Reviewerly/exHarmony huggingface dataset:
Reticular shield detection algorithm using superpixel segmentation

In this paper, we propose a novel reticular shield detection algorithm to restore the images that are obscured by reticular shield such as barbed wires and fences. The whole framework is composed of three stages: stage-1 for accurate superpixel segmentation, stage-2 for deriving the mask of reticular shield and stage-3 for restoring the image by introducing the algorithm. In this paper, to obtain the accurate mask of the shield, a new joint feature CCTP is used to train support vector machine (SVM) classifier and classify all superpixels. Finally, we make comparison of the effects of the proposed algorithm and several other algorithms put forward by other scientists. We introduce two indexes, PSNR and SSIM to evaluate the results and the proposed algorithm is obviously superior to the other two algorithms, which can also be proved by looking into the restoration images.
A Didactic Model with Technology 4.0 for Ubiquitous Learning at the UNIANDES University of Ecuador

This article describes the execution of the didactic innovation project proposed at Uniandes University in the city of Ambato in the Republic of Ecuador. This has as objective: propose a new educational model entirely supported by the called 4.0 technology to achieve ubiquitous learning in the educational institution. Specifically, it deals with the problems related to vocational training, not aimed at achieving competencies for efficient job performance in the future. Also, the conflictive situation is associated with the low level of technological management by teachers as well as total ignorance about technology 4.0. The research was developed among the students, and professors of the University, initially, a theoretical foundation was made related to didactic models, technology 4.0, ubiquitous learning. In the end, a techno-alternative didactic model is proposed, fully supported by 4.0 technology applied through computers, and mobile devices.
Hierarchical Convolutional Features Fusion for Visual Tracking

Abstract Hierarchical convolutional features have different impact on the tracking performance, as the higher convolutional layers encode the semantic information of targets and earlier convolutional layers are more precise to localize targets. In this paper, we propose a novel scheme for hierarchical convolutional features fusion for visual tracking. In the proposed scheme, hierarchical convolutional features are first concatenated to form the cascading feature at the feature level, and then a convolutional layer is added to reduce the feature dimension. Discriminative correlation filter (DCF) is finally utilized to obtain the target location, which is treated as a differentiable layer in the neural network. The experimental results demonstrate that our proposed scheme achieves superior performances on the visual tracking benchmark.
End-user software customization by direct manipulation of tabular data

Customizing software should be as easy as using it. Unfortunately, most customization methods require users to abruptly shift from using a graphical interface to writing scripts in a programming language.
Inter-Frame Differencing in Training Data for Artificial Intelligence : Contour Processing for Inter-Frame Differencing Method

We investigate the frame subtraction method in the research of the security surveillance system for old people by artificial intelligence using deep neural networks. The results in low-illumination images become smaller when the conventional inter-frame differencing method is applied. Therefore, we propose an optimal method of inter-frame differencing in various illuminance environments. The accuracy of inter-frame differencing improves when the proposed method using contour processing is applied.
Breaking Social Media Bubbles for Information Globalization: A Cross-Cultural and Cross-Language User-Centered Sense-Making Approach

With the globalization of data, online social media plays an active role in spreading information and classifying people, and thinking about how to break the solidification of algorithms becomes critical. Current algorithmic research in the social media space often focuses on a single service or language, mainly due to the lack of a way to connect the different bubbles. The panel speakers described their various research activities in which they presented different perspectives on how to break the bubble. This article provides a summary of this interactive panel.
A comparative study of linear discriminant analysis and an artificial neural network performances in breast cancer diagnosis

The use of different artificial intelligence techniques is increasingly widespread around the world. Among the various possibilities it offers, one that has been gained prominence in recent years was the medical field whether in diagnoses or treatments. In this scenario, using the Wisconsin Breast Cancer Database's parameters of a Fine Needle Aspiration, this paper proposes a performance comparison between an artificial neural network and linear discriminant analysis on a breast cancer diagnosis system, since this disease is responsible for thousands of deaths and its effective diagnosis is fundamental in increasing the chances of cure. Therefore, in this article are made analysis of the direct results of these applications and alternatives to improve the performance. Finally, it was obtained a 95.77% rate of correct diagnoses for the linear discriminant analysis and 92.78% for the artificial neural network.
PartID – Individual Objects Tracking in Occupancy Grids Using Particle Identities

