Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic in Predictive Harmonic Systems

From the risk prediction perspective it is important to be able to access any relevant information that may improve its accuracy. Many concerns arise regarding how well such information is being managed. From the traditional point of view, data can be numeric or nominal. These considerations affect the efficiency of the predictive model. Furthermore, an additional problem arises when the semantic interpretation is also taken into account: how precise would become the model? This paper introduces an approach called Fuzzy Harmonic Systems (FHS), an extension of Harmonic Systems (HS), an approach to perform adaptive predictions. The fuzziness may be a characteristic of data, or part of the process to improve flexibility. In the latter case, relaxing the original data derives in FHS patterns. This paper evaluates the efficiency of FHS compared to HS. Results indicate that even though the model is less precise, the predictive power increases, at a very low accuracy cost.
Hardware Design of Flight Control System and Flight Experiments of Small-Scale Unmanned Aerial Vehicle

In view of the strict requirements of small-scale unmanned aerial vehicle on the performance, volume and weight of flight control system, this paper presents a hardware design of flight control system based on STM32F103RCT6 microcontroller. In accordance with the system function and interface characteristics, the flight control system is divided into power module, main control module, sensor module and interface drive module, which meet the technical requirements of high integration, low coupling and expansibility of the flight control system. According to the characteristics of different types of sensor units integrated in the system, we design the interface circuit of inertial sensor, barometer and geomagnetic sensor based on I2C bus and GPS interface circuit based on UART. The flight experiments are carried out on the small-scale quadcopter platform. The results show that the flight control system designed in this paper has good practicability and high reliability, and meets the requirements of the small-scale unmanned aerial vehicle for the function and performance of the flight control system.
Comprehensive and Comparative Analysis of Different Files Using CP-ABE

In a business organization, the data needs to be shared among different personnel, therefore chances of fraud are more. In order to protect the organization’s confidential and sensitive data among different levels of employees from theft or any other illegal activity that violates the company security policy, ABE known as Attribute-Based Encryption was introduced. ABE is a cryptographic parameter that plays a vital role in providing fine-grained access control of outsourcing data in data sharing system of the business organization but due to its non-efficiency and several other limitations, it was proved inefficient in outsourcing of data. However, Cipher-text Policy Attribute-Based Encryption (CP-ABE) was implemented to overcome the existing ABE issues and to protect the firm’s confidential data from theft or leakage. CP-ABE defines an access policy that covers all the attributes within the system. In this scheme, the user’s private key is associated with the set of attributes for encryption and decryption purposes. Our experimental simulations demonstrate the real encryption/decryption time over different types of files and their sizes and how they get affected after applying CP-ABE. The performance and security analysis stipulated that the used scheme is competent to securely manage the data in any organization.
Adaptive Fractional Order Total Variation Image Denoising via the Alternating Direction Method of Multipliers

Due to the importance and priority, image denoising has various applications in image processing. How to mitigate the effect of staircase artifacts on denoised results and adjusts regularization parameters based on surrounding features are two main issues in the image denoising regularization process,. In this paper, an adaptive fractional order total variation l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization (AFOTV-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) model is proposed. Regularization parameters are adaptively adjusted by fractional order α . Alternating direction method of multipliers (ADMM) algorithm is introduced for solving AFOTV-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> model, which provides a feasible method to solve the optimization problems of image denoising by introducing auxiliary variables. Two experiments have been conducted with standard images "Lena" and "Man" to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method has good denoising effect and fast convergence rate for image denoising.
Linear Interpolation-Based Fuzzy Clustering Approach for Missing Data Handling

Clustering of incomplete data set containing missing values is a common problem in the literature. Methods to handle this problem have vast variations, including several imputation as well as non-imputation techniques for clustering. In this work, we have described the analysis of different approaches explored for handling missing data in clustering. The aim of this paper is to compare several FCM clustering approaches based on imputation and non-imputation strategies. Experimental results on one artificial and four real-world data sets from UCI repository show that linear interpolation-based FCM clustering approach performs significantly better than other techniques for these data sets.
Multiview Alignment and Generation in CCA via Consistent Latent Encoding

