Reviewerly/exHarmony huggingface dataset:
Superpixel-guided locality quaternion representation for color face hallucination

Recently, the learning based methods have been well exploited to hallucinate grayscale face images. When facing color images, however, the previous approaches either suffering from the non-flexibility for arbitrary pattern shapes or ignoring the inherent color information. To address these concerns, in this paper we propose a new learning model named as Superpixel-guided Locality Quaternion Representation (SLQR) for color face hallucination. Rather than handling squared patches with fixed size, the proposed method handles superpixels with adaptive shapes segmented from face images according to semantic contents, which can well preserve the face spatial features. Moreover, the superpixels are mapped into quaternion space to exploit the inherent spectral information for color image reconstruction. In addition, considering that images are inevitably corrupted by noise in practice, we extend the SLQR to the robust version (W-SLQR) by introducing a proper reweighting strategy into the objective function to suppress noise. Compared to some state-of-the-art methods, various experiments have been conducted to verify the superiority of our proposed methods in hallucinating clean and noisy face images.
Balancing stability and plasticity when learning topic models from short and noisy text streams

Learning hidden topics from short text streams has become crucial in modern applications such as social networks, instant messages, question and answer forums, etc. Building an effective learning method poses two main challenges: Short and noisy data, as well as the stability-plasticity dilemma. In this paper, we investigate carefully how existing methods face these challenges. From our theoretical and empirical analyses, they often deal well with a challenge but ineffectively handle the other. In particular, they often suffer from catastrophic forgetting, because they impose a constraint on the learned knowledge from only the previous time step in the streaming duration. In this paper, we propose a novel method, namely BSP, which has a regularization term based on second-order Taylor expansion to accumulate information from all former minibatches. Moreover, external knowledge and Dropout technique will be combined at each time step to handle better short and noisy texts as well as enhance the model’s plasticity. We empirically evaluate BSP, compared to other state-of-the-art streaming methods in terms of dealing with stability-plasticity dilemma and handling short and noisy texts. The extensive experiments show superior effectiveness of BSP.
Infrared and visible light dual-camera super-resolution imaging with texture transfer network

As low-resolution (LR) images often miss a large amount of feature information, the super-resolution (SR) algorithm that directly maps and reconstructs a single LR infrared image faces a considerable challenge due to the lack of prior features. We propose an infrared and visible light dual-camera SR imaging scheme based on a visible light texture transfer (VTTISR) method, which fills in the missing feature information of LR infrared images by transforming visible light texture features. The proposed VTTISR method consists of a multi-scale texture progressive transfer network and a cross-scale residual aggregation reconstruction network. The multi-scale texture progressive transfer network migrates visible light texture features to infrared features to generate multi-scale texture transfer features. The cross-scale residual aggregation reconstruction network sequentially fuses texture features at corresponding scales and generates 4-magnification SR infrared image. Experimental results show that VTTISR has made significant progress in quantitative image evaluation with higher efficiency. • An infrared and visible light binocular vision SR imaging method is proposed. • An infrared super-resolution algorithm based on visible texture transfer is proposed. • A texture transfer layer is developed to migrate the information of visible image. • The method has a higher efficiency than the method based on patch matching. • The method can obtain HR infrared images at different lighting conditions. conditions.
M-LDQ feature embedding and regression modeling for distribution-valued data

With the improving capacity to collect massive amounts of data, distribution-valued data are increasingly used in many applications, where they are presented in a clustered, summarized, or aggregated form to provide detailed information, as opposed to single-valued data. Most of the existing models for distribution-valued data are subject to limitations attributed to the inherent constraints caused by the special expressions of probability distributions. This makes the practical usage of distribution-valued data highly challenging. This paper introduces a novel feature embedding method to characterize a probability distribution, and on this basis, an effective linear regression model that does not contain additional constraints is proposed. Unlike previous models with nonnegative constraints on coefficients, our model is capable of addressing negative coefficients. The detailed parameter estimation procedure applying partial least squares for this model is presented to guarantee more stable results, especially in the presence of a relatively small sample size or multicollinearity among variables. Overall, the proposed method fundamentally facilitates distribution-valued data regression analysis. Extensive simulation experiments and empirical PM2.5 concentration modeling not only verify the effectiveness of our regression method for distribution-valued data but also demonstrate the advantages of the proposed method compared with existing approaches.
STDIN: Spatio-temporal distilled interpolation for electron microscope images