Occupancy grid tracking algorithms see the world as made out of cells that can be either free or occupied, and can have speed probability densities attached to each cell. These algorithms estimate the overall state of the environment based on sensor data, but they are not aware of, nor concerned with the identity of individual objects. This paper proposes a new approach for individual objects tracking using the dynamic occupancy grids, which will embed the identity of the objects in the grid state. The particle based dynamic occupancy grid is extended by attaching identity information to each particle, thus achieving individual object tracking at grid level without the need of explicit modeling of the object's shape. The position and dynamics of the world occupied cells are tracked independently of their identity, by the mechanism of the particle based occupancy grid. For achieving individual object tracking, this mechanism is extended with components for assigning and managing the identity of the particles. The designed system was tested on real world sequences, and was able to successfully track obstacles found on the road without making assumptions about their nature, shape or size.
A Novel High-Density Multilayered Audio Steganography Technique in Hybrid Domain

The work in this paper presents a novel data hiding technique with high density of payload data for transmission of sensitive message in a concealed form within a digital audio signal used as cover. To enhance security, the payload message is embedded into discrete Fourier transformed coefficients of time-domain audio samples, to make any potential hacking attempt harder. The proposed technique increases the density of the transmitting message by inserting multiple bits of message data at multiple LSB layers, selected pseudo-randomly within the binary counterpart of each transformed frequency coefficient. Furthermore, a 160-bit message digest is inserted along with the message data to validate the integrity of the extracted message by the receiver. Thorough experimentation has been carried out to evaluate the effectiveness of this method taking different types of cover audio and message as input to various standard performance metrics.
SVM and Formal Concept Analysis for epidemics detection

In this paper, a new classification system is proposed for the epidemic risk management. It consists of a decision support system using an expertise database shared between several hospitals. The goal of this system is to assist medical professionals in epidemic situation detection making pervasive decisions. A semi-supervised classification approach is proposed. It is a hybrid algorithm using Support Vector Machines (SVM) as a supervised classification approach, k-means as a non-supervised approach and Formal Concept Analysis (FCA) as a features selection technique.
Detection of Scenes Features for Path Following on a Local Map of a Mobile Robot Using Neural Networks

This article describes a software package for analyzing images from the camera of a mobile robot using neural networks. A key feature of the proposed solution is the model that generates recommendations for the robot about the direction of the further movement. This model receives raw video frames from the camera and generates a target rotation angle for the robot relative to the central axis of the robot, which must be followed at the next time in the control loop. Such a control loop is executed with a certain frequency in order to achieve the continuous adjustment of the robot’s driving direction to avoid collision with obstacles. A key feature of the proposed method is the high processing speed of video frames and acceptable quality. These characteristics are achieved due to the simple architecture of the neural network, without reinforcement learning, which is commonly used in robotics. The designed module can be used in addition to the existing navigation systems of a mobile robot.
Boundary Loss with Non-Euclidean Distance Constraint for ABUS Mass Segmentation

Mass segmentation plays an important role in the qualitative and quantitative analysis of 3D automated breast ultrasound (ABUS) volumes. However, accurate 3D mass segmentation is a challenging task due to the low signal to noise ratio, large tumor size variations, and the serious class imbalance between foreground and background in ABUS volumes. In this paper, we present a deep learning-based 3D ABUS tumor segmentation method mainly to solve the class imbalance problem. A 3D Residual U-Net is designed to effectively explore feature representations during training. In order to solve the class imbalance problem and make a trade-off between false positive and false negative predictions, a boundary loss with a signed non-Euclidean distance map is introduced. The proposed method is trained and evaluated on 83 ABUS volumes. Experimental results show that the performance of the proposed method is better than existing methods, with a Dice similarity coefficient (DSC) of 0.82 ± 0.08 on the testing set, indicating the effectiveness of the proposed method on 3D ABUS mass segmentation.
Usage of collaborative filtering in sharing platforms

Abstract This article examines the possibility of extending the collaborative filtering method in shared information systems. The need to build recommendations dictates the volume and the intensity of the emergence of more and more new information. In the scientific world, the possibilities of building recommendations are widely explored, and this work presents the authors’ vision of a possible direction of development, which is based on a preliminary iteration with the highlighting of keywords.
Anisotropic diffusion technique to eliminate speckle noise in continuous-wave Doppler ultrasound spectrogram

