Reviewerly/exHarmony huggingface dataset:
Segmentation of Noisy Mammograms Using Hybrid Techniques

Breast cancer is the most routinely identified carcinoma among women in India, and it is one of the foremost causes of cancer death in women. Radiologists prefer mammograms for visualizing breast cancer. Different types of noises including Gaussian noise and salt-and-pepper noise affect the mammograms leading to inaccurate classification. Mammograms consist of numerous artifacts too, which depressingly affect the finding of breast cancer. The existence of pectoral muscles makes anomaly finding a cumbersome task. The recognition of glandular tissue in mammograms is vital in assessing asymmetry between left and right breasts and in conjecturing the radiation risk associated with screening. Thus, the proposed method focuses on improving the segmentation accuracy of noisy mammograms. It involves preprocessing which includes denoising using a pretrained convolutional neural network, artifacts removal using thresholding, and modified region growing and enhancement using two-stage adaptive histogram equalization along with segmentation of mammogram images into sections conforming to different densities using K-means clustering. The projected method has been confirmed on the Mini-MIAS database with ground truth provided by expert radiologists. The results illustrate that the proposed method is competent in eradicating noise and pectoral muscles without degrading quality and contrast and in fragmenting different denoised mammograms into different mammographic densities with high accuracy.
Learning Laplacian Matrix for Smooth Signals on Graph

Learning a useful Laplacian matrix plays a significant role in graph learning. This paper focuses on smoothness analysis, which leads to the concept of total variation (TV) on graphs, a new learning Laplacian matrix framework and solving it via convex optimization techniques. We show that smoothness analysis leads to an another form that gives a more sophisticated characterization for smooth signals. This unified theoretical framework can learn a meaningful Laplacian matrix by the constraint of total variation. In our experiments, this framework is demonstrated by using three different data sets.
Identifying Fraudulent Behaviors in Healthcare Claims Using Random Forest Classifier With SMOTEchnique

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. Existing studies have a lack of real data for analysis and focus on a very partial version of the problem by covering only a specific actor, healthcare service, or disease. In this article, the proposed strategy identifies fraudulent behaviors in Medicare claims data using several predictors as model inputs. The methodology involves preprocessing and model development phases. At the initial phase, the feature mining is done by estimating their feature importance score. The labeling of instances by using the classification rules to the whole dataset. Thus, a transformed dataset is obtained by the model. In the development phase, the RF with SMOTE is applied against the training and testing data. Specifically, SMOTE adapted to balance data and sorts misclassified instances and finds the interesting instances. The results of the proposed model improvises the classifier performance RF with SMOTE when contrast with RF method.
Multi-attribute Guided Painting Generation

Controllable painting generation plays a pivotal role in image stylization. Currently, the control way of style transfer is subject to exemplar-based reference or a random one-hot vector guidance. Few works focus on decoupling the intrinsic properties of painting as control conditions, e.g., artist, genre and period. Under this circumstance, we propose a novel framework adopting multiple attributes from the painting to control the stylized results. An asymmetrical cycle structure is equipped to preserve the fidelity, associating with style preserving and attribute regression loss to keep the unique distinction of colors and textures between domains. Several qualitative and quantitative results demonstrate the effect of the combinations of multiple attributes and achieve satisfactory performance.
Numerical integration of slope data with application to deflectometry

This work presents a stable noise-robust numerical integration technique derived from a gradient representation of the Q-Forbes polynomials for surfaces with axial symmetry. This modal-integration technique uses an orthogonalization process through the Householder reflections to obtain a numerically orthogonal set for the surface slopes that is used to reconstruct the surface shape. It is shown that for typical Deflectometry measurements, the resulting random component of the uncertainty after numerical integration has a root mean square error well below 1nm.
A Subjective Quality Assessment Database for Mobile Video Coding

As mobile devices and network popularize significantly, increasing number of videos are captured and viewed with mobile devices. In this paper, we introduce a new subjective quality database for mobile videos. The database contains 128 distorted videos generated from 32 source videos captured with different mobile phones. Among the source videos, 28 out of 32 are in portrait mode that occupies a significant portion in current mobile video applications. Distortions are introduced by compressing the videos in different bitrates. The subjective quality rating tests are conducted on mobile devices, involving 23 naive subjects and finally resulting in 2816 quality rating samples and corresponding mean opinion scores (MOSs) for all the 128 distorted videos. Furthermore, we also evaluate some objective quality assessment models on the database to verify their capability on predicting the quality of mobile videos. The proposed database will be made public to facilitate future research on mobile video quality assessment.
Strong Asymptotic Composition Theorems for Sibson Mutual Information

