Reviewerly/exHarmony huggingface dataset:
Data clustering using hybrid water cycle algorithm and a local pattern search method

Cluster analysis is a valuable data analysis and data mining technique. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems including data clustering. In this paper, a recently proposed algorithm called the water cycle algorithm, based on the evaporation rate is used in conjunction with a local search method namely Hookes and Jeeves method to perform data clustering. Statistical analyses were carried out which show that the hybrid optimization method, in general, performs superior to the methods reported in the literature in terms of solution quality as well as computational performance. The proposed hybrid algorithm is tested on some selected standard datasets obtained from the UCI machine-learning repository. The objective function is based on the Euclidean distance as well as the DB index. The experimental results were compared with the data clustering results reported in published literature. The simulation results confirm the superiority of the proposed hybrid method as an efficient and reliable algorithm to solve clustering problems.
Few-Shot Learning via Feature Hallucination with Variational Inference

Deep learning has achieved huge success in the field of artificial intelligence, but the performance heavily depends on labeled data. Few-shot learning aims to make a model rapidly adapt to unseen classes with few labeled samples after training on a base dataset, and this is useful for tasks lacking labeled data such as medical image processing. Considering that the core problem of few-shot learning is the lack of samples, a straightforward solution to this issue is data augmentation. This paper proposes a generative model (VI-Net) based on a cosine-classifier baseline. Specifically, we construct a framework to learn to define a generating space for each category in the latent space based on few support samples. In this way, new feature vectors can be generated to help make the decision boundary of classifier sharper during the fine-tuning process. To evaluate the effectiveness of our proposed approach, we perform comparative experiments and ablation studies on mini-ImageNet and CUB. Experimental results show that VI-Net does improve performance compared with the baseline and obtains the state-of-the-art result among other augmentation-based methods.
Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics, one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis (CAD) systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion, a deep learning approach is applied, obtaining 97.5% accuracy with a 2.5% miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41% accuracy.Finally, decisionbased fusion applied to both breast cancer prediction models to diagnose its stages, resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches, rapidly diagnosing breast cancer to decrease its mortality rate.
WDNet: Watermark-Decomposition Network for Visible Watermark Removal

Visible watermarks are widely-used in images to protect copyright ownership. Analyzing watermark removal helps to reinforce the anti-attack techniques in an adversarial way. Current removal methods normally leverage image-to-image translation techniques. Nevertheless, the uncertainty of the size, shape, color and transparency of the watermarks set a huge barrier for these methods. To combat this, we combine traditional watermarked image decomposition into a two-stage generator, called Watermark-Decomposition Network (WDNet), where the first stage predicts a rough decomposition from the whole watermarked image and the second stage specifically centers on the watermarked area to refine the removal results. The decomposition formulation enables WDNet to separate watermarks from the images rather than simply removing them. We further show that these separated watermarks can serve as extra nutrients for building a larger training dataset and further improving removal performance. Besides, we construct a large-scale dataset named CLWD, which mainly contains colored watermarks, to fill the vacuum of colored water-mark removal dataset. Extensive experiments on the public gray-scale dataset LVW and CLWD consistently show that the proposed WDNet outperforms the state-of-the-art approaches both in accuracy and efficiency. The dataset CLWD is publicly available at https://github.com/ MRUIL/WDNet.
Intelligent fault diagnosis of rolling bearings based on refined composite multi-scale dispersion q-complexity and adaptive whale algorithm-extreme learning machine

In order to extract the non-linear fault characteristics of rolling bearings more accurately, a novel nonlinear dynamical analysis method, referred to as the refined composite multi-scale dispersion q-complexity (RCMSDQC), is proposed for fault feature extraction of rolling bearings. To improve further the overall performance of the extreme learning machine (ELM) algorithm, the adaptive whale optimization algorithm (AWOA) is used to determine the input weights and hidden layer biases of the ELM. The RCMSDQC has the advantages of strong feature extraction ability and stability compared to the composite multi-scale weighted permutation entropy (CMSWPE) and composite multi-scale permutation entropy (CMSPE) methods. Furthermore, compared to the whale optimization algorithm, particle swarm optimization, and genetic algorithm, the AWOA shows a superior performance in the benchmark function comparison experiment. Based on the experimental rolling bearing data from the Paderborn University, the performance of the proposed method is further evaluated. The experimental results indicate that the proposed fault diagnosis method can identify the type and severity of rolling bearing faults with an accuracy of 99.1%.
Multiscale hybrid algorithm for pre-processing of ultrasound images

