Reviewerly/exHarmony huggingface dataset:
Comparative Survey of Signal Processing and Artificial Intelligence Based Channel Equalization Techniques and Technologies

This paper presents an inspection and review of wired and wireless channel equalization techniques and their existing hardware implementations in terms of features, similarities, and differences.The authors begin with the theory behind channel equalization followed by techniques, and the technological realizations for achieving the proper filter in response to variations of the channel.Included in both the techniques and realizations are the rebirth of the use of artificial intelligence as a self-learning filter for the weights to use by the filtering structure of channel equalizers.These equalizers were compared, contrasted, and their key differentiation was identified.It was found that gaps such as complexity and convergence time are potential areas for extending the performance and limits of existing channel equalizers.
Correction to Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering [Nov 13 7718-7727]

This paper corrects errors in the calculation of the normalized maximum likelihood (NML) code length for a Gaussian mixture model (GMM). It shows that the NML code length calculated in “efficient computation of the NML codes for the GMMs with its applications to clustering” is an upper bound on the NML code length strictly calculated for the GMM. In addition, we correct the NML code length for generalized logistic distribution.
Computer-based Approach to Detect Wrinkles and Suggest Facial Fillers

Modern medical practice has embraced facial filler injections as part of the innumerable cosmetic procedures that characterize the current age of medicine.This study proposed a novel methodological framework.The Inception model is the core of the framework.By carefully detecting the classification of wrinkles, the model can be built for different applications to aid in the detection of wrinkles that can objectively help in deciding if the forehead area needs to have filler injections.The model achieved an accuracy of 85.3%.To build the Inception model, a database has been prepared containing face forehead images, including both wrinkled and non-wrinkled face foreheads.The face image pre-processing is the first step of the proposed framework, which is important for reliable feature extraction.First, in order to detect the face and facial landmarks in the image, a Multi-task Cascaded Convolutional Networks model has been used.Before feeding the images into the deep learning Inception model for classifying whether the face foreheads have wrinkles or no wrinkles, an image cropping process is required.Given the bounding box and the facial landmarks, face foreheads can be cropped accurately.The last step of the proposed methodology is to retrain an Inception model for the new categories (Wrinkles, No Wrinkles) to predict whether a face forehead has wrinkles or not.
Prediction Using LSTM Networks

Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. Long-Short Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction.
Machine Learning Based CloudBot Detection Using Multi-Layer Traffic Statistics

With the rapid development of e-commerce services and online transactions, an increasing number of advanced web robots are utilized by speculators and hackers in underground economy to perform click fraud, register fake accounts and commit other kinds of frauds, seriously harming the profit of businesses and the fairness of online activities. There is solid evidence that the vast majority of such malicious bot traffic comes from data centers. The malicious bot deployed on the hosts of data centers is referred to as a CloudBot. How to detect and block CloudBots effectively has become an urgent problem in practice, while the research on it can be seldom seen in public. To this end, we propose a traffic-based quasi-real-time method for CloudBot detection using machine learning, which exploits a new sample partitioning approach, as well as innovative multi-layer features that reveal the essential difference between CloudBots and human traffic. Our method achieves 93.4% precision in the experiment and performs well on the real-world dataset, which proves to be effective to detect unknown CloudBots and combat the concept drift caused by varying time. Besides, the approach is also privacy-preserving without using any specific application layer information. We believe our work can benefit network economy security and fairness in practice.
Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification

Nowadays, many applications that use large data have been developed due to the existence of the Internet of Things. These applications are translated into different languages and require automated text classification (ATC). The ATC process depends on the content of one or more predefined classes. However, this process is problematic for the Arabic translation of the data. This study aims to solve this issue by investigating the performances of three classification algorithms, namely, k-nearest neighbor (KNN), decision tree (DT), and na&amp;iuml;ve Bayes (NB) classifiers, on Saudi Press Agency datasets. Results showed that the NB algorithm outperformed DT and KNN algorithms in terms of precision, recall, and F1. In future works, a new algorithm that can improve the handling of the ATC problem will be developed.
Condensed Convolution Neural Network by Attention over Self-attention for Stance Detection in Twitter