Recently, flow-based approaches have shown considerable success in interpolating video images. However, in contrast to video images, electron microscope (EM) images are further complex due to noise and severe deformation between consecutive sections. Consequently, conventional flow-based interpolation algorithms, which assume a single offset per position, are not able to robustly model the movement of such complicated data. To address the aforementioned problems, this study propose a novel EM image interpolation framework that accommodates a range of offsets per location and further distills the intermediate features. First, a spatio-temporal ensemble (STE) interpolation module for capturing the missing middle features is presented. The STE is subdivided into two modules: temporal interpolation and residual spatial-correlated block (RSCB). The former predicts the intermediate features in two directions with several offsets at each location. Moreover, the RSCB uses the correlation coefficients for aggregated sampling. Thus, even if intermediate features are severely deformed, the STE effectively improves their accuracy. Second, a stackable feedback distillation block (SFDB) is introduced, which enhances the quality of intermediate features by distilling them from the input, and interpolated images, using a feedback mechanism. Extensive experiments demonstrate that the proposed method presents a superior performance compared with previous studies, both quantitatively and qualitatively.
Robust Weight Perturbation for Adversarial Training

Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation during network training by maximizing the classification loss on adversarial examples. Adversarial weight perturbation helps reduce the robust generalization gap; however, it also undermines the robustness improvement. A criterion that regulates the weight perturbation is therefore crucial for adversarial training. In this paper, we propose such a criterion, namely Loss Stationary Condition (LSC) for constrained perturbation. With LSC, we find that it is essential to conduct weight perturbation on adversarial data with small classification loss to eliminate robust overfitting. Weight perturbation on adversarial data with large classification loss is not necessary and may even lead to poor robustness. Based on these observations, we propose a robust perturbation strategy to constrain the extent of weight perturbation. The perturbation strategy prevents deep networks from overfitting while avoiding the side effect of excessive weight perturbation, significantly improving the robustness of adversarial training. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art adversarial training methods.
Effective full-scale detection for salient object based on condensing-and-filtering network

With the development of deep learning, salient object detection methods have made great progress. However, there are still two challenges: 1) The lack of rich features extracted from multiple perspectives at different encoder levels results in the omission of salient objects with varying scales. 2) The ineffective fusion of multi-level features during decoding dilutes the saliency features, which destroys the purity of the predicted maps. In this paper, we design a Condensing-and-Filtering Network (CFNet), in which a saliency pyramid condensing module (SPCM) and a saliency filtering module (SFM) are proposed to solve the above two problems respectively. Specifically, SPCM introduces pyramid convolution as the basic unit to condense full-scale features from global and local perspectives at each level of the encoder. SFM is equipped with an ingenious 'funnel' structure to effectively filter multi-level features and supplement details, which makes the fusion of features more robust. The two modules complement each other, so that the full-scale features can be used effectively to predict salient objects. Extensive experimental results on five benchmark datasets demonstrate that our method performs favourably against the state-of-the-art approaches, and also shows superiority in terms of speed (16.18ms) and FLOPs (21.19G). Meanwhile, we extend our CFNet to the task of RGB-D salient object detection and achieve better results, which further demonstrate its effectiveness. The code will be made available.
A dual-frequency temporal-geometric phase unwrapping based on computer-generated frequency-carrier Moiré profilometry

A dual-frequency temporal-geometric phase unwrapping based on computer-generated frequency-carrier Moiré profilometry is proposed. While two gratings with different frequencies but share the same direct current component are projected, both alternating current components of two deformed patterns can be skillfully calculated. They are multiplied by a sine function and a cosine function with the highest frequency to extract the computer-generated frequency-carrier Moiré fringe as purely as possible and to efficiently calculate the frequency-carrier wrapped phases. One artificial absolute phase is used to aid the low-frequency phase unwrapping, which assists the high-frequency phase unwrapping. It can relax the restriction of the frequency selection in traditional temporal phase unwrapping, and a higher measuring accuracy can be obtained. Meanwhile, the number of required patterns is reduced to the least two, significantly improving the measurement speed and making it suitable for real-time measurement. The experimental results verify the feasibility and flexibility of the proposed method.
A content-oriented no-reference perceptual video quality assessment method for computer graphics animation videos

