Reviewerly/exHarmony huggingface dataset:
Multiple Riemannian Manifold-Valued Descriptors Based Image Set Classification With Multi-Kernel Metric Learning

The importance of wild video based image set recognition is monotonically increasing due to the large amount of video data being collected by various devices including surveillance cameras, drive recorders, smart phones, and internet. The content of these videos is often complex, and it raises the question of how to perform image set modeling and feature extraction for image set-based classification. In recent years, image set classification methods have advanced considerably by modeling the image set in terms of a covariance matrix, linear subspace, or Gaussian distribution. Moreover, the distinctive geometry spanned by them include Symmetric Positive Definite (SPD) manifold, Grassmannian manifold, and Gaussian embedded Riemannian manifold, respectively. As a matter of fact, most of the approaches just adopt a single geometric model to describe each given image set, which may lose information useful for classification. To tackle this problem, we propose a novel algorithm to model each image set from a multi-geometric perspective. Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied to set representation simultaneously. In order to fuse these multiple heterogeneous Riemannian manifold-valued features, the well-equipped Riemannian kernel functions are first employed to map them into high dimensional Hilbert spaces. Then, a multi-kernel metric learning framework is devised to embed the learned hybrid kernels into a common lower dimensional subspace to facilitate classification. We conduct experiments on six widely used datasets each representing a different classification task: video-based face recognition, set-based object categorization, video-based emotion recognition, dynamic scene classification, set-based cell identification, and 3D hand pose estimation, to evaluate the classification performance of the proposed algorithm. The extensive experimental results confirm its superiority over the state-of-the-art methods.
Probabilistic solar irradiance transposition models

Transposition models convert the solar irradiance received on a horizontal surface to in-plane irradiance. All transposition models to date, unfortunately, only produce deterministic (as oppose to probabilistic) estimates. In modern energy meteorology, having the entire predictive distribution is more desirable than relying only on deterministic estimates. To that end, this paper outlines two strategies for creating probabilistic transposition models (PTMs), that can quantify the various types of uncertainty involved in the modeling process. The first strategy seeks the analytic expressions of measurement, model, and parameter uncertainty, and the final predictive variance is the sum of these three types of uncertainty. On the other hand, the second strategy directly models the overall uncertainty as a whole, and uses ensemble model output statistics to estimate the predictive distribution through optimizing a loss function. Both strategies generate estimates of tilted irradiance with Gaussian predictive distributions. As compared to their deterministic counterparts, PTMs clearly offer more insights on uncertainty quantification, during solar energy system design, simulation, performance evaluation, and power output forecasting.
Prediction of maize growth stages based on deep learning

An accurate forecast of daily meteorological factors throughout the year is not only of great significance for the study of climate trends in a certain area but also enables the prediction of crop growth stages. Moreover, the prediction of crop growth stages is related to the scheduling of planting and tillage, the determination of machine harvest time, and the prediction of crop yield. However, highly complex dynamics cause large volatility in meteorological factors, so it is very challenging to predict the crop growth stage accurately, based on weather data. To solve this problem, we propose a data-driven encoder-decoder model, using long short-term memory (LSTM) and convolutional LSTM (ConvLSTM), which can be applied to forecast daily sunshine duration, cumulative precipitation, and average temperature for the coming year. To further test the performance of the ConvLSTM-based model, it is compared with the conventional LSTM encoder-decoder model and the convolutional neural network (CNN)-LSTM encoder-decoder model. The results demonstrate that, the ConvLSTM-based model is more accurate than the others for forecasting temperature (MAE = 2.602 °C, RMSE = 3.456 °C), precipitation (MAE = 3.878 mm, RMSE = 10.503 mm), and sunshine hours (MAE = 3.445 h, RMSE = 4.172 h) in 2014–2016. Furthermore, precise forecasting of meteorological factors allows us to develop a hybrid model and a data-driven model for the prediction of each growth stage separately. The hybrid model combines the ConvLSTM encoder-decoder model with empirical models, whereas the data-driven model comprises the ConvLSTM encoder-decoder model and traditional neural network structures. Finally, we compared the two types of models on a real-world dataset from Dandong, and concluded that the data-driven model is more accurate than the hybrid model for prediction of maize growth stages, with R2 in the range of 0.755–0.883, MAE 0.588–2.205 days, and RMSE 0.978–2.729 days. In the future, these models can also be used to predict the growth stages of other crops.
The Impact of Local Attention in LSTM for Abstractive Text Summarization