In the era of Web 2.0, people have become accustomed to expressing their attitudes and exchanging opinions on social media sites such as Twitter. It is critical for security and business related applications to make sense of public opinions implied in users' texts. Stance detection aims to classify the stances users hold towards certain targets as FAVOR, AGAINST or NONE. In the literature, many efforts have been paid on neural network based stance detection to avoid hand-crafted features. As a widely used neural network structure, convolutional neural network (CNN) can mine and combine various local textual features for classifying stances with high training efficiency. However, global textual information is usually neglected in the convolution process. Besides, stance clues are often mixed with less informative words in noisy tweets, and it is hard for CNN to resolve and leverage long-distance semantic dependencies between words effectively. To address these issues, in this paper, we propose CCNN-ASA, the Condensed CNN by Attention over Self-Attention, to detect the stances of tweets. We first introduce self-attention into CNN to adaptively enhance word embeddings with global textual information. To make stance clues close to each other and thus more salient, we further introduce attention-based condensation module which identifies stance-indicative words to condense tweets. Experiments on a benchmark dataset show that CCNN-ASA outperforms state-of-the-art methods in stance detection of tweets.
An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information may be highly beneficial for segmentation performance improvement. To this end, this paper proposes an iterative multi-path fully convolutional network (IMFCN) to effectively leverage spatial context for automatic cardiac segmentation in cine MR images.To effectively leverage spatial context information, the proposed IMFCN explicitly models the interslice spatial correlations using a multi-path late fusion strategy. First, the contextual inputs including both the adjacent slices and the already predicted mask of the above adjacent slice are processed by independent feature-extraction paths. Then, an atrous spatial pyramid pooling (ASPP) module is employed at the feature fusion process to combine the extracted high-level contextual features in a more effective way. Finally, deep supervision (DS) and batch-wise class re-weighting mechanism are utilized to enhance the training of the proposed network.The proposed IMFCN was evaluated and analyzed on the MICCAI 2017 automatic cardiac diagnosis challenge (ACDC) dataset. On the held-out training dataset reserved for testing, our method effectively improved its counterparts that without spatial context and that with spatial context but using an early fusion strategy. On the 50 subjects test dataset, our method achieved Dice similarity coefficient of 0.935, 0.920, and 0.905, and Hausdorff distance of 7.66, 12.10, and 8.80 mm for LV, RV, and MYO, respectively, which are comparable or even better than the state-of-the-art methods of ACDC Challenge. In addition, to explore the applicability to other datasets, the proposed IMFCN was retrained on the Sunnybrook dataset for LV segmentation and also produced comparable performance to the state-of-the-art methods.We have presented an automatic end-to-end fully convolutional architecture for accurate cardiac segmentation. The proposed method provides an effective way to leverage spatial context in a two-dimensional manner and results in precise and consistent segmentation results.
AN ANALYSIS OF GRAMMATICAL ERRORS IN STUDENTS’ WRITING DESCRIPTIVE TEXT

Writing is considered as the most complicated skill to be mastered required not only great vocabularies but also grammar, as Harmer (2004:31) cited in (Wati &amp; Nursyaebah, 2017) states that, when writing, learners often have more time to think than they do in spoken activities. Therefore, they can stimulate their brain,even consult dictionaries, grammar books or other reference material to help them . He believes that grammar is really important in writing that is why he suggested to consult grammar books to make the writing grammatically correct. Therefore, the basic objective of this study is to analyze students’ grammatical errors in writing especially in descriptive text. In addition, (Wati &amp; Nursyaebah, 2017) there are six classifications of grammatical error; those are verb agreement (Leech, 1985), capitalization (Oshima, 1988) usage, sentence pattern (Ross, 1975) pronoun and spelling (Brooks, 1964). The data were analyzed based on the error analysis’s of students in writing. In the research finding it showed that students mostly did error in verb agreement 41% that caused by using incorrect fromsimple present, followed by pronoun 18%, usage 15%, sentence pattern12%, spelling 9%, and capitalization error 5%. From the result finding, it is necessary or students learn more about English grammar and it is suggested for teacher to re-teaching about verb agreement and pronoun as the most failure which students mostly did in their writing.
Improve of contrast-distorted image quality assessment based on convolutional neural networks

&lt;span lang="EN-US"&gt;Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI.&lt;/span&gt;
Modified ACS Centroid Memory for Data Clustering

Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems.The key component that governs the search process in this algorithm is the management of its memory model.In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results.The algorithm's memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration.Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems.However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant's movements in the pheromone matrix.The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable.The label of the current centroid value changes from one iteration to another because of changes in centroid label.Thus the pheromone values are lost because they are associated with the label (position) of the centroid.ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run.This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution.This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid.Centroids will be identified based on the adaptive instance route.A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.
Gender Prediction from Social Media Comments with Artificial Intelligence

In the 21st century, which can be termed as artificial age of intelligence, machine learning techniques that can become widespread and improve themselves can be given more quality services to humanity in many fields. As a result of these developments, nowadays many companies deliver their products and services to their customers via social media accounts. But not every customer is interested in all product or service. Each customer's area of interest is different. Gender is one of the main reasons for this difference. If the gender of a social media user is determined correctly, the amount of sales may be increased by offering the appropriate products or services. The main aim of our study is an estimation of genders of the commenters thanks to machine learning techniques by analyzing the comments of companies posting on Facebook. As a result of the study the genders of the commenters were labelled according to the names by collecting the comments from Facebook. The data set is divided into training and test data as 70-30%. As a result of the study, it was seen that machine learning methods predicted with similar accuracy rates, while the highest accuracy rate (74.13%) was obtained by logistic regression method.
Boosting Local Shape Matching for Dense 3D Face Correspondence