In this paper, we propose a content-oriented no-reference (NR) perceptual video quality assessment (VQA) method for computer graphics (CG) animation videos. First, we extract features in terms of spatiotemporal information and its visual perception from the videos as inputs of our proposed artificial neural network-based VQA model. Second, to facilitate the video quality evaluation, we apply a convolutional neural network (CNN) in the VQA model to generate weight factors for the input features adaptively according to the different types of CG content in videos. Third, we build a subjective CG video quality database for validation of VQA metrics. Experiments demonstrated that our method achieved superior performance in terms of evaluating the quality of CG animation videos. Both the code and proposed database are publicly available at https://github.com/WeizhiXian/CGVQA. The corresponding newly established database is available at https:// pan.baidu.com/s/1_P2ZNrLzJwZfG6xa6tKnDQ (password: cgvq).
Representation learning of knowledge graphs with the interaction between entity types and relations

Representation learning of knowledge graphs is the task of representing entities and relations of real-world knowledge as low-dimensional vectors. Some previous works have demonstrated that entity types are beneficial for knowledge representation, but these methods rely too much on prior knowledge or do not have pluggability. To address these limitations, we propose a new method named TRPE which can better explore the entity type information for knowledge graph embedding. We suggest that entities should have multiple representations and each entity type should play a different role in different relations. To better characterize the interaction between entity types and relations, we build the type-relation pairs for each entity and gain each type-relation pair feature through the interaction between type-specific features and relation-specific features. These type-relation pair features are assigned different weights which are aggregated to serve as the transformation of entity embeddings. After that, we divide each entity embedding into some sub-embeddings where each sub-embedding does its own transformation. At the same time, our model is a pluggable module that can be attached to other models. We integrate our model into three baseline models for evaluation on FB15k and FB15k + datasets. The experiment results show that our model brings significant improvement to baseline models. On CoDEx datasets with more noisy entity type data, our model still achieves good results compared with other models, which also demonstrates the generalization ability of our model.
StreamVizzard

Processing continuous data streams is one of the hot topics of our time. A major challenge is the formulation of a suitable and efficient stream processing pipeline. This process is complicated by long restart times after pipeline modifications and tight dependencies on the actual data to process. To approach these issues, we have developed StreamVizzard - an interactive and explorative stream processing editor to simplify the pipeline engineering process. Our system allows to visually configure, execute, and completely modify a pipeline during runtime without any delay. Furthermore, an adaptive visualizer automatically displays the operator's processed data and statistics in a comprehensible way and allows the user to explore the data and support his design decisions. After the pipeline has been finalized our system automatically optimizes the pipeline based on collected statistics and generates standalone runtime code for productive use at a targeted stream processing engine.
Some features of the construction of theatrical artistic reality (analysis of the production of "Eugene Onegin" by Rimas Tuminas)

The article presents two plans for the analysis of the famous production of Rimas Tuminas "Eugene Onegin". The first discusses the problems and difficulties of translating Pushkin's poetic work into the language of the theater. At the same time, the author uses the analysis of this production carried out by Alexander Minkin in the book "Mute Onegin". In particular, a number of techniques are considered (splitting the heroes, Onegin and Lensky, into two persons, giving historical events a modern form of existence, which implies rethinking them); they, according to Minkin, were used in his production by Tuminas. The author provides a voice to Alexander Sergeevich, who does not recognize his work in the production of Tuminas, assuming that he solved problems far from those that interested Pushkin. In the second part, a reconstruction of the logic is proposed, in accordance with which Tuminas most likely acted, creating his own statement. The author shows that, firstly, Tuminas is not trying to present Pushkin's brilliant work in a new, modern way, but creates a new work, his own; secondly, the artistic reality of this new work is made up of very different events, including reflexive ones. He raises the question of how these heterogeneous events create a whole. If in the traditional aesthetics the artist depicts the world and life existing outside of a person (or inside him), which are considered as universally significant for everyone, then in the new one the world and events of consciousness of the personality of the artist and the viewer are described and constituted.
Weapon Detection from Surveillance Images using Deep Learning

Security is the biggest concern in today’s world which needs to be addressed to save people from critical threats. We need to detect these threats at the earliest to protect people and take required actions. Security cameras are used almost everywhere now ranging from our home to shopping malls to banks. Currently, not many surveillance cameras have an automatic weapon detection system but with the advancement in technologies, it can be easily equipped. This will help the people in charge concerned to take the appropriate actions and thus prevent crimes. Deep learning techniques are used widely to detect objects as the traditional methods of object detection have their own limitations in certain situations. One such algorithm – Mask RCNN is implemented in this work to detect guns from surveillance video images. Gaussian deblur technique is used to enhance the features of handgun for efficient detection especially in blurred images. The experiment results show that the performance of the model increased with preprocessing.
Large Scale Follower Recommendation in Instagram

