Reviewerly/exHarmony huggingface dataset:
Optimal Image Watermark Technique Using Singular Value Decomposition with PCA

Image watermarking is very important phenomenon in modern society where intellectual property right of information is necessary. Considering this impending problem, there are many image watermarking methods exist in the literature each of having some key advantages and disadvantages. After summarising state-of-the-art literature survey, an optimum digital watermark technique using singular value decomposition with principle component analysis (PCA) is proposed and verified. Basically, the host image is compressed using PCA which reduces multi-dimensional data to effective low-dimensional information. In this scheme, the watermark is embedded using the discrete wavelet transformation-singular value decomposition approach. Simulation results show that the proposed method improves the system performance compared with the existing method in terms of the watermark embedding, and extraction time. Therefore, this work is valuable for image watermarking in modern life such as tracing copyright infringements and banknote authentication.
HDMFH: Hypergraph Based Discrete Matrix Factorization Hashing for Multimodal Retrieval

In recent years, hashing based cross-modal retrieval methods have attracted considerable attention for the high retrieval efficiency and low storage cost. However, most of the existing methods neglect the high-order relationship among data samples. In addition, most of them can only deal with two modalities, e.g., image and text, without discussing the scenario of multiple modalities. To address these issues, in this paper, we propose a novel cross-modal hashing method, named Hypergraph Based Discrete Matrix Factorization Hashing (HDMFH), for multimodal retrieval. Different from most previous approaches, our method based on hypergraph regularization and matrix factorization can handle the cross-modal retrieval of more than two modalities, which is known as multimodal retrieval. Extensive experiments demonstrate that HDMFH outperforms the state-of-the-art cross-modal hashing methods.
A new way of finding better neighbors in recommendation systems based on collaborative filtering

One of the biggest problems of the internet is information overload. A way to handle this is Collaborative Filtering. However, it can present problems as they work with large rating matrices, and they are always really sparse. In this paper, we purpose a model that finds the closest neighbors efficiently incorporating dimensionality reduction, using Truncated Singular Value Decomposition which helps with sparse data and avoids noise caused by lack of ratings, then using clustering as we have a dense reduced matrix, and finally applying the correct similarity metric to improve predictions. To evaluate the prediction quality we use the mean absolute error. The experiments are executed with MovieLens 1M Open Data Set. And to explain the model we use a running example, named datatoy.
A New Sentiment Analysis based Application for Analyzing Reviews of Web Series and Movies of Different Genres

This research paper proposes an application of sentiment analysis that works on the principle of machine learning. The proposed application provides a comparative analysis of web series and movies of different genres of a particular time period on the basis of sentiments of the viewers. Data is fetched from twitter through API keys and twitter access tokens. The movies and web series from the year 2017 to 2019 of four different genres were taken and sentiment analysis was performed on each web series and movie, which gives result in the form of positive reviews and negative reviews. The famous hashtag for each movie and web series are determined. The total number of tweet counts is 3000. A Table of each genre was formed that contained the name of movie and web series, percentage of positive sentiments of corresponding web series or movie and percentage of negative sentiments of corresponding movie or web series. The graphical representation of each genre was done to analyze the results graphically. The combined analysis was performed after calculating the average percentage reviews of a positive and negative sentiment of all the movies and web series of each genre. The graphical representation of the combined analysis is done to analyze the final results. Through the proposed application results were analyzed concluding that whether movies or web series of a particular genre in the year 2017-19 were more liked by the viewers.
Combination of neural network and advanced encryption and decryption technique is used for digital image watermarking

The computer system represents data in various format viz (i) Audio, (ii) Video, (iii) Text, (iv) Message, and (v) Image format. There are many ways through which, data can be easily transferred. The process of transferring digital bank cheque image is done with the help of cheque truncation system . It transfers cheque from home branch to clearing bank branch for faster clearance of customer cheque. This helps the banking system to keep transparency of transaction. During the flow of digital bank cheque image, there may be possibility of various attacks like, Cropping, JPEG compression, Median filtering, Gaussian Blur noise, Rotation, Salt & Pepper noise, etc. This arise the issues of copyright protection and security for digital bank cheque image. In this research work, The Combination of Digital Image Watermarking Using Neural Network and Advanced Encryption and Decryption Technique is Used for Providing Copyright Protection & Security Technique to Digital Bank Cheque Image.
Researches on Software Requirements Elicitation Approach of the Aviation Electronics Systems based on Multi-ontology