Multiview alignment, achieving one-to-one correspondence of multiview inputs, is critical in many real-world multiview applications, especially for cross-view data analysis problems. An increasing amount of work has studied this alignment problem with canonical correlation analysis (CCA). However, existing CCA models are prone to misalign the multiple views due to either the neglect of uncertainty or the inconsistent encoding of the multiple views. To tackle these two issues, this letter studies multiview alignment from a Bayesian perspective. Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multiview inputs by matching the marginalization of the joint distribution of multiview random variables under different forms of factorization. To realize our design, we present adversarial CCA (ACCA), which achieves consistent latent encodings by matching the marginalized latent encodings through the adversarial training paradigm. Our analysis, based on conditional mutual information, reveals that ACCA is flexible for handling implicit distributions. Extensive experiments on correlation analysis and cross-view generation under noisy input settings demonstrate the superiority of our model.
A Comparative Analysis of Clustering Quality Based on Internal Validation Indices for Dimensionally Reduced Social Media Data

Almost all modern industries leverage data analytics to deal with various dimensions of their business like demand forecasting, targeted marketing, and supply chain planning. In addition to historic data, social media data has also become a prominent source of input for data analytics. The key challenges observed with social media data are its huge volume and high dimensions that need to be dealt with. Clustering is the proven strategy in data analytics to segregate the relevant data for processing and thereby reducing the impact of huge volume. Dimensionality corresponds to the diverse features of the data subject being represented. The application of dimensionality reduction techniques can help in reducing the computational intensiveness caused by the curse of dimensionality. This paper covers an experimental analysis using four popular dimensionality reduction techniques – two linear and two nonlinear approaches – to verify the impact of dimensionality reduction on cluster quality using internal clustering validation indices.
An interview with Lana Yarosh

In this series of interviews with innovation leaders, Ubiquity Associate Editor and software engineer, Dr. Bushra Anjum, sits down with Prof. Lana Yarosh of the University of Minnesota to discuss geophysical and emotional distances as they relate to social disconnection and our evergrowing dependence on technology.
Redesign of Application with Notification of 4G Drive Test Results Cellular Network Case Study Area Coverage of Universitas Singaperbangsa Karawang

Radio Frequency (RF) data files taken from the coverage of the 4G Base Transceiver Station (BTS) are very important to control their work performance. The length of the drive test process starting from collecting data to produce extracted RF data files that raises the opportunity for manipulation of RF data files before received by the data center. Bad RF data converted into good RF data. Field manager of the 4G RF network quality requires real-time information and does not experience delays when receiving RF data files in the data center. To overcome this problem, an information system was made. The system aims to send information by e-mail that RF data files have been collected and extracted to the field manager automatically. The system made using SMTP and PHP languages. The results of this study prove the delivery of information in real-time and the speed of the information process minimizes the occurrence of data manipulation.
Quantum Consensus

In this paper, we propose a novel consensus mechanism utilizing the quantum properties of qubits. This move from classical computing to quantum computing is shown to theoretically enhance the scalability and speed of distributed consensus as well as improve security and be a potential solution for the problem of blockchain interoperability. Using this method may circumvent the common problem known as the Blockchain Trilemma, enhancing scalability and speed without sacrificing de-centralization or byzantine fault tolerance [1]. Consensus speed and scalability is shown by removing the need for multicast responses and exploiting quantum properties to ensure that only a single multicast is required. We also leverage work done on the E91 quantum key distribution protocol [2] to securely transmit values and prevent a man-in-the-middle attack or system disturbance, enhancing confidentiality and integrity of transmitted information. Distributed ledger interoperability is explored by proposing a system to achieve a verifiable bridge for private transactions between a small private network and its corresponding consortium network. A proof-of-concept using IBM’s Qskit is shown from which initial results appear to show a strong sensitivity to non-consensus which could be useful in many applications. The present practical feasibility of the above is shown and future work is explored.
Recognition of Online Handwritten Gurmukhi Characters Through Neural Networks

This paper recognizes online handwritten Gurmukhi characters and words. The neural network-based recognition for online handwritten Gurmukhi characters and words has been observed first time in this study. In this work, a scheme is proposed to develop a feature vector and its use as an input to neural network recognition engine. A set of low-level, high-level, and Gabor features are extracted, and a feed-forward neural network is trained to recognize 40 classes of Gurmukhi characters. This work implements rearrangement of strokes stage after recognition and post-processing stages. The results have been achieved as 93.53% and 83.69% for 4511 Gurmukhi characters and 2576 Gurmukhi words, respectively.
A Robust Deep Model for Human Action Recognition in Restricted Video Sequences

In this paper, we propose an action recognition algorithm in noisy data conditions with Convolutional Neural Network (CNN) as the front end and Deep Bidirectional Long Short Term Memory (DBi-LSTM) as the backend. The deep features are extracted from the input frames using a VGG16 model. The sequential information among frames is learned using the DBi-LSTM part, which is composed of three layers stacked together in both forward and backward directions to increase the learning depth. The proposed algorithm achieved 96.77% vs. 96.76% and 95.83% vs. 91.60% accuracy of the baseline methods on KTH and YouTube datasets, respectively. Moreover, the proposed algorithm has shown significant robustness in noisy training data as the accuracy drops only 1% down.
Influence of Shape, Density, and Edge Crossings on the Perception of Graph Differences

