Reviewerly/exHarmony huggingface dataset:
Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos

Abnormal event detection in large videos is an important task in research and industrial applications, which has attracted considerable attention in recent years. Existing methods usually solve this problem by extracting local features and then learning an outlier detection model on training videos. However, most previous approaches merely employ hand-crafted visual features, which is a clear disadvantage due to their limited representation capacity. In this paper, we present a novel unsupervised deep feature learning algorithm for the abnormal event detection problem. To exploit the spatiotemporal information of the inputs, we utilize the deep three-dimensional convolutional network (C3D) to perform feature extraction. Then, the key problem is how to train the C3D network without any category labels. Here, we employ the sparse coding results of the hand-crafted features generated from the inputs to guide the unsupervised feature learning. Specifically, we define a multilevel similarity relationship between these inputs according to the statistical information of the shared atoms. In the following, we introduce the quadruplet concept to model the multilevel similarity structure, which could be used to construct a generalized triplet loss for training the C3D network. Furthermore, the C3D network could be utilized to generate the features for sparse coding again, and this pipeline could be iterated for several times. By jointly optimizing between the sparse coding and the unsupervised feature learning, we can obtain robust and rich feature representations. Based on the learned representations, the sparse reconstruction error is applied to predicting the anomaly score of each testing input. Experiments on several publicly available video surveillance datasets in comparison with a number of existing works demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber–physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in Cloud and Fog architectures for better fulfilment of the requirements of mission criticality and time criticality arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a Cloud and Fog architecture.
Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution

Depth image super-resolution is a significant yet challenging task. In this paper, we introduce a novel deep color guided coarse-to-fine convolutional neural network (CNN) framework to address this problem. First, we present a datadriven filter method to approximate the ideal filter for depth image super-resolution instead of hand-designed filters. Based on large data samples, the filter learned is more accurate and stable for upsampling depth image. Second, we introduce a coarse-to-fine CNN to learn different sizes of filter kernels. In coarse stage, larger filter kernels are learned by CNN to achieve crude high-resolution depth image. As to fine stage, the crude high-resolution depth image is used as the input so that smaller filter kernels are learned to gain more accurate results. Benefit from this network, we can progressively recover the high frequency details. Third, we construct a color guidance strategy that fuses color difference and spatial distance for depth image upsampling. We revise the interpolated high-resolution depth image according to the corresponding pixels in highresolution color maps. Guided by color information, the depth of high-resolution image obtained can alleviate texture copying artifacts and preserve edge details effectively. Quantitative and qualitative experimental results demonstrate our state-of-the-art performance for depth map super-resolution.
Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
Skewed Histogram Shifting for Reversible Data Hiding Using a Pair of Extreme Predictions

Reversible data hiding hides data in an image such that the original image is recoverable. This paper presents a novel embedding framework with reduced distortion called skewed histogram shifting using a pair of extreme predictions. Unlike traditional prediction error histogram shifting schemes, where only one good prediction is used to generate a prediction error histogram, the proposed scheme uses a pair of extreme predictions to generate two skewed histograms. By exploiting the structure of the skewed histogram, only the pixels from the peak and the short tail are used for embedding, which decreases the distortion from the lesser number of pixels being shifted. Detailed experiments and analysis are provided using several image databases.
A novel image encryption algorithm based on polynomial combination of chaotic maps and dynamic function generation

Present paper introduces a polynomial combination of one dimensional chaotic maps that is blended in a dynamic image encryption algorithm. It is special because not only this combination has butterfly folding effect but also it shows generalization property over any polynomial combination. Hence, the butterfly folding effect is caused by governed parameters of polynomial combination. Moreover, multiple simulations and evaluations show the superiority of the proposed chaotic system. An application of this system, which we propose in cryptography, is a novel image encryption algorithm based on dynamic function generation. Compared to the state of the art algorithms, our image encryption algorithm has higher statistical and cryptanalytic properties. Even though this algorithm is not suitable for real-time applications such as streaming video encryption, it makes a good use of the proposed chaotic system. Uppermost cryptanalytic properties that are proven by statistical/numeric tests show good performance and reliability of proposed algorithm for image encryption tasks while unlike any other chaotic image encryption system, our algorithm uses a string input for secret key.
Deep Neural Network Compression by In-Parallel Pruning-Quantization