One of the diagnostic tools for breast cancer screening is the ultrasound. Ultrasounds are characterized by low cost and non-invasive patterns. A major drawback of ultrasound is the multiplicative speckle noise. Speckle noise limits the effectiveness of images thereby reducing the efficiency of the test. This paper proposes a new algorithm to reduce noise. The method is based on the combination of the multiscale approach, the wiener filter, and the new fast bilateral filter. A multiscale image was first created. Subsequently, the wiener filter was used for initial filtering and to reduce the mean square error of the multiscaled images. Finally, the new fast bilateral filter was used to remove speckle noise. The algorithm was tested on 50 synthetic images degraded by speckle noise with varying intensity and 250 breast ultrasound (BUS) images from two datasets. The results were compared with selected state-of-the-art filters. The proposed approach shows better performance in terms of standard noise evaluation measures. The noise reduction in the US images of breast cancer (BUS) has been verified by selected conventional segmentation procedures. Results indicate that the proposed method reduced speckle better than other methods. Specifically, the proposed method produced a structural similarity index measure value of 95% for both benign and malignant tumors. In addition a peak signal-to-noise ratio of 30.41db and 30.75db for benign and malignant tumors were obtained. The proposed filter also achieves high accuracy in terms of the segmentation measures. The proposed speckle noise reduction algorithm achieves better accuracies than other competing filters. The algorithm will act as a tool for speckle reduction in breast ultrasound images.
Improved phase sensitivity in a quantum optical interferometer based on multiphoton catalytic two-mode squeezed vacuum states

The usage of non-Gaussian states to improve the phase sensitivity of interferometers has been studied before. In this paper, we introduce the multiphoton catalysis two-mode squeezed vacuum (MC-TMSV) state as an input of the Mach-Zehnder interferometer (MZI) and study its phase sensitivity with photon-number parity measurement and the influence of photon losses. We also study the statistical properties of the MC-TMSV state in terms of the average photon number, the Wigner function, and the Mandel-$Q$ parameter. Our results show that the MC-TMSV state can exhibit stronger nonclassical characteristics, thereby making the phase sensitivity more precise with either the increase of the photon-catalyzed number or the decrease of the transmissivity. Furthermore, we also consider the effects of photon losses involving external- and internal-loss processes of the MZI, and we find that in both cases the sensitivity with the MC-TMSV state can be significantly better than that with the TMSV state under the same parameters especially in the serious photon losses and small initial squeezing regimes. We also find that the multiphoton catalysis is more sensitive to the external photon losses compared with the internal ones. Our results here can find important applications in quantum metrology.
Machine Learning with Big Data

In the past decade, machine learning techniques have been used for solving several problems with respect to big data. In current, there are several types of Machine Learning (ML) techniques available like supervised, unsupervised and semi-supervised. Similarly, several techniques like classification, Pre-processing, Association rules, Random forest, Decision tree, Support vector machines, etc. available to solve several problems like data imbalance, machine translation, enhancement in robotics, etc. Today’s we need to several basic facts about machine learning techniques to solve many problems like prediction analysis in several applications, for example, in e-healthcare applications (big data: data generated from connected electronically smart devices)/ in other applications (agriculture, e-commerce, defence, etc.). For that, most of the researcher confused and hesitate to discuss/ decide which technique or metric to use in respective applications. How machine leaning techniques differs from Data mining techniques? So this article reviewed several existing work/ papers and presents a remedy for all (those) researchers, which does not solve only doubt from their first stage (selection of techniques or metrics and doubts regarding data mining and machine learning) but also mitigate several issues with respect to machine learning techniques (in compare to deep learning). Hence in summary, this work summarizes with various needful information related to Machine Learning (including Big Data). Following survey on evaluation metrics and some other related factors, this paper showed some future directions at last.
Towards Anomaly Detectors that Learn Continuously

