Reviewerly/exHarmony huggingface dataset:
SMOTE Variants for Imbalanced Binary Classification: Heart Disease Prediction

Author(s): Zheng, Xiaoru | Advisor(s): Li, Jingyi | Abstract: Class imbalance is prevalent in many medical diagnosis problems, where the number of patients suffering from a particular disease is much smaller than the number of healthy people in the population. A similar phenomenon also occurs in credit card fraud detection and spam email filtering. Approaches to dealing with imbalanced data can be divided into those at data and algorithm levels. At the data level, resampling techniques such as oversampling and undersampling can result in a more balanced distribution of classes. At the algorithm level, cost-sensitive learning takes the prediction error into account in the training process, which can achieve better overall prediction performance. In this study, variants of the Synthetic Minority Oversampling Technique (SMOTE) are implemented for comparison: regular SMOTE, Borderline-SMOTE, SVM-SMOTE, KMeans-SMOTE, which are combined four classification algorithms under the classical and Neyman-Pearson (NP) paradigms to build predictive models on the heart disease data, and the performance of these models is compared. Our results show that the SVM-SMOTE and the Borderline-SMOTE outperform other SMOTE variants, and the NP classification is superior in controlling the type I error effectively.
A Multiplicative Noises and Additive Correlated Noises Cubature Kalman Filter and Its Application in Quadruped Robot

The traditional Cubature Kalman Filter (CKF) and its derived algorithms cannot work without the two hypotheses of Kalman Filter (KF), one is that the system model is accurate and the other is the system is only influenced by independent white noise with known statistical characteristics. However, it is difficult to fully guarantee the above hypotheses in actual operation. The presence of multiplicative noise undermines the first hypothesis, while the additive noise correlation undermines the second hypothesis. Given invalid hypotheses, the estimated performances of CKF and its derivative algorithms are degraded drastically. To solve this issue, this article proposes Multiplicative Noises and Additive Correlated Noises Cubature Kalman Filter (MACNCKF), which solves state estimation problems involving multiplicative noise and additive noise while maintaining CKF advantages. Moreover, this algorithm has been testified to be correct and easy to be transplanted to CKF and its derivative algorithm after strict mathematical derivation. When the system lacks multiplicative noise and additive noise, the algorithm is degraded to corresponding algorithm in a general form, which helps extend the application environment of CKF and its derivative algorithms, thus improving robustness. Numerical simulation and experiments on quadruped robot system indicate that MACNCKF can effectively solve state estimation problem involving both multiplicative noise and additive noise. Given time consumption in MACNCKF basically comparable to that in CKF, MACNCKF has improved estimation accuracy, robustness and reliability.
Graphical Data Sets as Compositional Structure: Sonification of Color Graphs in <i>RGB</i> for Clarinet and Piano

Abstract This article follows the methodology behind the composition of the author's piece RGB (2019) for clarinet and piano, a sonification of four color graphs generated from Jackson Pollock's Out of the Web (1949). The article demonstrates the process of “mapping” data to sound while creating allowances for compositional intuition. In this way, the author hopes to demonstrate the usefulness and flexibility of composing with this approach, as well as its future implications and potential improvements, while acknowledging that this is a specific example of such an approach rather than an all-encompassing taxonomy for any visual input.
Indian Sign Language Recognition on PYNQ Board

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.
Delivery of omnidirectional video using saliency prediction and optimal bitrate allocation

In this work, we propose and investigate a user-centric framework for the delivery of omnidirectional video (ODV) on VR systems by taking advantage of visual attention (saliency) models for bitrate allocation module. For this purpose, we formulate a new bitrate allocation algorithm that takes saliency map and nonlinear sphere-to-plane mapping into account for each ODV and solve the formulated problem using linear integer programming. For visual attention models, we use both image- and video-based saliency prediction results; moreover, we explore two types of attention model approaches: (i) salient object detection with transfer learning using pre-trained networks, (ii) saliency prediction with supervised networks trained on eye-fixation dataset. Experimental evaluations on saliency integration of models are discussed with interesting findings on transfer learning and supervised saliency approaches.
A Novel Anti-attack Revenue Optimization Algorithm in the Proof-of-Work Based Blockchain