An attentional mechanism is very important to enhance a neural machine translation (NMT). There are two classes of attentions: global and local attentions. This paper focuses on comparing the impact of the local attention in Long Short-Term Memory (LSTM) model to generate an abstractive text summarization (ATS). Developing a model using a dataset of Amazon Fine Food Reviews and evaluating it using dataset of GloVe shows that the global attention-based model produces better ROUGE-1, where it generates more words contained in the actual summary. But, the local attention-based gives higher ROUGE-2, where it generates more pairs of words contained in the actual summary, since the mechanism of local attention considers the subset of input words instead of the whole input words.
Performance of Hash Algorithms on GPUs for Use in Blockchain

The main cryptographic primitives in blockchain networks are hashing functions that are designed to form short and unpredictable digests for the message entered. In blockchain networks, hashing is used to build linked block lists, which provide safe and secure storage of important information in a distributed repository. The peculiarity of the hash search problem in blockchain networks allows applying the maximum parallelization of calculations, what good are multithreaded graphics processors (GPUs). In this paper, we explore the performance of GOST 34.311, STRIBOG, KECCAK, SHA2, RIPEMD160, Blake2b, and Whirlpool cryptographic hashing algorithms. HashCat software and various GPUs were used for comparative analysis of efficiency. GPUs were used: Geforce 740M 2GB; Geforce GTX1050ti 4GB; Rx580 Aorus 4GB; Rx580 Sapphire Pulse 8GB; Sapphire Vega 56 8GB.
Single image dehazing by approximating and eliminating the additional airlight component

This paper proposes a novel technique for single image dehazing using adaptive nearest neighbor regularization to obtain a haze-free transmission map and then approximating the additional airlight component present in the hazy image. The proposed method relies on the intensity distribution in the small image patches of the image, exhibited from the Y channel of the YCbCr representation of the image, in order to preserve the texture information of the image. We substitute the commonly used soft matting technique in assessing the refined transmission map for haze removal, by adaptive nearest neighbor classifier. We assume that the actual color of the haze-free pixels in the image is approximated by a set of discrete colors. We discover the haze-free pixels using the Nearest-Neighbor (NN) regularization. Finally, unlike the state-of-the-art methods, we approximate the additional airlight present in the image patch and eliminate that to clear the haze, instead of estimating the transmission of the medium. The proposed nearest neighbor regularization technique automatically changes the patch size, which helps in dealing with the high depth region (e.g., sky region) of the image. We experimented on standard synthetic and real hazy image datasets and observed that, the proposed method outperforms the state-of-the-art, especially for images with sky regions.
Belief at first sight

Abstract Data visualizations are often represented in public discourse as objective proof of facts. However, a visualization is only a single translation of reality, just like any other media, representation devices, or modes of representation. If we wish to encourage thoughtful, informed, and literate consumption of data visualizations, it is crucial that we consider why they are often presented and interpreted as objective. We reflect theoretically on data visualization as a system of representation historically anchored in science, rationalism, and notions of objectivity. It establishes itself within a lineage of conventions for visual representations which extends from the Renaissance to the present and includes perspective drawing, photography, cinema and television, as well as computer graphics. By examining our tendency to see credibility in data visualizations and grounding that predisposition in a historical context, we hope to encourage more critical and nuanced production and interpretation of data visualizations in the public discourse.
Machine Learning &amp; Concept Drift based Approach for Malicious Website Detection

The rampant increase in the number of available cyber attack vectors and the frequency of cyber attacks necessitates the implementation of robust cybersecurity systems. Malicious websites are a significant threat to cybersecurity. Miscreants and hackers use malicious websites for illegal activities such as disrupting the functioning of the systems by implanting malware, gaining unauthorized access to systems, or illegally collecting personal information. We propose and implement an approach for classifying malicious and benign websites given their Uniform Resource Locator (URL) as input. Using the URL provided by the user, we collect Lexical, Host-Based, and Content-Based features for the website. These features are fed into a supervised Machine Learning algorithm as input that classifies the URL as malicious or benign. The models are trained on a dataset consisting of multiple malicious and benign URLs. We have evaluated the accuracy of classification for Random forests, Gradient Boosted Decision Trees and Deep Neural Network classifiers. One loophole in the use of Machine learning for detection is the availability of the same training data to the attackers. This data is exploited by the miscreants to alter the features associated with the Malicious URLs, which will be classified as benign by the supervised learning algorithms. Further, owing to the dynamic nature of the malicious websites, we also propose a paradigm for detecting and countering these manually induced concept drifts.
Just Noticeable Difference Level Prediction for Perceptual Image Compression