In this paper, we first discuss the challenges of adapting an already trained DNN-based anomaly detector with knowledge mined during the execution of the main system. Then, we present a framework for the continual learning of anomaly detectors, which records in-field behavioural data to determine what data are appropriate for adaptation. We evaluated our framework to improve an anomaly detector taken from the literature, in the context of misbehavior prediction for self-driving cars. Our results show that our solution can reduce the false positive rate by a large margin and adapt to nominal behaviour changes while maintaining the original anomaly detection capability.
Goal-driven Long-Term Trajectory Prediction

The prediction of humans' short-term trajectories has advanced significantly with the use of powerful sequential modeling and rich environment feature extraction. However, long-term prediction is still a major challenge for the current methods as the errors could accumulate along the way. Indeed, consistent and stable prediction far to the end of a trajectory inherently requires deeper analysis into the overall structure of that trajectory, which is related to the pedestrian's intention on the destination of the journey. In this work, we propose to model a hypothetical process that determines pedestrians' goals and the impact of such process on long-term future trajectories. We design Goal-driven Trajectory Prediction model - a dual-channel neural network that realizes such intuition. The two channels of the network take their dedicated roles and collaborate to generate future trajectories. Different than conventional goal-conditioned, planning-based methods, the model architecture is designed to generalize the patterns and work across different scenes with arbitrary geometrical and semantic structures. The model is shown to outperform the state-of-the-art in various settings, especially in large prediction horizons. This result is another evidence for the effectiveness of adaptive structured representation of visual and geometrical features in human behavior analysis.
Exploiting Multiple Timescales in Hierarchical Echo State Networks

Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights whilst the internal reservoir is formed of fixed randomly connected neurons. With a correctly scaled connectivity matrix, the neurons' activity exhibits the echo-state property and responds to the input dynamics with certain timescales. Tuning the timescales of the network can be necessary for treating certain tasks, and some environments require multiple timescales for an efficient representation. Here we explore the timescales in hierarchical ESNs, where the reservoir is partitioned into two smaller linked reservoirs with distinct properties. Over three different tasks (NARMA10, a reconstruction task in a volatile environment, and psMNIST), we show that by selecting the hyper-parameters of each partition such that they focus on different timescales, we achieve a significant performance improvement over a single ESN. Through a linear analysis, and under the assumption that the timescales of the first partition are much shorter than the second's (typically corresponding to optimal operating conditions), we interpret the feedforward coupling of the partitions in terms of an effective representation of the input signal, provided by the first partition to the second, whereby the instantaneous input signal is expanded into a weighted combination of its time derivatives. Furthermore, we propose a data-driven approach to optimise the hyper-parameters through a gradient descent optimisation method that is an online approximation of backpropagation through time. We demonstrate the application of the online learning rule across all the tasks considered.
Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning

In the last few years use of social networking sites has increased tremendously. People use social media platforms to share their views on almost all subjects. These views are in various forms like, blogs, tweets, Facebook posts, online discussion boards, Instagram posts, etc. Sentiment analysis deals with the process of computationally defining and classifying the views expressed in the comment, post or document. Typically, the aim of sentiment analysis is to find out the customer's attitude towards a product or service. Customers' feedback is vital for businesses, and social media being a powerful platform, can be used to improve and enhance business opportunities if the feedback on social media can be analyzed timely. Therefore, the focus of this paper is to analyze the customer reviews about various restaurants across Karachi - one of the biggest cities of Pakistan. For this research, customer reviews are collected from a very popular Facebook community- the SWOT'S guide to Karachi's restaurants. The contribution of this research is twofold. First, it performs sentiment analysis and classifies each comment as positive, negative. Second, by using text categorization techniques, comments are automatically classified according to feedback about food taste, ambiance, service, and value for money. A manually annotated dataset of around 4000 records was used for training and testing. Several algorithms were used for classification, including Naive Bayes Classifier, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The performance comparison of these algorithms is presented. The best results, that is 95% accuracy, were achieved by using a random forest algorithm.
Exploiting Laguerre transform in image steganography

