility of Chinese fir plantations. The cloud model is an uncertain transformation model which can combine a qualitative description with a quantitative calculation as well as reflect the randomness and fuzziness of the principle. It is an effective tool for realizing qualitative and quantitative conversion. The accuracy of the model was evaluated with the Namerow integrated index method, and the results were compared with previous studies. The results showed that the soil fertility was grade II (Ex = 0.653), medium; soil pH was grade II (Ex = 4.59), strongly acidic; soil organic matter (OM) was grade V (Ex = 33.40), rich; total nitrogen (TN) was grade V (Ex = 1.53), rich; total phosphorus (TP) was grade III (Ex = 0.49), poor; total potassium (TK) was grade IV (Ex = 17.10), medium; available nitrogen (AN) was grade V (Ex = 128.30), rich; available phosphorus (AP) was grade II (Ex = 4.10), very poor; and available potassium (AK) was grade III (Ex = 52.73), poor. The degree of artificial influence of soil nutrients in Chinese fir plantations was ranked AN &gt; AK &gt; OM &gt; TK &gt; pH &gt; TN &gt; TP = AP. The soil fertility of the Chinese fir plantation was comprehensively evaluated by the cloud model and the evaluation results were unbiased and reliable. This method makes up for the shortcomings of traditional methods and provides new ideas for soil fertility evaluation. We suggest that this cloud model could be extended to other regions in the future and hope that there will be an article to compare several different statistical techniques.
Order-Preserving Wasserstein Discriminant Analysis

Supervised dimensionality reduction for sequence data projects the observations in sequences onto a low-dimensional subspace to better separate different sequence classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. This paper presents a linear method, namely Order-preserving Wasserstein Discriminant Analysis (OWDA), which learns the projection by maximizing the inter-class distance and minimizing the intra-class scatter. For each class, OWDA extracts the order-preserving Wasserstein barycenter and constructs the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the order-preserving Wasserstein distance between the corresponding barycenters. OWDA is able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments show that OWDA achieves competitive results on three 3D action recognition datasets.
Context‐based ensemble classification for the detection of architectural distortion in a digitised mammogram

The problem of computer-aided detection of architectural distortion (AD) in a digitised mammogram has been attempted in this manuscript. In examining a mammogram, the decision regarding a particular region of interest (RoI) is dependent on the appearance of the surrounding regions. However, in existing methods to detect AD the inference about an RoI is dependent on the appearance of this RoI alone. In addition, multiple radiologists infer the same mammogram in coming to a final decision about the mammogram. Contrary to popular ensemble classifiers like Adaboost and Random Forest, the authors propose an ensemble based method (imitating multiple radiologists by classifiers) for detecting AD such that the decision on a test RoI is dependent on the decisions of the surrounding RoIs in the proposed ensemble classifier. The proposed context-based ensemble classifier has been validated on two mammographic databases. The proposal shows promising results in both the databases.
Graph Filter Design Using Sum-of-squares Representation

Graph filters are an essential part of signal processing on graphs enabling one to modify the spectral content of the graph signals. This paper proposes a graph filter optimization method with an exact control of the ripple on the passband and the stopband of the filter. The proposed filter design method is based on the sum-of-squares representation of positive polynomials. The optimization of both FIR and ARMA graph filters is convex with the proposed method.
A Comparison of Type Theory with Set Theory

This paper discusses some of the ways in which Martin-Löf type theory differs from set theory. The discussion concentrates on conceptual, rather than technical, differences. It revolves around four topics: sets versus types; syntax; functions; and identity. The difference between sets and types is spelt out as the difference between unified pluralities and kinds, or sorts. A detailed comparison is then offered of the syntax of the two languages. Emphasis is put on the distinction between proposition and judgement, drawn by type theory, but not by set theory. Unlike set theory, type theory treats the notion of function as primitive. It is shown that certain inconveniences pertaining to function application that afflicts the set-theoretical account of functions are thus avoided. Finally, the distinction, drawn in type theory, between judgemental and propositional identity is discussed. It is argued that the criterion of identity for a domain cannot be formulated in terms of propositional identity. It follows that the axiom of extensionality cannot be taken as a statement of the criterion of identity for sets.
A Survey on Image Segmentation Techniques

The essential step in digital image processing is segmentation which can be used to partition the images into particular regions or objects and the level of partitioning depends on the individuality of the problem being solved. Segmentation of the image is widely categorized into two. The first one is Discontinuity which measures the sudden changes of intensities for partitioning the image and the other category Similarity is measured, based on the predefined methods such as thresholding, region growing, splitting and merging. The images are considered as inputs for performing segmentation and the result is attributes extracted from the images. Segmenting an image is an initial step to understand and analyze what is inside the image and this will be done mandatorily for all medical imaging analysis. Several segmentation techniques have been proposed in the past, but none of the segmentation methods are invented without any drawbacks. Hence, this study discusses a review on the various segmentation techniques of image which will help in further advancement in this field.
HypAR: Situated Mineralogy Exploration in Augmented Reality