A perceptual image compression framework is proposed in this work, including an adaptive picture-level just noticeable difference (PJND) prediction model and a perceptual coding scheme. Specifically speaking, a convolutional neural network (CNN) model is designed with the existing subjective image database to predict the PJND label for a given image. Then, the support vector regression model is utilized to determine the number of PJND levels. After that, a just noticeable difference generation algorithm is developed to compute the corresponding quality factor for each PJND level. Moreover, an effective perceptual coding scheme is devised for perceptual image compression. Finally, the accuracy of the proposed PJND prediction model and the performance of the proposed perceptual coding scheme are evaluated. The experimental results show that the proposed CNN based PJND prediction model achieves good prediction accuracy and the proposed perceptual coding scheme produces state-of-the-art rate distortion performances.
Depth-Guided Dense Dynamic Filtering Network for Bokeh Effect Rendering

Bokeh effect refers to the soft defocus blur of the background, which can be achieved with different aperture and shutter settings in a camera. In this work, we present a learning-based method for rendering such synthetic depth-of-field effect on input bokeh-free images acquired using ordinary monocular cameras. The proposed network is composed of an efficient densely connected encoder-decoder backbone structure with a pyramid pooling module. Our network leverages the task-specific efficacy of joint intensity estimation and dynamic filter synthesis for the spatially-aware blurring process. Since the rendering task requires distinguishing between large foreground and background regions and their relative depth, our network is further guided by pre-trained salient-region segmentation and depth-estimation modules. Experiments on diverse scenes show that our model elegantly introduces the desired effects in the input images, enhancing their aesthetic quality while maintaining a natural appearance. Along with extensive ablation analysis and visualizations to validate its components, the effectiveness of the proposed network is also demonstrated by achieving the second-highest score in the AIM 2019 Bokeh Effect challenge: fidelity track.
Recommendation attack detection based on deep learning

Collaborative recommender systems are vulnerable to recommendation attack, in which malicious users insert fake profiles into the rating database in order to bias the systems output. To reduce this risk, many methods have been proposed to detect such attack. Despite their effectiveness, a lot of these methods are built based on the hand-designed features which are usually difficult to extract even for domain experts. In order to build the detection method without resorting to hand-designed features, in this paper, we propose a deep learning-based approach for detecting recommendation attack (called DL-DRA). The proposed approach can learn directly from the low-level rating data for the training of classifier. Therefore, it does not have the problem of how to extract hand-designed features. We first propose a framework to show the basic structure of the proposed detection approach. Then, we propose a rating matrix generation method to transform the rating vector into rating matrix for each user. After that, we use the bicubic interpolation algorithm to resize the rating matrix in order to reduce the sparsity of the rating matrix. Finally, on the basis of convolutional neural network (CNN), we construct a deep learning network which can learn directly from the resized rating matrix. On this basis, we propose an algorithm for detecting the recommendation attack. We conduct a large number of comparative experiments with the state-of-the-art methods for recommendation attack on different scale MovieLens datasets. The experimental results show that the proposed approach can detect the recommendation attack, effectively and steadily.
PanOpticon: A Comprehensive Benchmarking Tool for Serverless Applications

Serverless computing manifests as the FaaS offering where clients submit code to be managed and run by the service provider in the place of hiring and managing VMs for this purpose. Multiple Cloud Service providers have come up with their own implementations of FaaS infrastructure providing end-users with a multitude of choices. Each such platform provides a non-overlapping set of features which satisfies a subset of users. Further the design of the platform dictates the performance overheads of triggering the function. A tool that automates capturing how a function behaves under different configurations of a platform and across platforms will, therefore, be useful for end-users intending to deploy applications as a collection of FaaS units. In spite of the presence of a few benchmarking tools for FaaS offerings, they lack the comprehensive breadth required to understand the performance aspects of the design choices made by the end-users. Most tools focus on tuning resource parameters like memory, CPU requirements and measure metrics like execution time. They lack the option to measure the effects of features such as function chaining and choice of function triggers. We present PanOpticon - a tool that automates the deployment of FaaS applications on different platforms under a set of tunable configuration choices and presents the users with performance measurements for each configuration and platform.
A Multi-detector Solution Towards an Accurate and Generalized Detection of Face Morphing Attacks