We characterize the growth of the Sibson mutual information, of any order that is at least unity, between a random variable and an increasing set of noisy, conditionally independent observations of the random variable. The Sibson mutual information increases to an order-dependent limit exponentially fast, with an exponent that is order-independent. The result is contrasted with composition theorems in differential privacy.
Steganography protocols for quantum channels

We study several versions of a quantum steganography problem in which two legitimate parties attempt to conceal a cypher in a quantum cover transmitted over a quantum channel without arising suspicion from a warden who intercepts the cover. In all our models, we assume that the warden has an inaccurate knowledge of the quantum channel and we formulate several variations of the steganography problem depending on the tasks used as the cover and the cypher task. In particular, when the cover task is classical communication, we show that the cypher task can be classical communication or entanglement sharing; when the cover task is entanglement sharing and the main channel is noiseless, we show that the cypher task can be randomness sharing; when the cover task is quantum communication and the main channel is noiseless, we show that the cypher task can be classical communication. In the latter case, our results improve earlier ones by relaxing the need for a shared key between the transmitter and the receiver and hold under milder assumptions on the cover quantum communication code.
VDetor: An Effective and Efficient Neural Network for Vehicle Detection in Aerial Image

Vehicle detection in aerial image is the foundation of some applications, such as the traffic management, parking lot utilization, etc. Recently, universal object detection methods based on the convolutional neural networks have achieved state-of-the-art performances, this is mainly because CNNs can extract more effective features compared with the handcrafted features in early mainstream methods. Extracting effective feature is crucial for vehicle detection in aerial image where the vehicles are small and the background is rather complicated. As a result, these methods based on CNNs have been used to detect the vehicles in aerial image. However, the performance may be poor when directly performing these universal methods. Firstly, these existing methods mostly detect the vehicles with horizontal bounding boxes. But these horizontal boxes do not match the vehicles in aerial image with arbitrary orientations and multiply aspect ratios. As a result, this kind of box would harm the detection accuracy directly. In addition, these methods are mostly computationally expensive, so they are not suitable for the platform with limited computational resources, for example the unmanned aerial vehicle. To address these problems above, by introducing the rotated bounding box regression and a lightweight network into SSD, we propose our vehicle detection networks, VDetor. Specifically, we use rotated bounding box rather than horizontal one to match the vehicle well and use a lightweight network, PeleeNet, as the backbone network of SSD to speed up the inference. Experiments on VEDAI dataset illustrate that our model performs better than SSD in terms of the detection accuracy and detection speed.
Design and Development of Spinning Bike Game Based on VR Technology

Virtual reality technology can provide a concurrent and multi-sensory interaction between humans and computers, enhancing the immersion, realism and fun in the process of human-computer interaction. The introduction of virtual reality technology into the design and development of fitness games can change the deficiencies of traditional fitness games such as low dimensionality, boringness, and low substitution. It makes the fitness process interesting and efficient. This article takes a VR spinning bike game designed and developed based on the Unity3D platform as an example to explore the application of VR technology in the fitness game industry.
On reverse converters for arbitrary multi-moduli RNS

In this paper, we propose a new design of reverse converters for residue number systems with arbitrary moduli sets consisting of any number of odd moduli and one even modulus of the type 2k. The new converters are arithmetic-based designs, that may be implemented using only arithmetic components without any read-only memories nor lookup tables. We tackle the problem of large modular reduction imposed by the properties of Chinese Remainder Theorem (CRT) employed in our method by calculating small correction factor in parallel with weighted sum of CRT in a set of constant multipliers followed by one two-operand modulo adder. Synthesis results show delay reduction over existing designs of up to 39.23% with area reductions of up to 28.48%.
Forest Fire Control with Learning from Demonstration and Reinforcement Learning