Dense 3D face correspondence is a fundamental and challenging issue in the literature of 3D face analysis. Correspondence between two 3D faces can be viewed as a non-rigid registration problem that one deforms into the other, which is commonly guided by a few facial landmarks in many existing works. However, the current works seldom consider the problem of incoherent deformation caused by landmarks. In this paper, we explicitly formulate the deformation as locally rigid motions guided by some seed points, and the formulated deformation satisfies coherent local motions everywhere on a face. The seed points are initialized by a few landmarks, and are then augmented to boost shape matching between the template and the target face step by step, to finally achieve dense correspondence. In each step, we employ a hierarchical scheme for local shape registration, together with a Gaussian reweighting strategy for accurate matching of local features around the seed points. In our experiments, we evaluate the proposed method extensively on several datasets, including two publicly available ones: FRGC v2.0 and BU-3DFE. The experimental results demonstrate that our method can achieve accurate feature correspondence, coherent local shape motion, and compact data representation. These merits actually settle some important issues for practical applications, such as expressions, noise, and partial data.
Ship Detection: An Improved YOLOv3 Method

YOLOv3 is the state of art detector, which performs an excellent balance in detection speed and accuracy. In this paper, an improved YOLOv3 model named YOLOv3-ship is proposed for the ship detection. The main contributions to the YOLOv3-ship consists of dimension Clusters, network Improvement and embedding of the Squeeze-and-Excitation(SE) module. The experiments results show that the detection accuracy has been significantly improved by the YOLOv3-ship.
An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams

Outlier detection in data streams is considered a significant task in data mining that targets the discovery of elements in an unprecedented data arrival rate. The fast arrival of data demands fast computation within the shortest period, and with minimal memory usage. Detecting distance-based outliers in such a scenario are more complicated. Existing techniques such as the two best-known methods - Micro-Cluster Outlier Detection (MCOD) and Thresh_LEAP have presented some solutions to these challenges. However, the combination of the strength of both techniques can be a lot more improvement to the individual methods proposed. Therefore, in this paper, we propose a method called Micro-Cluster with Minimal Probing (MCMP), which is a hybrid approach of the combination of the strength of MCOD and Thresh_LEAP. We offer a new distance-based outlier detection technique to minimize the computational cost in detecting distance-based outliers effectively. The proposed MCMP technique is comprised of two approaches. Firstly, we adopt micro-clusters to mitigate the range query search. Then, to deal with the objects outside the micro-clusters, we propose the concept of differentiating between strong and trivial inliers. The proposed method improves the computational speed and memory consumption, while simultaneously maintaining the outlier detection accuracy. Our experiments are conducted on both real-world and synthetic data sets. We varied the window size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(w)$ </tex-math></inline-formula> , neighbor count threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k)$ </tex-math></inline-formula> and distance threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(R)$ </tex-math></inline-formula> , and observed that our method outperforms the state-of-the-art methods in both CPU time and memory consumption in the majority of the datasets.
Generic Isolated Cell Image Generator

Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of the biggest challenges of such systems, especially those using state-of-the-art deep learning techniques, is that they usually require a large amount of training data to be accurate. However, in the medical field, the confidentiality of the data and the need for medical expertise to label them significantly reduce the amount of training data available. A common practice to overcome this problem is to apply data set augmentation techniques to artificially increase the size of the training data set. Classical data set augmentation methods such as geometrical or color transformations are efficient but still produce a limited amount of new data. Hence, there has been interest in data set augmentation methods using generative models able to synthesize a wider variety of new data. VitaDX is actually developing an automated bladder cancer screening system based on the analysis of cell images contained in urinary cytology digital slides. Currently, the number of available labeled cell images is limited and therefore exploitation of the full potential of deep learning techniques is not possible. In an attempt to increase the number of labeled cell images, a new generic generator for 2D cell images has been developed and is described in this article. This framework combines previous works on cell image generation and a recent style transfer method referred to as doodle-style transfer in this article. To the best of our knowledge, we are the first to use a doodle-style transfer method for synthetic cell image generation. This framework is quite modular and could be applied to other cell image generation problems. A statistical evaluation has shown that features of real and synthetic cell images followed roughly the same distribution. Finally, the realism of the synthetic cell images has been assessed through a visual evaluation performed with the help of medical experts. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Crime Hotspot Detection With Clustering Algorithm Using Data Mining

Data mining is a technology accustomed solve model crime issues. Crimes are a social burden or nuisance and worth lots to our public in various ways. This can be approached by using clustering algorithm. K-means clustering is one such method is that advancement to support in the procedure of detecting crime patterns[1]. The dimension of this calculation is better-quality with ordinary two-level bunch techniques like Affinity Propagation (AP) and RBF system and Affinity Propagation (AP) utilizing abnormal state standpoint and RBF network[8]. Our system proposes to extract data from crime data record, on which we intend to perform clustering. Data is obtained and the data are clustered and by using data live streaming the data are are streamed according to the sources. The final end product could thus be a project where future predictions made by primary crime data sets, and the output is in order to be simple to understand to the user.