Face morphing attack images are built to be verifiable to multiple identities. Associating such images to identity documents leads to building faulty identity links, causing vulnerabilities in security critical processes. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work introduces a multi-detector fusion solution that aims at gaining both, accuracy and generalization over different morphing types. This is performed by fusing classification scores produced by detectors trained on databases with variations in morphing type and image pairing protocols. This work develop and evaluate the proposed solution along with baseline solutions by building a database with three different pairing protocols and two different morphing approaches. This proposed solution successfully lead to decreasing the Bona Fide Presentation Classification Error Rate at 1.0&#x0025; Attack Presentation Classification Error Rate from 15.7&#x0025; and 3.0&#x0025; of the best performing single detector to 2.7&#x0025; and 0.0&#x0025;, respectively on two face morphing techniques, pointing out a highly generalized performance.
Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions

Understanding the causes or triggers of toxicity adds a new dimension to the prevention of toxic behavior in online discussions. In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger. The prediction model incorporates text-based features and derived features from previous studies that pertain to shifts in sentiment, topic flow, and discussion context. Our findings show that triggers of toxicity contain identifiable features and that incorporating shift features with the discussion context can be detected with a ROC-AUC score of 0.87. We discuss implications for online communities and also possible further analysis of online toxicity and its root causes.
Identifying Referential Intention with Heterogeneous Contexts

Citing, quoting, and forwarding & commenting behaviors are widely seen in academia, news media, and social media. Existing behavior modeling approaches focused on mining content and describing preferences of authors, speakers, and users. However, behavioral intention plays an important role in generating content on the platforms. In this work, we propose to identify the referential intention which motivates the action of using the referred (e.g., cited, quoted, and retweeted) source and content to support their claims. We adopt a theory in sociology to develop a schema of four types of intentions. The challenge lies in the heterogeneity of observed contextual information surrounding the referential behavior, such as referred content (e.g., a cited paper), local context (e.g., the sentence citing the paper), neighboring context (e.g., the former and latter sentences), and network context (e.g., the academic network of authors, affiliations, and keywords). We propose a new neural framework with Interactive Hierarchical Attention (IHA) to identify the intention of referential behavior by properly aggregating the heterogeneous contexts. Experiments demonstrate that the proposed method can effectively identify the type of intention of citing behaviors (on academic data) and retweeting behaviors (on Twitter). And learning the heterogeneous contexts collectively can improve the performance. This work opens a door for understanding content generation from a fundamental perspective of behavior sciences.
Cross-Weather Image Alignment via Latent Generative Model With Intensity Consistency

Image alignment/registration/correspondence is a critical prerequisite for many vision-based tasks, and it has been widely studied in computer vision. However, aligning images from different domains, such as cross-weather/season road scenes, remains a challenging problem. Inspired by the success of classic intensity-constancy-based image alignment methods and the modern generative adversarial network (GAN) technology, we propose a cross-weather road scene alignment method called latent generative model with intensity constancy. From a novel perspective, the alignment problem is formulated as a constrained 2D flow optimization problem with latent encoding, which can be decoded into an intensity-constancy image on the latent image manifold. The manifold is parameterized by a pre-trained GAN, which is able to capture statistic characteristics from large datasets. Moreover, we employ the learned manifold to constrain the warped latent image identical to the target image, thereby producing a realistic warping effect. Experimental results on several cross-weather/season road scene datasets demonstrate that our approach can significantly outperform the state-of-the-art methods.
Fuzzy-Crow Search Optimization for Medical Image Segmentation

Fuzzy C Means (FCM) clustering technique is widely used in medical image segmentation. The classical FCM is sensitive to noise and the objective function often gets trapped in local optima. In this work, we have combined FCM with various optimization techniques to find the optimum cluster centres. The Artificial Bee Colony (ABC), Firefly, Cuckoo and Simulated Annealing (SA) are the widely used optimization techniques in segmentation as well as for classification problems. Here we are proposing the Crow Search (CS) Optimization for the image segmentation using FCM. The CS optimization was found to produce satisfactory and promising results when compared with ABC, Firefly, Cuckoo and SA algorithm in the segmentation of abdomen CT images using FCM. In addition, a thorough investigation has been made in choosing the appropriate validation functions for the clustering techniques. The algorithms have been developed in Matlab 2015a and tested on CT abdomen datasets. Finally, the hardware implementation of FCM-CS algorithm was done in Raspberry Pi B+ model embedded board.