Paper described a simplified speckled elimination algorithm for the reconstruction of continuous-wave (CW) Doppler ultrasound spectrogram. Standard Nonlinear Anisotropic Diffusion (SNAD) technique fails to eliminate the speckle noise, blurs the interregional area of neighbouring speckle clusters and regulates the heat flow to preserve the systolic and diastolic peaks in the spectrogram. To resolve this problem, author modelled a kernel, conductance and diffusion function to regulate the blurring of speckle clusters and sharpen the high-contrast edges over low-contrast ones. Paper analysed the performance based on the variation of conductance (C), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE) and structural similarity index (SSIM) of each pairs of speckled and despeckled spectrograms. Quantitative analysis reveals that the minimised version of the kernel effectively regulates the flow of heat at edges with a high SNR (8 dB) and PSNR (>20 dB) for a low-frequency range of 20-25 Hz. SSIM (>0.9) shows an efficient structural reconstruction that identifies the lower and higher local SSIM indices as dark and bright pixels on SSIM maps. Hence, the algorithm performs its best at lower peak frequencies in the Doppler spectrograms.
State transfer on two-fold Cayley trees via quantum walks*

Perfect state transfer (PST) has great significance due to its applications in quantum information processing and quantum computation. The main problem we study in this paper is to determine whether the two-fold Cayley tree, an extension of the Cayley tree, admits perfect state transfer between two roots using quantum walks. We show that PST can be achieved by means of the so-called nonrepeating quantum walk [ Phys. Rev. A 89 042332 (2014)] within time steps that are the distance between the two roots; while both the continuous-time quantum walk and the typical discrete-time quantum walk with Grover coin approaches fail. Our results suggest that in some cases the dynamics of a discrete-time quantum walk may be much richer than that of the continuous-time quantum walk.
Implementation of Service Platform For Smart City As A Service

An ecosystem of digital services requires a service platform to facilitate a dynamic service introduction. As a system of systems and a service-oriented system, Smart City has key characteristics of a service ecosystem. However, a service platform has unique characteristics to become a service platform for the implementation of Smart City. On the other hand, an implementation of a service platform for a Smart City must depart from a proven service platform concept conforming to the characteristics of a Smart City Architecture. This paper elaborates an initial process of a service platform implementation with Smart City characteristics. This initial study takes its form as a descriptive study from a systematic literature review employing a Design Research Methodology (DRM) approach. This paper provides an overview of an application of service platforms concept in the digital service ecosystem and service computing systems to fulfill the need of a Smart City Architecture characteristics.
An Intensity Separated Variational Regularization Model for Multichannel Image Enhancement

The channels of the multi-temporal SAR image have strong scattering target distribution in different positions. Focus on this, this paper propose the intensity segregation representation model for the multi-temporal SAR image restoration. This new variational regularization model based on the intensity separation of the multi-temporal SAR image is composed of two sub-models. The first one is a variational regularization model for the intensity component of the image, where the noise is assumed to be multiplicative, and the regularization term is the total variation. A fixed point iterative algorithm is used to solve the Euler-Lagrangian equation of the first sub-model. The second sub-model is the vectorial variational regularization model for the vector component of the image, which is obtained by the assumption that the noise is multiplicative. And the vectorial total variation norm of the vector defined on the unit sphere is obtained. A partial differential equation method is used to get the differential iterative algorithm to solve the Euler-Lagrangian equation of the second sub-model. In this paper, the intensity separation model is applied to the multi-temporal SAR image despeckling. The strong scattering target is well preserved while the good efficient of despeckling is obtained. In summary, this method is proved to highly promote the ability of distinguish different kinds of surface target of the multi-temporal SAR image.
A case study in language-based security: building an I/O library for Wyvern

As the impact of vulnerabilities increases in practice, it is imperative for programming languages to include security as a first-class design consideration. While a number of security-related language features have been proposed to address this need, in many cases, we do not know enough about whether it is practical and useful to build software systems in languages with these features.
Caterpillar Alignment Distance for Rooted Labeled Caterpillars: Distance Based on Alignments Required to Be Caterpillars

A rooted labeled caterpillar (caterpillars, for short) is a rooted labeled tree transformed to a rooted path after removing all the leaves in it. In this paper, we discuss the alignment distance between two caterpillars, which is formulated as the minimum cost of possible alignments as supertrees of them. First, we point out that, whereas the alignment between two trees is always a tree, the alignment between two caterpillars is not always a caterpillar. Then, we formulate a caterpillar alignment distance such that the alignment is required to be a caterpillar. Also we formulate a caterpillar less-constrained mapping and show that the caterpillar alignment distance coincides with the minimum cost of possible caterpillar less-constrained mappings. Furthermore, we design the algorithm to compute the caterpillar alignment distance between two caterpillars in \(O(h^2\lambda ^3)\) time under the general cost function and in \(O(h^2\lambda )\) time under the unit cost function, where h is the maximum height and \(\lambda \) is the maximum number of leaves in caterpillars. Finally, we give experimental results of computing the caterpillar alignment distance.
A Study on Avoidance Behavior on Bicycle Trips Detection Using Multiple Features for Improvement Road Management