Steganography, an approach used to conceal information into the digital media, generally works in two domains: spatial and transform. Though spatial domain methods are simpler, but transform domain methods are good at identifying the features which make the end system more secure. In this work, a novel Steganographic scheme based on an integer sequence named Laguerre transform (LT) is proposed. The Cover image is decomposed into non-overlapping m-pixel groups and then each such pixel group is transformed by applying LT. Variable length bits from the secret information are fabricated into the transformed components. A post-embedding adjustment is applied over these components to minimize the distortion. By applying Inverse LT (ILT), the m-pixel groups are re-computed from the resulting adjusted components. Experimental results reveal that disparity between cover and stego-pixels increases as m increases. Proposed scheme offers better stego image and higher payload compared to some state-of-the-art techniques. Code of this method is publicly available here.
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

Many state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple (e.g., nearest centroid) classifiers. We take an approach that is agnostic to the features used, and focus exclusively on meta-learning the final classifier layer. Specifically, we introduce MetaQDA, a Bayesian meta-learning generalisation of the classic quadratic discriminant analysis. This approach has several benefits of interest to practitioners: meta-learning is fast and memory efficient, without the need to fine-tune features. It is agnostic to the off-the-shelf features chosen, and thus will continue to benefit from future advances in feature representations. Empirically, it leads to excellent performance in cross-domain few-shot learning, class-incremental few-shot learning, and crucially for real-world applications, the Bayesian formulation leads to state-of-the-art uncertainty calibration in predictions.
Single Image Human Proxemics Estimation for Visual Social Distancing

In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios. Our approach proposes a semi-automatic solution to approximate the homography matrix between the scene ground and image plane. With the estimated homography, we then leverage an off-the-shelf pose detector to detect body poses on the image and to reason upon their inter-personal distances using the length of their body-parts. Inter-personal distances are further locally inspected to detect possible violations of the social distancing rules. We validate our proposed method quantitatively and qualitatively against baselines on public domain datasets for which we provided groundtruth on interpersonal distances. Besides, we demonstrate the application of our method deployed in a real testing scenario where statistics on the inter-personal distances are currently used to improve the safety in a critical environment.
Are These from the Same Place? Seeing the Unseen in Cross-View Image Geo-Localization

In an era where digital maps act as gateways to exploring the world, the availability of large scale geo-tagged imagery has inspired a number of visual navigation techniques. One promising approach to visual navigation is cross-view image geo-localization. Here, the images whose location needs to be determined are matched against a database of geo-tagged aerial imagery. The methods based on this approach sought to resolve view point changes. But scenes also vary temporally, during which new landmarks might appear or existing ones might disappear. One cannot guarantee storage of aerial imagery across all time instants and hence a technique robust to temporal variation in scenes becomes of paramount importance. In this paper, we address the temporal gap between scenes by proposing a two step approach. First, we propose a semantically driven data augmentation technique that gives Siamese networks the ability to hallucinate unseen objects. Then we present the augmented samples to a multi-scale attentive embedding network to perform matching tasks. Experiments on standard benchmarks demonstrate the integration of the proposed approach with existing frameworks improves top-1 image recall rate on the CVUSA data-set from 89.84 % to 93.09 %, and from 81.03 % to 87.21 % on the CVACT data-set.
Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities

The Internet of Things (IoT) allows various embedded devices and smart sensors to be connected with each other, which provides a basis for building smart cities. The IoT-enabled smart city can greatly benefit people's daily lives, where smartphone is one of the most widely used IoT devices. For example, people can use the phone to check their financial account, store personal data and communicate with peers. Thus it is very important to safeguard the phones from unauthorized access. To complement traditional textual passwords, touch behavioral authentication has attracted much attention while it is still a challenge on how to build a robust scheme in practice. This is because users' touch actions are often dynamic and hard to model. For this challenge, previous work has proved that touch actions could become consistent when users interact with social networking applications. Motivated by this observation, in this work, we perform a study to investigate users' touch behavior within Email applications on smartphones (with Email being one of the most important and widely used means in connecting with others). The study results with 60 participants validate the former observation that users' touch behavioral deviation can be greatly decreased when they play Email applications.