To improve the revenues of both the attack mining pools and miners under block withholding attack, this paper proposes a novel anti-attack mining revenue optimization algorithm (MROA) in the Proof-of-work (PoW) based blockchain. MROA considers the situation that the miners have the flexibility to select an attack mining pool to join and carry out honest mining or attack on other mining pools. The proposed approach achieved the revenue optimization on each attack mining pool, and maintained high group efficiency of the mining pools under block withholding attack by utilizing a modified artificial bee colony algorithm based on Pareto to solve the models. Furthermore, MROA could obtain a composition scheme of each attack mining pool and work scheme of each miner under block withholding attack. The comprehensive evaluation demonstrates that MROA can always identify the optimized miner work scheme for each attack mining pool with any miner population. Our test result of MROA also outperforms the state-of-arts such as ABC, NSGA2 and MOPSO with reduces miner revenue variance and spacing value of solution set.
Design of Vehicle Remote Monitoring System Based on LoRa Technology and QT Platform

Due to the rapid growth in the number of urban vehicles especially electrical bikes and electrical cars, the dispatch management and safety issues of electrical bikes have become increasingly critical and prominent. With the development of IoT communication technology, traditional wireless technologies such as Bluetooth, ZigBee, WiFi and GPRS have some shortcomings for examples short communication distances and large power consumption. However, LoRa wireless communication technology has the advantages of long communication distance and low power consumption. Therefore, this paper proposes the remote monitoring vehicle system for electrical bikes based on LoRa wireless communication technology. The system consists of electrical bikes terminal and a remote monitoring center. The electrical bikes terminal adopts the architecture association of ARM, GPS and LoRa to complete the acquisition and wireless transmission of electrical bikes position information. The GPS module provide the position information, and the ARM transmit the location information to the monitoring center by LoRa wireless communication. The remote monitoring center is based on QT and GIS technology to complete the real-time display of the electrical bikes. The actual test results show that the system can meet the monitoring requirements, the communication quality is reliable, the communication distance is long, and it can be displayed in real time, dynamic and real scene.
Impossible and Conflicting Obligations in Justification Logic

Different notions of the consistency of obligations collapse in standard deontic logic.In justification logics, which feature explicit reasons for obligations, the situation isdifferent. Their strength depends on a constant specification and on the availableset of operations for combining different reasons. We present different consistencyprinciples in justification logic and compare their logical strength. Further, we proposea novel semantics for which justification logics with the explicit version of axiom D,jd,are complete for arbitrary constant specifications. We then discuss the philosophicalimplications with regard to some deontic paradoxes.
A Graph Partitioning Algorithm for Edge or Vertex Balance

The definition of effective strategies for graph partitioning is a major challenge in distributed environments since an effective graph partitioning allows to considerably improve the performance of large graph data analytics computations. In this paper, we propose a multi-objective and scalable Balanced GRAph Partitioning (B-GRAP) algorithm to produce balanced graph partitions. B-GRAP is based on Label Propagation (LP) approach and defines different objective functions to deal with either vertex or edge balance constraints while considering edge direction in graphs. The experiments are performed on various graphs while varying the number of partitions. We evaluate B-GRAP using several quality measures and the computation time. The results show that B-GRAP (i) provides a good balance while reducing the cuts between the different computed partitions (ii) reduces the global computation time, compared to Spinner algorithm.
Invisible Colour Image Watermarking Technique for Colour Images Using DWT and SVD

Recent popularity of social media and content sharing networks paired with fast Internet access has paved the way for mass multimedia content sharing on the Internet. As a result, volumes of sensitive data are been shared in various digital forms such that the risk of copyright infringements and misappropriations of digital content has grown at the same rate. In order to preserve copyright or authenticate digital content, digital watermarking has been considered as a viable solution for these problems. In the domain of digital watermarking, embedding a colour image as an invisible watermark to a colour cover image remain a challenge. As a solution to this problem, this paper presents such an invisible watermarking scheme based on DWT and SVD. The watermark created using the novel approach is experimentally shown to have the robustness, imperceptibility and capacity requirements.
Hybrid method combining orthogonal projection Fourier transform profilometry and laser speckle imaging for 3D visualization of flow profile