The perception of differences between graphs represented as node-link diagrams is an important issue in many disciplines. This paper presents results from a study with 40 participants. The goal of the study was to test whether shape, density, and edge crossings of the graph influence the perception of differences between graphs and the order in which they are perceived. The participants worked under time constraints. Our results indicate that an increase in density lowers the recognition of differences while a newly introduced edge crossing helps to spot a change. Shape did not have a significant influence on the perception of differences.
A Model-Agnostic Recommendation Explanation System Based on Knowledge Graph

Recommender systems have been gaining attention in recent decades for the ability to ease information overload. One of the main areas of concern is the explainability of recommender systems. In this paper, we propose a model-agnostic recommendation explanation system, which can improve the explainability of existing recommender systems. In the proposed system, a task-specialized knowledge graph is introduced, and the explanation is generated based on the paths between the recommended item and the user's history of interacted items. Finally, we implemented the proposed system using Wikidata and the MovieLens dataset. Through several case studies, we show that our system can provide more convincing and diverse personalized explanations for recommended items compared with existing systems.
Low-Light Image Brightening via Fusing Additional Virtual Images

Capturing high-quality images via mobile devices in low-light or backlighting conditions is very challenging. In this paper, a new, single image brightening algorithm is proposed to enhance an image captured in low-light conditions. Two virtual images with larger exposure times are generated to increase brightness and enhance fine details of the underexposed regions. In order to reduce the brightness change, the virtual images are generated via intensity mapping functions (IMFs) which are computed using available camera response functions (CRFs). To avoid possible color distortion in the virtual image due to one-to-many mapping, a least square minimization problem is formulated to determine brightening factors for all pixels in the underexposed regions. In addition, an edge-preserving smoothing technique is adopted to avoid noise in the underexposed regions from being amplified in the virtual images. The final brightened image is obtained by fusing the original image and two virtual images via a gradient domain guided image filtering (GGIF) based multiscale exposure fusion (MEF) with properly defined weights for all the images. Experimental results show that the relative brightness and color are preserved better by the proposed algorithm. The details in bright regions are also preserved well in the final image. The proposed algorithm is expected to be useful for computational photography on smart phones.
Truth Graph: A Novel Method for Minimizing Boolean Algebra Expressions by Using Graphs

Boolean algebra expressions are used by stakeholders from a range of disciplines, such as engineers, to build logic circuits. To reduce the cost and transaction numbers of logic circuits, engineers minimize Boolean expressions by reducing the number of terms or arithmetic operations. A well-known pictorial method applied for minimizing Boolean expressions is the Karnaugh map. However, a drawback is that these maps are not effective for minimizing Boolean expressions involving more than four variables. We introduce a novel method for minimizing Boolean expressions by using graphs, which we call a “Truth Graph” that can be effectively applied when many variables exist.
Noise Reduction with Detail Preservation in Low-Dose Dental CT Images by Morphological Operators and BM3D

Compared to other traditional imaging exams, computed tomography (CT) is more efficient, where a digital geometry processing is used to generate a 3D image of an internal structure of an object, or patient, from a series of 2D images obtained during various rotations of the CT scan around the scanned object. Also taking in consideration traditional imaging exams such as MRI or ultrasound, for example, the CT technique uses higher radiation doses than these exams, providing high quality images. However, in order to prevent constant exposures to high radiation doses, low-dose computed tomography (LDCT) scans are often recommended. Nevertheless, the images acquired in LDCT scans are degraded by undesirable artifacts, known as noise, which affects negatively the image quality. In this study, a two-stage filter based on morphological operators and Block-Matching 3D (BM3D) is proposed to remove noise in low-dose dental CT images. The quantitative results obtained by our proposed method demonstrated superior performance when compared to several state of the art techniques. Also, our proposed method obtained better visual performance, removing the noise and preserving details more efficiently than the compared filters.
Pinning down text complexity