Because of the increasing complexity of software systems as well as the more various types of R&D personnel of the workers, software development is going to a significant trend of knowledge intensification. In the field of knowledge engineering, an ontology is an explicit specification of a conceptualization. In this paper, the ontology method is used to construct the requirement knowledge framework and the various of requirement knowledge is expressed as a clear, complete and consistent hierarchical ontology concept and association, which is more conducive to knowledge sharing and reuse as well as reflects multiple viewpoints of stakeholders. Furthermore, the relevant content of the decomposition of the generalization ontology is added in the framework, which is divided into the structure ontology and action ontology. It makes up for the deficiency of undifferentiated knowledge composition representation of the generalization ontology. Besides, the concept of software requirement error pattern is proposed and integrated into the multi-ontology framework of requirement knowledge in a consistent form. Based on the framework, the domain requirement model and the application requirement model can be built. It can be adopted as the basis of the requirement elicitation activities to avoid errors and improve the quality and reliability of software products.
Robust Visual Tracking with Context-Based Active Occlusion Recognition

Occlusion is a great challenge for target model update in visual tracking. The target template may be corrupted by non-object information in the process of online learning due to occlusion. In this paper, we propose a context-based active occlusion recognition framework that can be integrated with various tracking approaches. The basic idea is to recognize the occlusion patches by actively tracking context patches and distinguish them from target based on a target model that integrates the information of both target and context. The framework consists of a context patch tracker, an occlusion patch recognizer and a local target template updater. The context patch tracker locates the context patches. The occlusion patch recognizer identifies the context patches occluding target. The local target template updater updates only non-occluded regions of the target template. In this way, the target template not only learns current target features but also avoids context corruption caused by occlusion. The experimental results show that our framework can improve the tracking performance in cases of heavy occlusion.
Spherical Video Coding with Geometry and Region Adaptive Transform Domain Temporal Prediction

Many virtual and augmented reality applications depend critically on efficient compression of spherical videos. Current approaches apply a projection geometry to map a spherical video onto the plane(s), wherein a standard codec can be used for compression. Video coders employ simple pixel copying from reference frames for inter-prediction, which ignores underlying spatial correlations, and is hence suboptimal. A novel paradigm of transform domain temporal prediction (TDTP) was developed previously in our lab to effectively overcome this suboptimality of standard video coding. This paper is motivated by the observation that projected spherical videos exhibit significantly more statistical variation due to i) the choice of projection geometry and ii) position of the block on the sphere, which reflect variations in sampling density and various statistical features. To account for such variations, we propose geometry and region adaptive TDTP that is tailored to spherical videos. For a given geometry, the sphere is divided into regions, according to expected signal statistics, and prediction filters are designed for each region. Experimental results show significant performance gains as evidence for the efficacy of TDTP in spherical video coding.
Adaptive Robust Kalman Filter for Vision-based Pose Estimation of Industrial Robots

In this paper, an adaptive robust Kalman filter (ARKF) for precise and robust pose detection of industrial robots is presented. The proposed ARKF exploits the advantages of adaptive estimation method for states noise covariance (Q), least square identification for measurement noise covariance (R) and a robust mechanism for state variables error covariance (P). In simulation on PUMA 560, the comparison between the proposed ARKF and other well-known version of Kalman filter such as adaptive Kalman filter (AKF) and standard Kalman filter (SKF) shows the superiority of the ARKF in terms of root mean square (RMS) and Variance (Var) of filtered errors. The ARKF outperforms above-mentioned methods both in smooth filtering and in signal tracking. Simulation results reveal the superior tracking performance of the ARKF when the robot is subjected to the measurement noises and uncertainties.
Learning-based CT Perfusion Image Denoising with Only Noisy Training Data

Computed tomography perfusion (CTP) imaging measures the hemodynamics of the brain parenchyma, which provides important information for acute ischemic stroke diagnosis. CTP images are very noisy due to the low dose in the sequential scans. Conventional denoising methods significantly reduce the spatial resolution, which leads to low sensitivity to small infarct cores. Despite of the success of deep neural networks in medical image denoising and reconstruction, they require high quality reference images for training, which are not available in CTP. In this work, we proposed a network-based method for CTP denoising which only required noisy data. Rather than mapping a noisy image to its clean version, the network was learned by mapping the noisy image to its estimation with independent noise. The noise-to-noise training was equivalent to learning noise-to-clean mapping with independent noises. The method was trained and tested on ISLES 2018 dataset and demonstrated improved spatial resolution, noise level and contrast to noise ratio compared to existing denoising methods.
Multi-prong Framework Toward Quality-Assured AI Decision Making