With advancements in predictive and prescriptive analytics over the past few years, various businesses have been extensively using recommender systems for marketing purposes. Say whether it’s some sort of association analysis for customer market or any social networking for connecting people together, this very concept of recommenders has been utilized up to a very large extent over the past decade. When it comes to implementing the underlying concepts behind these systems, most of the time we get to have a paradox of choice of various techniques, which need to be carefully analyzed and acted upon to get the desired accuracy of the model. In cases where the recommendations are made based on a connected graph, we need proper feature generation techniques which play an important role in training the model as per our objective. In this paper, we propose a combination of feature extraction techniques employed over a Gradient Boost Decision Tree model for recommending people whom a person is likely to follow. We also shall investigate a few ways where the recommendation is made based on the common followers of two or more profiles and how likely two or more people may or may not know each other. In any problem, hyperparameter tuning plays the most important part which is a key aspect to optimize the model (so that it doesn’t overfit or underfit the data available) and is totally problem specific.
Spam and Non-Spam URL Detection using Machine Learning Approach

In the modern world, where everyone is connected with internet, a threat has always surrounded every internet user which steals user credentials by various means; this threat is known as Phishing. The attacker acts as a trustworthy agent and steals victim’s credentials or personal details. This information is then used for illegal purpose which may result in identity theft or financial loss. This project provides a better understanding on how phishing attacks are carried out by attackers and how these attacks are detected by websites that uses various detection methods. A tool is designed using machine learning and deep neural net which classify Uniform Resource Locator (URLs) as phishing or legitimate URL. Dataset is collected from various resources that contain phishing and legitimate URLs of websites and then these datasets are provided to the neural net for supervised training. Various classification algorithms are, then, applied on these datasets and then accuracy of these algorithms is predicted.
An Analysis and Comparison Of Deep-Learning Techniques and Hybrid Model for Sentiment Analysis for Movie Review

In today’s world, where each day 2.5 quintillion bytes of data is generated, the safe storage and secure processing of such data is a fundamental task. The vastness of the web has brought trillions of data as comments, reviews, blog posts etc. For a service provider the analysis of these are very essential and also finding out the trends in which sentiment analysis plays a huge role. This differs from typical topic-based text classification as it is done by classifying text based on the sentiment it conveys. User generated text is the basis from which opinion and subject knowledge is drawn for sentiment analysis. In this paper, we deal with the sentiment analysis of the IMDB dataset of movie reviews. We explore the effectiveness of different deep learning techniques such as CNN, LSTM, LSTM-CNN, GRU, BERT, BERT-CNN, BERT-LSTM for the sentiment analysis of the IMDB movie review dataset. Data cleaning and the techniques used are explained and the performance of these various algorithms used are measured in terms of recall, precision and accuracy. In this we hope to find the best machine learning model amongst the ones tested for future research.
Content Based Apparel Recommendation for E-Commerce Stores

Recommendation systems are extensively in use these days. These recommendations may be in the form of friend suggestions on Facebook, suggesting similar questions in Quora, or product suggestions on e-commerce sites, etc. Whenever we use an e-commerce website or app, we get to see product recommendations based on previous search history. Starting from OTT platforms like Prime and Netflix to general e-commerce sites like Flipkart and Amazon all of them use this feature so that the product search for the end-user becomes easier. It’s estimated that e-commerce platforms generate around 35% of their revenue just by these recommendation systems which run for their users. In this paper, we present a content-based recommendation system for women’s apparel where, given an apparel, the system generates other apparel to the users which are similar to the query apparel. We use various text-based techniques to retrieve the information from the product page based on the product image and its description.
Producer-Consumer problem using Thread pool

Many server programs perform repetitive operations on a large number of short tasks. As there is a need for the improvement of the efficiency in doing many concurrent processes, thread-pools came into existence to improve the performance of these concurrent servers. The thread-pool-based solution to the producer-consumer problem is discussed in this paper. We used semaphores to prevent multiple processes from accessing the same buffer at the same time. We’ve discussed how the proposed producer-consumer problem plays out in various scenarios. We observed that using thread-pool instead of threads made this model more efficient in terms of time. This paper describes a real-life scenario in which this approach can be employed (Delivery System).
Central and non central codes of dihedral 2-groups

In this paper, the central and non central codesof semisimple dihedral group algebra FqG, over a finite field Fq, are constructed. Further the distances of these central and non central codes are computed.