Abstract In this article, we present the results of a corpus-based study where we explore whether it is possible to automatically single out different facets of text complexity in a general-purpose corpus. To this end, we use factor analysis as applied in Biber’s multi-dimensional analysis framework. We evaluate the results of the factor solution by correlating factor scores and readability scores to ascertain whether the selected factor solution matches the independent measurement of readability, which is a notion tightly linked to text complexity. The corpus used in the study is the Swedish national corpus, called Stockholm-Umeå Corpus or SUC. The SUC contains subject-based text varieties (e.g., hobby), press genres (e.g., editorials), and mixed categories (e.g., miscellaneous). We refer to them collectively as ‘registers’. Results show that it is indeed possible to elicit and interpret facets of text complexity using factor analysis despite some caveats. We propose a tentative text complexity profiling of the SUC registers.
Quantum Computation and Its Effects in Database Systems

Classically, searching an unsorted database requires a linear search, which is $$\mathcal {O}(n)$$ in time. Using Grover's quantum search algorithm, it is possible to do it in $$\mathcal {O}(\sqrt{n})$$ time which is a quadratic speedup compared to its classical counterpart. The aim of this research is to exploit this speedup and find other applications in different algorithms commonly used in database systems.
Model-Free Variable Selection With Matrix-Valued Predictors

We introduce a novel framework for model-free variable selection with matrix-valued predictors. To test the importance of rows, columns, and submatrices of the predictor matrix in terms of predicting the response, three types of hypotheses are formulated under a unified framework. The asymptotic properties of the test statistics under the null hypothesis are established and a permutation testing algorithm is also introduced to approximate the distribution of the test statistics. A maximum ratio criterion (MRC) is proposed to facilitate the model-free variable selection. Unlike the traditional stepwise regression procedures that require calculating p-values at each step, the MRC is a noniterative procedure that does not require p-value calculation and is guaranteed to achieve variable selection consistency under mild conditions. Performance of the proposed method is evaluated in extensive simulations and demonstrated through the analysis of an electroencephalography data. Supplementary materials for this article are available online.
Hybrid features for skeleton‐based action recognition based on network fusion

Abstract In recent years, the topic of skeleton‐based human action recognition has attracted significant attention from researchers and practitioners in graphics, vision, animation, and virtual environments. The most fundamental issue is how to learn an effective and accurate representation from spatiotemporal action sequences towards improved performance, and this article aims to address the aforementioned challenge. In particular, we design a novel method of hybrid features' extraction based on the construction of multistream networks and their organic fusion. First, we train a convolution neural networks (CNN) model to learn CNN‐based features with the raw skeleton coordinates and their temporal differences serving as input signals. The attention mechanism is injected into the CNN model to weigh more effective and important information. Then, we employ long short‐term memory (LSTM) to obtain long‐term temporal features from action sequences. Finally, we generate the hybrid features by fusing the CNN and LSTM networks, and we classify action types with the hybrid features. The extensive experiments are performed on several large‐scale publically available databases, and promising results demonstrate the efficacy and effectiveness of our proposed framework.
The risks of misunderstandings in family discourse

Abstract Based on a large dataset of Russian material, the paper presents these general features of the home: a place to spend leisure time containing a long-established group of different ages and sexes free to move about in their environment These factors lead to tension between communicants and a diversity of topics of conversation. Inadequate recipient design is an overarching trigger for misunderstanding caused by the speaker. It derives from poor concentration on interaction and the common ground fallacy, and leads to the frequent use of indirect and elliptical expressions. Inadequate concentration causes the recipient to non-listen and overguess. Finally, misunderstandings occur because of mishearings, misinterpretations and misreferences. Misinterpretation may concern the content, intention or mode of the message.
Global Path Planning for USV Waypoint Guidance System Using Dynamic Programming

An analytical method for finding the optimal route to be followed by an Unmanned Surface Vehicle (USV) is proposed. The proposed algorithm aims to generate a collision-free route with minimum travel time in reaching some given waypoints. The method comprises of two algorithms. First, a grid map-based algorithm for static obstacle avoidance by forming sub-waypoints at the tangent angle of the obstacle using the weight of each obstacle to generate feasible paths. The obstacle weights are determined based on whether the neighboring cells are also obstacles or not. Second, the dynamic programming-based path planning optimization to find several shortest paths among those feasible paths. The USV is modeled in a Six-Degree-of-Freedom (6-DOF) equations of motion whose parameters are determined based on a physical model USV. Simulation results are presented to show the method successfully guides the USV without crashing into obstacles. As shown, due to the USV's limited maneuverability, the shortest path obtained using dynamic programming is not always the path with the shortest travel distance, and the shortest travel distance does not guarantee the shortest travel time either. As a comparison, a genetic algorithm has been conducted to produce the optimal route. Simulation results show that the proposed algorithm produces an 11.6% reduction in travel time and a 99.55% reduction in computing time than the genetic algorithm.