Can we trust decisions made by artificial intelligence (AI)? This is a question that is important not just for computer scientists, but also an increasingly broad spectrum of non-specialist users of AI. AI is already used for decision support in healthcare (e.g. diagnosis), business and commerce (e.g. hiring, loan applications), and so on. There is therefore an urgent need to address the question of quality assurance of AI decision making. Contemporary AI focuses on a connectionist approach with an emphasis on artificial neural networks (NN) that get deeper and deeper for better quantitative regression or classification performance. However, while NN and more generally machine learning (ML) algorithms perform well statistically, results are individually unreliable. When ML fails, it can fail miserably with unclear failure models due to a lack of interpretability of many of the results generated by contemporary AI. This paper addresses this critical and timely issue by proposing a multi-prong framework that brings together elements of good old AI and ensemble decision making for enhanced interpretability, and best practices in software quality assurance, toward ensuring a high standard of quality in AI decision making. The framework is illustrated with use case scenarios drawn from multiple domains to demonstrate its broad applicability.
Speech Recognition in Mongolian Language using a Neural Network with pre-processing Technique

In this paper, we developed a neural network model, which is capable of recognizing a limited number of words in Mongolian language. We have chosen four words in Mongolian language. These words were chosen for further designing and creating a special device with an audio interface. In this experiment, we used audio recordings recorded in a computer using a microphone in a normal audience with minimal background noise. The database of audio recordings used to train the neural network consists of speeches of 11 people (7 men and 4 women). One of them is around 20-30 years old, three people are 60-70 and the rest are 30-40. The work uses a regular personal computer with an Intel Core i5 processor - the 3rd generation and with 8GB DDR IV RAM.
A Rule Based Stemmer for Sinhala Language

Stemming, as its word implies it converts the original word into its root/base format which is called as stem. Stemming process plays a prominent role in natural language processing (NLP) because it makes applications more efficient and effective. Though stemming is such an important task, it is hard to find a stemming method for Sinhalese language which is official language of Sri Lanka. There are common language analyzers which cannot be use for stemming since they are highly language dependent. In this paper, we present a rule-based stemming method by using suffix and prefix rules in Sinhalese language.
An Evaluation of Contrast Enhancement of Brain MR Images Using Morphological Filters

Tumor is the uncontrollable growth of abnormal cells in the brain which can be screened using magnetic resonance imaging (MRI). But, MRI is prone to poor contrast and noise during acquisition. This might affect the visibility of the tumor in the image which makes contrast enhancement an essential part of MR image analysis for tumor detection. In this method, a disk-shaped flat structuring element is applied with morphological operators consisting of bottom-hat, dilation and erosion for the purpose of noise controlled enhancement of MRI tumors. The outcomes of the proposed method are validated by image fidelity assessment parameters like: contrast improvement index (CII) and peak signal-to-noise ratio (PSNR).
Tensor-Based Emotional Category Classification via Visual Attention-Based Heterogeneous CNN Feature Fusion

The paper proposes a method of visual attention-based emotion classification through eye gaze analysis. Concretely, tensor-based emotional category classification via visual attention-based heterogeneous convolutional neural network (CNN) feature fusion is proposed. Based on the relationship between human emotions and changes in visual attention with time, the proposed method performs new gaze-based image representation that is suitable for reflecting the characteristics of the changes in visual attention with time. Furthermore, since emotions evoked in humans are closely related to objects in images, our method uses a CNN model to obtain CNN features that can represent their characteristics. For improving the representation ability to the emotional categories, we extract multiple CNN features from our novel gaze-based image representation and enable their fusion by constructing a novel tensor consisting of these CNN features. Thus, this tensor construction realizes the visual attention-based heterogeneous CNN feature fusion. This is the main contribution of this paper. Finally, by applying logistic tensor regression with general tensor discriminant analysis to the newly constructed tensor, the emotional category classification becomes feasible. Since experimental results show that the proposed method enables the emotional category classification with the F1-measure of approximately 0.6, and about 10% improvement can be realized compared to comparative methods including state-of-the-art methods, the effectiveness of the proposed method is verified.
Bispectrum-Based Detection Homogeneous Areas in the Digital Images

A novel bispectrum-based strategy applied to digital image processing is suggested for solving the problems of detection the homogeneous areas in digital images. Novel classification features evaluated in the form of bispectrum magnitude estimates and computed for the certain image areas are suggested and studied. Decision about homogeneous area has been made by comparative analysis of the bimagnitude peak values. Small biamplitude values indicate homogeneous area and large biamplitude values indicate non- homogeneous area contained in the image. Results of computer simulations performed for grayscale images are represented and discussed. Performance of detection the homogeneous areas has been examined both for various noiseless and contaminated by noise test images. The biamplitude computations have been performed under influence of four various noise types. It has been examined the following noises: Gaussian, Poisson, salt & pepper, and speckle. According to the experimental results, proposed technique provides high accuracy for detecting homogeneous areas in the noiseless images. But in the presence of noise, the proposed technique is less effective.
Salient Pattern Detection via Integrating Local Contrast and Minimum Spanning tree