This paper reports a straightforward technique for three-dimensional (3D) visualization of a flow profile by a hybrid algorithm combining Fourier transform orthogonal fringe projection and laser speckle imaging techniques. The use of orthogonal projection aims to suppress the zero order allowing surface reconstruction with high spatial resolution and accuracy while analyzing the intensity fluctuations of diffuse backscattered laser light providing 2D flow information. Once both are achieved, 3D flow visualization can be displayed. The method is experimentally validated first with a plastic tube filled with scattering liquid (milk) running at various controlled flow rates and then with the tube embedded under scattering layers (chicken breast) of varying thickness. The system includes a single, common camera, a commercial projector (profilometry channel), a laser light source (flow channel), and a computer station. In addition, orthogonal projection processing was combined with Hilbert transform, increasing the visualization and resolution of the measured flow profile.
Drilling Localization and Error Analysis of Radial Horizontal Jet Drilling Based on Magnetic Gradient Tensor

To obtain the real-time drilling trajectory of radial horizontal jet drilling (RHJD), a magnetized nozzle localization method based on magnetic gradient tensor (MGT) is proposed. The MGT system consisting of five tri-axial magnetometers, a tri-axial accelerometer, and a tri-axial gyroscope are installed in the casing. The magnetized nozzle is made of a strong magnetic permanent magnet whose position can be obtained by measuring the MGT generated by itself at the measurement point. The simulation and experiment of the localization method are carried out, which show that the method has high accuracy. When the detection distance is 40 m, the error is only 0.07 m, which meets the requirements of engineering practice. The accuracy of the magnetometer, the baseline distance and the magnetization strength of the nozzle are the main factors affecting the localization error. This localization method can meet the requirements of positioning accuracy in practical engineering, which greatly avoids the drilling failure of RHJD and has a wide application prospect.
Modified grasshopper optimisation algorithm

The grasshopper optimization algorithm (GOA) mimics the foraging behavior of grasshopper insects. It is one of the youngest and widespread algorithms for optimization. In GOA exploration and exploitation depends on coefficient c used in position update process. So as to improve balancing in exploration and exploitation this paper introduced modified coefficient c for fine tuning these to contradictory process while searching for optimum solution. The new value of c is decided adaptively and stimulated by hyperbolic function. The anticipated algorithm is named as modified GOA (mGOA) and tested over a standard set of benchmark problems. Outcomes proves that mGOA outperformed considered algorithm for more than 90% problems.
The Complexity Assessment Method of Logical Reasonings

The development of methods for assessing the various didactic characteristics of teaching texts and teacher messages is an urgent problem of modern teaching theory. Its solution allows to rank texts (descriptions, proofs, explanations, etc.) according to the degree of complexity, and this helps to optimize the learning process and increase the objectivity of assessing students' knowledge. In general, educational texts contain a descriptive-narrative and logical component; the share of the latter can be defined as the ratio of informative content of logical reasonings to the overall informative content of the text. The complexity (informativity) of the text can be found as a sum of the com-plexities of its constituent scientific terms and ordinary words. The semantic complexity of the term relative to the Z0 thesaurus is equal to the number of words that need to be spoken to explain the term to a student with the thesaurus Z0. It should be noted that each new mention of the term reduces its contribution to the overall complexity of the text. The proposed method for assessing the complexity of logical reasonings involves: 1) identifying all facts and logical rules used, the complexity of which exceeds the cho-sen level of knowledge; 2) expanding the text by adding all the statements necessary for conducting reasonings; 3) creation of text file text.txt containing verbal part of educa-tional text and verbally encoded formulas and figures; 4) creation of a dictionary (slo-var.txt file) containing the scientific terms used with indication of their level of com-plexity; 5) analysis of the text.txt file using the special computer program addressing to the dictionary which allows to determine the overall semantic complexity of the eva-luated text; 6) isolating logical reasonings from the text and determining their total complexity; 7) finding the share of logical reasoning in the text. The complexity of four text fragments was estimated, for which the share of logical reasonings and the average coefficient of information folding were calculated.
Multiple Images Steganography of JPEG Images Based on Optimal Payload Distribution

Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with three parameters based on the exponential distribution. The related model is shown to be skillful to the fitting of various lifetime data, more or less heterogeneous. Among all the possible applications, we consider two data sets of current interest, linked to the COVID-19 pandemic. They concern daily cases confirmed and recovered in Pakistan from March 24 to April 28, 2020. As a result of our analyzes, the proposed model has the best fitting results in comparison to serious challengers, including the former odd Fréchet model.
An unsupervised approach to disjointness learning based on terminological cluster trees

In the context of the Semantic Web regarded as a Web of Data, research efforts have been devoted to improving the quality of the ontologies that are used as vocabularies to enable complex services based on automated reasoning. From various surveys it emerges that many domains would require better ontologies that include non-negligible constraints for properly conveying the intended semantics. In this respect, disjointness axioms are representative of this general problem: these axioms are essential for making the negative knowledge about the domain of interest explicit yet they are often overlooked during the modeling process (thus affecting the efficacy of the reasoning services). To tackle this problem, automated methods for discovering these axioms can be used as a tool for supporting knowledge engineers in modeling new ontologies or evolving existing ones. The current solutions, either based on statistical correlations or relying on external corpora, often do not fully exploit the terminology. Stemming from this consideration, we have been investigating on alternative methods to elicit disjointness axioms from existing ontologies based on the induction of terminological cluster trees, which are logic trees in which each node stands for a cluster of individuals which emerges as a sub-concept. The growth of such trees relies on a divide-and-conquer procedure that assigns, for the cluster representing the root node, one of the concept descriptions generated via a refinement operator and selected according to a heuristic based on the minimization of the risk of overlap between the candidate sub-clusters (quantified in terms of the distance between two prototypical individuals). Preliminary works have showed some shortcomings that are tackled in this paper. To tackle the task of disjointness axioms discovery we have extended the terminological cluster tree induction framework with various contributions: 1) the adoption of different distance measures for clustering the individuals of a knowledge base; 2) the adoption of different heuristics for selecting the most promising concept descriptions; 3) a modified version of the refinement operator to prevent the introduction of inconsistency during the elicitation of the new axioms. A wide empirical evaluation showed the feasibility of the proposed extensions and the improvement with respect to alternative approaches.
Properties of LSM-Estimator of Correlation Function of Biperiodically Correlated Random Processes

Recurrence and stochasticity are the features of many oscillation processes which occur in the different fields of science and techniques. Nowadays the models in the form of periodically correlated random processes (PCRP) are successfully used for the analysis of these processes. PCRP-approach provided more efficient solution to problem of signal transformation in communication theory, technical and medical diagnosis, energetics, forecasting of geophysical processes. Based on the experimental data coherent and component methods, the least square method, the linear filtration method have been developed for estimating correlation and spectral characteristics of PCRP. However, when analyzing oscillations there are situations when stochastic recurrence of one period interacts with stochastic recurrence of other. To analyze properties of double rythmicity the use is made of its models in the form of biperiodically correlated random processes (BPCRP). With close combination frequencies the application of the component method for estimating BPCRP characteristics can lead to significant leakage errors. As shown in this paper using the least square method helps to avoid these errors. Analysis of features of correlation functions estimates was carried out based on solution of the matrix equation providing the necessary conditions for the quadratic functional minimum. An expression is obtained for biasing estimates arising due to a preliminary definition of mathematical expectation. It is shown that the condition for the asymptotical unbiasedness of estimates is the attenuation of the correlation relationship with an increase in the bias. This condition also provides the mean square convergence of estimates for Gaussian BPCRP. Expression for the variance of LSM-estimator of correlation function compared with the variance of the component estimator contains additional components dependent of combinative frequencies and tending to zero when the length of realization is growing. Consideration was given to the example of LSM-estimating the correlation function of quadrature BPCRP-model and the comparison of efficiency of component and LSM-estimators was carried out.
Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts

Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically generated summaries the most useful when they are generated using word similarity and based on the eight most relevant software artefacts.