Hyperspectral imaging, as a fast and cost effective method of mapping the composition of geological materials in context, is a key enabler for scientific discoveries in the geosciences. Being able to do this in-situ in real world context, possibly in real time, would have profound implications for geology and minerals exploration. This work addresses this important issue by developing an augmented reality application called HypAR that enables in-situ, interactive exploration of mineralogy spatially co-located and embedded with rock surfaces. User centred design is deployed to assure the utility and validity of the system. We describe the requirements analysis and design process for HypAR. We present a prototype using the Microsoft HoloLens that was implemented for a rock wall containing a wide range of minerals and materials from significant geological localities of Western Australia. We briefly discuss several use cases for which HypAR and extensions thereof may prove useful to geoscientists and other end users who have to make effective, informed decisions about the mineralogy of rock surfaces.
Security Vulnerabilities in Traditional Wireless Sensor Networks by an Intern in IoT, Blockchain Technology for Data Sharing in IoT

The Internet of Things (IoT) contributes significantly to transform and modernize the industry and society for digitizing the knowledge, hence that it can be sensed and actuated in real-time. The fast growth of connected devices across the globe anticipated to 50 billion by 2020, due to the intelligent connection of things, people, data, and process. Hence the security complications of IoT applications are essential to deal at various levels, but complexity advances due to the high heterogeneity of devices also lack in performance. A blockchain is a key technology to bring transaction processing and intelligence to devices as well as privacy issues, scalability and reliability problems in the IoT paradigm. The technology inclusion of blockchain and the IoT in the government system could accelerate communication among citizens, companies, and governments.
Learning Relational Fractals for Deep Knowledge Graph Embedding in Online Social Networks

Knowledge Graphs (KGs) have deep and impactful applications in a wide-array of information networks such as natural language processing, recommendation systems, predictive analysis, recognition, classification, etc. Embedding real-life relational representations in KGs is an essential process of abstracting facts for many important data mining tasks like information retrieval, privacy and control, enrichment and so on. In this paper, we investigate the embedding of the relational fractals which are learned from the Relational Turbulence profiles in the transactions of Online Social Networks (OSNs) into KGs. These relational fractals have the capability of building both compositional-depth hierarchies and shallow-wide continuous vector spaces for more efficient computations on devices with limited resources. The results from our RFT model show accurate predictions of relational turbulence patterns in OSNs which can be used to evolve facts in KGs for more accurate and timely information representations.
Advanced optimization-based neutrosophic sets for medical image denoising

Medical image denoising techniques play a critical role in computer-aided diagnosis systems. Recently, neutrosophic set-based denoising methods have proved their efficiency in different imaging modalities. However, to guarantee efficient mapping of the medical images to the NS domain, several operators in the NS process should be optimized. The chapter proposes a novel medical image denoising method based on an NS indeterminacy filter (IF) with an optimized kernel. Then, the proposed filter, called the optimized indeterminacy filter (OIF), is applied for denoising dermoscopy images. The experimental results included comparative studies of the proposed filter to other traditional denoising filters. Furthermore, different performance evaluation metrics were measured, proving the dominance of the proposed denoising technique. The results show that the proposed filter achieved the best maximum signal-to-noise ratio and peak signal-to-noise ratio of values 27.75 and 31.47, respectively. Furthermore, it achieved the best minimum mean squared error (MSE) and root of MSE (RMSE) of values 57.86 and 7.18, respectively.
Image-Based Facial Expression Recognition Using Local Neighborhood Difference Binary Pattern

Automatic facial expression recognition (FER) has gained enormous interest among the computer vision researchers in recent years because of its potential deployment in many industrial, consumer, automobile, and societal applications. There are a number of techniques available in the literature for FER; among them, many appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP) have been shown to be very efficient and accurate. In this paper, we propose a new descriptor called local neighborhood difference binary pattern (LNDBP). This new descriptor is motivated by the recent success of local neighborhood difference pattern (LNDP) which has been proven to be very effective in image retrieval. The basic characteristic of LNDP as compared with the traditional LBP is that it generates binary patterns based on a mutual relationship of all neighboring pixels. Therefore, in order to use the benefit of both LNDP and LBP, we have proposed LNDBP descriptor. Moreover, since the extracted LNDBP features are of higher dimension, therefore a dimensionality reduction technique has been used to reduce the dimension of the LNDBP features. The reduced features are then classified using the kernel extreme learning machine (K-ELM) classifier. In order to, validate the performance of the proposed method, experiments have been conducted on two different FER datasets. The performance has been observed using well-known evaluation measures, such as accuracy, precision, recall, and F1-score. The proposed method has been compared with some of the state-of-the-art works available in the literature and found to be very effective and accurate.
Deep learning model for object detection