This paper presents an approach to distinguish a salient object from an image. In this paper, we have introduced a robust method to detect the salient object from an image. We have utilized local contrast and edge color dissimilarity features to identify the salient regions. Firstly, Local contrast is used which is highly capable to extract the foreground salient object from background based on local contrast with its neighboring regions while foreground and background are more similar. Secondly, edge color dissimilarity measure helps to locate the salient region from other regions of an image having more cluttered background images. However, combination of local contrast and edge color dissimilarity features works better in salient region detection from an image. Experimental results demonstrate that proposed approach presents a good result which has been tested on two datasets ECSSD and SOD.
Bilateral Recurrent Network for Single Image Deraining

Single image deraining has been widely studied in recent years. Motivated by residual learning, most deep learning based deraining approaches devote research attention to extracting rain streaks, usually yielding visual artifacts in final deraining images. To address this issue, we in this paper propose bilateral recurrent network (BRN) to simultaneously exploit rain streak layer and background image layer. Generally, we employ dual residual networks (ResNet) that are recursively unfolded to sequentially extract rain streaks and predict clean background image. Furthermore, we propose bilateral LSTMs into dual ResNets, which not only can respectively propagate deep features across multiple stages, but also bring the interplay between rain streak layer and background image layer. The experimental results demonstrate that our BRN notably outperforms state-of-the-art deep deraining networks on both synthetic datasets and real rainy images. All the source code and pre-trained models are available at https://github.com/shangwei5/BRN.
Semi-Supervised Learning Based on Hierarchical Generative Models for End-to-End Speech Synthesis

This paper proposes a general framework of semi-supervised learning based on hierarchical generative models and adapts it to a Japanese end-to-end text-to-speech (TTS) system. In English TTS, several end-to-end systems have recently achieved sound quality close to that of natural human speech. However, in non-alphabetic languages such as Japanese, it is difficult to realize true text-input end-to-end TTS due to character diversity and pitch accents. To address this problem, we propose end-to-end TTS based on semi-supervised learning that makes the most of existing data consisting of any combination of text, phoneme, and waveform as training data. To demonstrate the effectiveness of the proposed system, listening tests were conducted for pronunciation and naturalness. Our results show that the proposed system improves both pronunciation and naturalness.
FPSSD7: Real-time Object Detection using 7 Layers of Convolution based on SSD

In recent years, attention has been paid to developing object detection methods from images, based on deep learning. In particular, toward self-driving cars, it is essential to make the detection as accurately as possible, as quickly as possible, as economically as possible. Here, we focus on SSD: Single Shot MultiBox Detector (SSD300) and attempt to improve it with less GPU memory. We propose a new method called “FPSSD7” (Feature Pyramid SSD7), which has less convolutional layers, keeping semantic information. For this purpose, we develop a method to interpolate semantic information between lower layers with high resolution feature maps and upper layer with low resolution features maps. We conducted comparative experiments with the conventional methods using Udacity Annotated Driving Dataset. As the result, we demonstrate that our proposed method outperform the conventional methods.
Stockbridge Damper Identification Of Overhead Power Lines Based On HOG Feature And GWO-SVM

The stockbridge damper identification is of the great significance of the overhead power line inspection. To improve inspection efficiency, this paper proposes an identification method based on Gray Wolf Optimization (GWO), histogram of oriented gradient (HOG) feature and Support Vector Machine (SVM). Firstly, the HOG feature of the data set is extracted as input. GWO is used to optimize parameters c and g. Then, SVM is trained by the optimal parameters to realize the automatic identification of stockbridge damper. Besides, this paper proposes a slide window detection method based on the position of the power line for detection in large scenes. The position is determined by pre-processed methods, and the identification speed is improved. The results show that GWO-SVM can identify the stockbridge damper effectively, and furthermore this method has higher recognition accuracy than the SVM optimized by Grid Search (GS) and Particle Swarm Optimization (PSO).
AVAE: Adversarial Neural Network for Self-representation in One Class Classification

To address the bottleneck of machine learning algorithms in one class classification(OCC), we present an end-to-end deep learning model for OCC. The basic idea is that our model is divided into two parts, reconstructor and discriminator, which are trained by a minmax game to learn the visual features of target class efficiently. Then the target class is enhanced and reconstructed, so that the discriminator can better distinguish between target class and non-target class. Furthermore, we propose an effective training method for RGB image to further improve the accuracy in OCC. The experiment results on CIFAR-10 show that the accuracy of our method is 6% higher than that of the baseline method which called ALOCC in CVPR201S.