This paper proposes a method for detecting avoidance behavior on bicycle trips in videos using multiple features. Our previous method detected the avoidance behavior on bicycle trips by using single feature based on rider's body parts. Also, the collaborative use of multiple features is effective for video analysis tasks. Thus, the proposed method introduces two new features for detection performance improvement. The introduced features are based on optical flow and Convolutional Neural Network (CNN), which are widely used in video analysis. Consequently, more accurate detection is realized. Experimental results show the effectiveness of the proposed method.
Online Signature Verification Based on Multi-mode Matching

In order to improve the accuracy of online signature verification, an online signature verification technology based on multi-mode matching is proposed. Combined with dynamic time warping (DTW) and string editing distance (SED), the average value, maximum value, minimum value and median value of the distance between the signatures to be tested and the signature of the template were calculated respectively. After normalization, it was combined into eight-dimensional feature vectors, and the experiment was carried out using SVM classifier. The experiment eventually achieved 1.25% FAR and FRR on SVC2004 Task2.
Research on Disease Identification in Chinese Domain Based on Word Embedding Technology

Machine learning technology has been widely used in the field of auxiliary diagnosis. However, existing models ignore the association between words. Therefore, performance improvement is limited when the scale of data is expended. To solve this problem, we use the cw2vec model to process the Chinese medical record text, combine it with the fusion model of Bidirectional Long Short-Term Memory and conditional random field, and propose a new model. This model makes full use of the word embedding technology and improves the existing neural network by combining the semantic connotation of Chinese. The experimental results show that the improved model has higher disease identification rate than the existing models under the same data scale.
A New Rabin-Type Cryptosystem with Modulus $$p^{2}q$$

In 1979, Rabin introduced a variation of RSA using the encryption exponent 2, which has become popular because of its speed. Its drawback is decryption to four possible messages which has led to various ideas to identify the correct plaintext. This paper provides a new Rabin-type cryptosystem based on a modulus of the form $$p^{2}q$$ . Along with a theoretical proof that the decryption is correct, we provide a complete example. To demonstrate its efficiency, we compare runtime of our algorithms with those of two others with similar aims. We also conjecture that our scheme is secure against chosen ciphertext attacks because of our inclusion of Simplified Optimal Asymmetric Encryption Padding of messages.
Devising a Cross-Domain Model to Detect Fake Review Comments

The online reviews not only have huge impact on consumer shopping behavior but also online stores' marketing strategy. Positive reviews will have positive influence for consumer's buying decision. Therefore, some sellers want to boost their sales volume. They will hire spammers to write undeserving positive reviews to promote their products. Currently, some of the researches related to detection of fake reviews based on the text feature, the model will reach to high accuracy. However, the same model test on the other dataset the accuracy decrease sharply. Relevant researches had gradually explored the identification of fake reviews across different domains, whether the model built using comprehensive methods such as text features or neural networks, encountering the decreasing of accuracy. On the other hand, the method didn't explain why the model can be applied to cross-domain predictions. In our research, we using the fake reviews and truthful reviews from Ott et al. (2011) and Li, Ott, Cardie, and Hovy (2014) in the three domain (hotel, restaurant, doctor). The cross-domain detect model based on Stimuli Organism Response (S-O-R) combine LIWC (Linguistic Inquiry and Word Count), add word2vec quantization feature, overcoming the decreasing accuracy situation. According to the research result, in the method one SOR calculation of feature weight of reviews, the DNN classification algorithm accuracy is 63.6%. In the method two, calculation of frequent features of word vectors, the random forest classification algorithm accuracy is 73.75%.
Image Captioning in Vietnamese Language Based on Deep Learning Network

Image captioning is an underlying and crucial problem in artificial intelligence. This is challenging since it requires advanced research in computer vision to detect objects and the correlation of these objects in the image, and text-mining to convert these relationships into words. Although some based deep-learning and machine translation approaches have been achieved state-of-art results in English recently, it is missing an approach to generate the caption in Vietnamese, which is a local language in Vietnam with complex grammar and variable meaning in simple words. Moreover, machine translation is affected negatively by a significant issue called unknown words, which is caused by both large vocabulary size and unbalanced dataset. In this paper, we propose a new approach to generate the Vietnamese caption of the image and also a simple and effective solution to tackle unknown words problem of machine translation. In general, the results of these methods achieved in the self-build testing database are promising.