Machine learning is entering in the everyday life of people in different forms. The reasons for this are the continuous development of computer systems, the increase of their computing power and the increase of data stored on electronic media. The main goals of developing self-learning models are to improve or replace existing methods for processing large amounts of information, to improve the services offered by different institutions, and generally to improve and facilitate the lifestyle of modern man. Machine learning can be used to detect complex relationships between a large set of input data, making it an appropriate method for solving a wide range of issues in different spheres such as Bioinformatics, Computer networks, Computer vision, Marketing, Medicine, Natural Language Processing (NLP) and many others.
Feature selection of Human Daily Activities using Ensemble method Classification

In Human activity recognition (HAR) research study it is a common practice using a wearable sensor to acquire the signal of human daily activities. In this study, database from smartphone inertial sensors is analysed for six different activities recognition. The aim of this paper is to compare different filter method feature selections for multiclass problem based on human daily living activities using the smartphone inertial sensor. Three components for HAR processing stage are involved that comprises of data filtering and segmentation, data feature extraction, feature selection of the data and classification. An ensemble method of Random subspace with Support vector machine is adapted for classification. Model evaluation of holdout and 10-fold cross-validation methods are implemented for classification assessment. The performance of all human daily activities is evaluated according to comparison of overall accuracy for four type filter method feature selection method. From the result findings, the number of features that reduce to 198 feature archived 98.89% compared to 561 numbers of features 98.74% of overall accuracy for holdout method. While using the 10 cross validation method, the numbers of features are reduced to 424 with the overall accuracy 99.28% compared to 561 number of features with 99.22% of overall accuracy.
A Robust Image Sequence Registration Algorithm for Videosar Combining Surf with Inter-Frame Processing

VideoSAR (video synthetic aperture radar) can provide continuous surveillance over a specific region, and shadow can be utilized to detect moving target. However, feature variations (unstable scattering properties and continuous motion) may seriously damage the precision of registration. Speeded Up Robust Feature (SURF) is one of the most popular method for feature detection and SAR image registration. So in this paper, a robust image sequence registration algorithm for videoSAR is proposed based on the combination of SURF and inter-frame processing. SURF is firstly introduced to videoSAR registration, whereas the continuous feature variation through the entire image sequence, showing a severe loss of robustness . Aiming at this defect, SURF and the idea of inter-frame processing are integrated and forming a novel algorithm. Simulation experiments underline the effectiveness and robustness of the proposed method in videoSAR image sequence registration.
Technical Debt and Waste in Non-functional Requirements Documentation: An Exploratory Study

Background: To adequately attend to non-functional requirements (NFRs), they must be documented; otherwise, developers would not know about their existence. However, the documentation of NFRs may be subject to Technical Debt and Waste, as any other software artefact. Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs documentation. Method: Based on a subset of data acquired from the most recent NaPiRE (Naming the Pain in Requirements Engineering) survey, we calculate, for a standard set of NFR types, how often respondents state they document a specific type of NFR when they also state that it is important. This allows us to quantify the occurrence of potential Technical Debt and Waste. Results: Based on 398 survey responses, four NFR types (Maintainability, Reliability, Usability, and Performance) are labelled as important but they are not documented by more than 22% of the respondents. We interpret that these NFR types have a higher risk of Technical Debt than other NFR types. Regarding Waste, 15% of the respondents state they document NFRs related to Security and they do not consider it important. Conclusions: There is a clear indication that there is a risk of Technical Debt for a fixed set of NFRs since there is a lack of documentation of important NFRs. The potential risk of incurring Waste is also present but to a lesser extent.
An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition

In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications. We consider a statistical framework based on a mixture of Multinomial Scaled Dirichlet (MSD) distributions that we have previously proposed in [1]. Given that the likelihood function plays a key role in statistical inference, e.g. in maximum likelihood estimation and Fisher information matrix investigation, we propose to improve the efficiency of computing the MSD log-likelihood by approximating its function based on Bernoulli polynomials. As compared to [1], the log-likelihood function is computed using the proposed mesh algorithm and a model selection approach is seamlessly integrated with the parameters estimation. The improved clustering framework offers a good compromise between other techniques and improves the approach used before for the same model. The merits of the proposed approach are validated via a challenging application that involves human action recognition.
Robotic Arthroscopy: The Uncertainty in Internal Knee Joint Measurement

We report a study that developed algorithms to measure the dimension and uncertainty range of free space inside the knee joint for the purpose of minimally invasive surgery. During knee arthroscopy, the patient's leg position is continuously adjusted to create the space for surgical instruments inside the joint. Surgeons 'feel' the force they apply to the leg and estimate the joint space from a 2D video. In many cases, they overestimate the instrument gap, resulting in damaging to the knee joint by pushing instruments through a gap that is too small. We used cadaveric experiments to inform the noise induced by the sensors and image processing steps, to derive an error point-cloud in a simulated environment. From the point-cloud, we calculate the instrument gap range inside the knee joint. For a selected surgical instrument gap size, the measurement algorithm is accurate to less than a millimetre. However, measurement errors introduce an uncertainty of 14%. The performance of our algorithms demonstrates the use of a single-lens arthroscope to measure the instrument gap to provide feedback to a surgeon or enable control of a robotic leg manipulator.