This paper describes a novel approach to control forest fires in a simulated environment using connectionist reinforcement learning (RL) algorithms. A forest fire simulator is introduced that allows to benchmark several popular model-free RL algorithms that are combined with multilayer perceptrons that serve as a value function approximator. For our experiments, we test in total four different algorithms: Q-Learning, SARSA, Dueling Q-Networks and a novel algorithm called Dueling-SARSA. To enable the algorithms to better cope with the difficulty to contain the forest fires when they start learning, we use demonstration data that is inserted in an experience-replay memory buffer before learning. In the experiments, the performance of these algorithms are compared under different experimental setups ranging from the complexity of the simulated environment to how much demonstration data is initially given. The results show that the demonstration data are necessary to learn very good policies for controlling the forest fires in our simulator and that the novel Dueling-SARSA algorithm performs best. Furthermore, the results indicate that the used on-policy algorithms are better able to use the demonstration data than the off-policy algorithms.
An Automatic Generation of Domain Ontologies Based on an MDA Approach to Support Big Data Analytics

This chapter proposes a new methodology for the automatic generation of domain ontologies to support big data analytics. This method ensures the recommendations of the MDA approach by transforming UML class diagrams to domain ontologies in PSM level through ODM, which is an OMG standard for ontology modeling. In this work, the authors have focused on the model-driven architecture approach as the best solution for representing and generating ontology artifacts in an intuitive way using the UML graphical syntax. The creation of domain ontologies will form the basis for application developers to target business professional context; however, the future of big data will depend on the use of technologies to model ontologies. With that said, this work supports the combination of ontologies and big data approaches as the most efficient way to store, extract, and analyze data. It is shown using the theoretical approach and concrete results obtained after applying the proposed process to an e-learning domain ontology.
An efficient image denoising approach to remove random valued impulse noise by truncating data inside sliding window

Random impulse noise disrupts the homogeneity of the image and deteriorates the other features such as edges, layer depth and sharpness. To alleviate this problem, this paper presents a new algorithm that detects and removes the random valued impulse noise by devising the local statistics inside the sliding window. The corrupt pixel is identified by examining the data under the current window using Gaussian probability distribution function. Meanwhile, the median of the entire data and the mean value of truncated data inside the current window estimates the optimum intensity value at the position where the pixel is disrupted by noise. Simulations show that the proposed method effectively reduces the noise at various level of noise density and yields a better intensity estimate in restored image.
Developing and Validating an Academic Vocabulary List in Russian: A Computational Approach

Abstract To date, attempts at empirically validating a construct of academic vocabulary in the form of a frequency list in languages other than English remain conspicuously absent in peer‐reviewed journals. This study aims to close this gap by using Russian as a case study to develop an academic vocabulary list and prove its viability through a variety of data science methods, including cross‐validation and out‐of‐sample coverage. Our findings support the utility of such a construct in Russian and its potential impact on teaching Russian for academic purposes.
Temperature Prediction Modeling and Control Parameter optimization Based on Data Driven

The aluminum smelting process is characterized with multi-variable, strong coupling and large lag, which leads to low efficiency and high energy consumption. Previous study shows that Extreme learning machine (ELM) can be used to model furnace temperature for its high train speed. However, due to the random initialization of the input weight and the hidden layer threshold in the ELM, the furnace temperature prediction model is unstable in the start of each smelting process. To improve the stability, we introduce the restricted Boltzmann machine to optimize an ELM model. In addition, a furnace temperature segmentation optimization strategy based on whale optimization algorithm is proposed to effectively find the optimal values of the control parameters in each furnace temperature section. The simulation results state that the aluminum melting time can be reduced by about 6.67% after optimization.
Structure and Function Analysis of the Digital Ecosystem of Scientific Journals

With the wide application of digital technology, digital ecosystem is developing rapidly. Actively constructing the new digital ecosystem of scientific journals can lay the foundation for the decision-making of content production, the promotion of influence and the realization of user value. This paper takes digital ecosystem as the theme to conduct literature search in China National Knowledge Infrastructure(CNKI), through the visual quantitative analysis method, explores its research and application in the field of journalism and communication; puts forward the concept of digital ecosystem of scientific journals using the method of concept transplantation from the perspective of natural ecology theory, clarifies its structure and analyzes the function of constructing digital ecosystem of scientific journals. The results showed that: the research on digital ecosystem is in the stage of rapid development, while the proportion in the field of news and media is relatively small, which needs to be improved; the construction of digital ecosystem of scientific journals can effectively promote the coordinated development of periodical industry, realize the sharing of resources, and finally realize the value creation and value-added of the main body of digital ecosystem of scientific journals.
Seven-Point Solar Tracking Control for a Fiber-Optic Daylighting System