Deep neural networks enable state-of-the-art accuracy on visual recognition tasks such as image classification and object detection. However, modern networks contain millions of learned connections, and the current trend is towards deeper and more densely connected architectures. This poses a challenge to the deployment of state-of-the-art networks on resource-constrained systems, such as smartphones or mobile robots. In general, a more efficient utilization of computation resources would assist in deployment scenarios from embedded platforms to computing clusters running ensembles of networks. In this paper, we propose a deep network compression algorithm that performs weight pruning and quantization jointly, and in parallel with fine-tuning. Our approach takes advantage of the complementary nature of pruning and quantization and recovers from premature pruning errors, which is not possible with two-stage approaches. In experiments on ImageNet, CLIP-Q (Compression Learning by In-Parallel Pruning-Quantization) improves the state-of-the-art in network compression on AlexNet, VGGNet, GoogLeNet, and ResNet. We additionally demonstrate that CLIP-Q is complementary to efficient network architecture design by compressing MobileNet and ShuffleNet, and that CLIP-Q generalizes beyond convolutional networks by compressing a memory network for visual question answering.
Intuitionistic Fuzzy Twin Support Vector Machines

Fuzzy twin support vector machine (FTSVM) is an effective machine learning technique that is able to overcome the negative impact of noise and outliers in tackling data classification problems. In the FTSVM, the degree of membership function in the sample space describes the space between input data and class center, while ignoring the position of input data in the feature space and simply miscalculated the ledge support vectors as noises. This paper presents an intuitionistic FTSVM (IFTSVM) that combines the idea of intuitionistic fuzzy number with twin support vector machine (TSVM). An adequate fuzzy membership is employed to reduce the noise created by the pollutant inputs. Two functions, i.e., linear and nonlinear, are used to formulate two nonparallel hyperplanes. An IFTSVM not only reduces the influence of noises, it also distinguishes the noises from the support vectors. Further, this modification can minimize a newly formulated structural risk and improve the classification accuracy. Two artificial and eleven benchmark problems are employed to evaluate the effectiveness of the proposed IFTSVM model. To quantify the results statistically, the bootstrap technique with the 95% confidence intervals is used. The outcome shows that an IFTSVM is able to produce promising results as compared with those from the original support vector machine, fuzzy support vector machine, FTSVM, and other models reported in the literature.
Feature concatenation multi-view subspace clustering

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic properties of different views are diverse, even incompatible, few approaches implement multi-view clustering based on the concatenated features straightforward. However, feature concatenation is a natural way to combine multi-view data. To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data. Specifically, multi-view data are concatenated into a joint representation firstly, then, $l_{2,1}$-norm is integrated into the objective function to deal with the sample-specific and cluster-specific corruptions of multiple views. Moreover, a graph regularized FCMSC is also proposed in this paper to explore both the consensus information and complementary information of multi-view data for clustering. It is noteworthy that the obtained coefficient matrix is not derived by simply applying the Low-Rank Representation (LRR) to concatenated features directly. Finally, an effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is designed to optimize the objective functions. Comprehensive experiments on six real-world datasets illustrate the superiority of the proposed methods over several state-of-the-art approaches for multi-view clustering.
Solar radiation estimation methods using ANN and empirical models

Many empirical equations and methods have been used and proposed in order to estimate the solar radiation (Rs). In this work, empirical equations, such as Hargreaves method, Artificial Neural Networks (ANN) technology and multi-linear regression methods (MLR) were used to estimate solar radiation. The daily meteorological measurements of air temperature, radiation, humidity and wind velocity from the stations of Aristotle University Farm and Amintaio in Northern Greece were used to derive the solar radiation models. The measurements of Rs were used to derive new and to evaluate existing models. Different combinations of input variables were examined in the ANN models, and in the MLR models different variables were used. The results of RMSE criteria of the examined models showed that they are in the same range with many other models describing Rs as summarized in many review articles. The use of extraterrestrial radiation and the square root of daily difference in temperature in the ANN and MLR models improve the accuracy of the results. The results of ANN models in comparison to MLR models using the same input variables are consistent between them.
CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer

This paper presents a new data mining technique for an accurate prediction of breast cancer (BC), which is one of the major mortality causes among women around the globe. The main objective of our study is to expand an automatic expert system (ES) to provide an accurate diagnosis of BC. Both, Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) were applied to analyze BC data. The well-known Wisconsin Breast Cancer Dataset (WBCD), available in the UCI repository, was examined in our study. We first tested the SVM algorithm using various values of the C, ɛ and γ parameters. As a result of the first experiment, we were able to observe that the adjustment of these regularization parameters can greatly improve the performance of the traditional SVM algorithm applied for BC detection. The highest obtained accuracy at the first step was 99.71%. Then, we performed a new BC detection approach based on two ensemble learning techniques: the confidence-weighted voting method and the boosting ensemble technique. Our model, called CWV-BANNSVM, combines boosting ANNs (BANN) and two SVMs, using optimal parameters selected during the first experiment. The performance of the applied methods was evaluated using several popular metrics, such as specificity, sensitivity, precision, FPR, FNR, F1 score, AUC, Gini and accuracy. The proposed CWV-BANNSVM model was able to improve the performance of the traditional machine learning algorithms applied to BC detection, reaching the accuracy of 100%. To overcome the overfitting issue, we determined and used some appropriate parameter values of polynomial SVM. Our comparison with the existing studies dedicated to BC prediction suggests that the proposed CWV-BANN-SVM model provides one of the best prediction performances overall.
Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison

Sentiment analysis in social media is harder than in other types of text due to limitations such as abbreviations, jargon, and references to existing content or concepts. Nevertheless, social media provides more information beyond text, such as linked media, user reactions, and relations between users. We refer to this information as social context. Recent works have successfully leveraged the fusion of text with social context for sentiment analysis tasks. However, these works are usually limited to specific aspects of social context, and there have not been any attempts to analyze and apply social context systematically. This work aims to bridge this gap by providing three main contributions: 1) a formal definition of social context; 2) a framework for classifying and comparing approaches that use social context; 3) a review of existing works based on the defined framework.
New attacks and defense for encrypted-address cache

Conflict-based cache attacks can allow an adversary to infer the access pattern of a co-running application by orchestrating evictions via cache conflicts. Such attacks can be mitigated by randomizing the location of the lines in the cache. Our recent proposal, CEASER, makes cache randomization practical by accessing the cache using an encrypted address and periodically changing the encryption key. CEASER was analyzed with the state-of-the-art algorithm on forming eviction sets, and the analysis showed that CEASER with a Remap-Rate of 1% is sufficient to tolerate years of attack.
Enabling Verifiable and Dynamic Ranked Search over Outsourced Data

Cloud computing as a promising computing paradigm is increasingly utilized as potential hosts for users&#x2019; massive dataset. Since the cloud service provider (CSP) is outside the users&#x2019; trusted domain, existing research suggests encrypting sensitive data before outsourcing and adopting Searchable Symmetric Encryption (SSE) to facilitate keyword-based searches over the ciphertexts. However, it remains a challenging task to design an effective SSE scheme that simultaneously supports <i>sublinear search time</i>, <i>efficient update and verification</i>, and <i>on-demand information retrieval</i>. To address this, we propose a Verifiable Dynamic Encryption with Ranked Search (VDERS) scheme that allows a user to perform top-<inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2922177.gif"/></alternatives></inline-formula> searches on a <i>dynamic</i> document collection and verify the correctness of the search results in a secure and efficient way. Specifically, we first provide a basic construction, <inline-formula><tex-math notation="LaTeX">$\mathsf {VDERS}^0$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="sans-serif">VDERS</mml:mi><mml:mn>0</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="liu-ieq2-2922177.gif"/></alternatives></inline-formula>, where a <i>ranked inverted index</i> and a <i>verifiable matrix</i> are constructed to enable verifiable document insertion in top-<inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq3-2922177.gif"/></alternatives></inline-formula> searches. Then, an advanced construction, <inline-formula><tex-math notation="LaTeX">$\mathsf {VDERS}^{\star }$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="sans-serif">VDERS</mml:mi><mml:mi>&#x2605;</mml:mi></mml:msup></mml:math><inline-graphic xlink:href="liu-ieq4-2922177.gif"/></alternatives></inline-formula>, is devised to further support document deletion with a reduced communication cost. Extensive experiments on real datasets demonstrate the efficiency and effectiveness of our VDERS scheme.
PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification

In this work we present an experimental setup to semi automatically obtain exhaustive nuclei labels across 19 different tissue types, and therefore construct a large pan-cancer dataset for nuclei instance segmentation and classification, with minimal sampling bias. The dataset consists of 455 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 216.4K labeled nuclei, each with an instance segmentation mask. We independently pursue three separate streams to create the dataset: detection, classification, and instance segmentation by ensembling in total 34 models from already existing, public datasets, therefore showing that the learnt knowledge can be efficiently transferred to create new datasets. All three streams are either validated on existing public benchmarks or validated by expert pathologists, and finally merged and validated once again to create a large, comprehensive pan-cancer nuclei segmentation and detection dataset PanNuke.
Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation

We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it's residual variant.
Semi-supervised and Task-Driven Data Augmentation

Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen images. In practice, obtaining a large number of annotations from clinical experts is expensive and time-consuming. One way to address scarcity of annotated examples is data augmentation using random spatial and intensity transformations. Recently, it has been proposed to use generative models to synthesize realistic training examples, complementing the random augmentation. So far, these methods have yielded limited gains over the random augmentation. However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process. With this motivation, we propose a novel task-driven data augmentation method where to synthesize new training examples, a generative network explicitly models and applies deformation fields and additive intensity masks on existing labelled data, modeling shape and intensity variations, respectively. Crucially, the generative model is optimized to be conducive to the task, in this case segmentation, and constrained to match the distribution of images observed from labelled and unlabelled samples. Furthermore, explicit modeling of deformation fields allows synthesizing segmentation masks and images in exact correspondence by simply applying the generated transformation to an input image and the corresponding annotation. Our experiments on cardiac magnetic resonance images (MRI) showed that, for the task of segmentation in small training data scenarios, the proposed method substantially outperforms conventional augmentation techniques.