Abstract A strategy for precise solar tracking has been developed using feedback signals from seven photosensors in conjunction with the operation of an active daylighting system. The tracking system was composed of a microcontroller, two stepper motors, photosensors, a grooves-in Fresnel lens concentrator, and a glass optical fiber cable. A robust control was implemented using cadmium sulfide (CdS) sensors to track the sun’s path precisely from sunrise to sunset. To avoid the cloud effect, two separate sensors were installed apart from the main tracking sensors. The control system was allowed to track the sun’s position if clouds covered the sky continuously for less than approximately 70 min. To analyze the performance of the solar tracker for daylighting applications, a series of experiments were performed in different weather conditions where the accuracy and effectiveness of the present solar tracking control were confirmed.
Improving a Software Framework from an Assistive Technology Application for iTV

TV is one of the most popular media in people’s homes around the world. Its rapid technological evolution has led to what we today call interactive TV (iTV), a concept that extends the possibilities of its use. Despite iTV’s applications to provide great advances in the interaction with the audience, most of its features are still accessible just through conventional remote controls. This interaction device ends up making the content inaccessible, for example, for people with physical/cognitive limitations. Assistive Technology (AT) is a research area focused on developing applications for this audience. An example of an AT-based solution is IOM (Interface Óculos Mouse), an interaction device that uses the head movements to access the computer. However, to create applications that use devices (such as IOM) in different scenarios, it is necessary to provide tools that assist the development process. Reusable software structures can achieve this goal by encapsulating essential features for using these devices. Design techniques to this type of solution propose the development of a set of applications, under different usage contexts, to evidence the functionalities that should be exposed by the framework. This paper explores the survey of requirements that can compose the IOM’s software framework through IOM4TV development and evaluation. It is an application designed to use the IOM device in the ITV’s context. Thus, the paper initially outlines the research areas’ state-of-art involved in this work. After that, the article focuses on IOM4TV’s development and testing stages. Finally, the paper highlights the requirements perceived in this context of use, which can be applied in the modeling of the proposed software framework.
Perceptual Sensitivity Based Image Structure-Texture Decomposition

Structure-texture decomposition (S-TD) is of significance for many perception-related image processing tasks. In this work, by mimicking the visual perceptual property in the local receptive field of the human visual system (HVS), a novel S-TD method is introduced based on perceptual sensitivity. Considering the perceptual sensitivity of the HVS, three indicators, i.e., the luminance contrast (which measures the change of luminance), the structure anisotropy (which represents the local structure property), and the pattern complexity (which reflects the regularity of the visual content), are firstly defined and measured. And then, according to visual property from the three indicators, image contents are decomposed into five regions, which are the smooth area, the primary edge, the secondary edge, the regular texture, and the irregular texture. Finally, the S-TD method is applied on two perception-related image processing tasks, i.e., image quality assessment and just noticeable difference estimation. And both experimental results verify the effectiveness of the proposed S-TD method.
Attribute-Based Weighted Keyword Search Scheme Supporting Multi-Search Mechanism in Fog Computing

With the popularity of intelligent terminal, fog computing as a new generation of internet technology can integrate the computing power of terminals and improve the effective utilization of social resources. At the same time, more and more data owners store ciphertext in the network. Hence, the advent of searchable encryption (SE) scheme enables searchers to search ciphertext without downloading all of it. However, the existing SE scheme cannot identify the matching degree of keywords and ciphertext. Besides, these schemes can only provide one or two search mechanism. In this paper, we propose a ciphertext-policy attribute-based weighted keyword searchable encryption scheme (ABWKS-MSM) in fog computing in order to solve the matching degree issue and provide multi-search mechanism, so that it becomes more friendly to fog computing applications. Furthermore, the proposed weighted keyword model can reduce the computational and storage cost. Finally, the performance analysis is also given, which proves that our scheme is efficient and feasible in fog computing.
The Fusion of Local and Global Descriptors in Face Recognition Application

In a complex process like face recognition, the type of the features embedded in human face plays a dominant role in the recognition process. The global features describe the whole face image, while the local features are used to describe the local areas in the face. Therefore, combining these features together in the same feature vectors leads to provide attractive features, which have the ability to distinguishing between similar face images. In this paper, we propose a combined local and global face recognition technique. The local features are captured using radon transform descriptor, while the global features are provided using Chebyshev–Fourier moments. Extensive experiments have been performed on four different face databases, namely JAFFE, FERET, YALE, and UMIST, to evaluate the accuracy of the suggested approach under different face variations. Results of these experiments indicate that the presented technique achieved high recognition accuracy in the presence of illumination, pose, and facial expression variations.