Reviewerly/exHarmony huggingface dataset:
Character Photo Selection for Mobile Platform

As the smartphones are widely used in daily life, people are habituated to take photos in anywhere at anytime. The character photos, which contain both people and landscapes, are the most extensive ones in all kinds of photos. However, it is usually time-consuming and laborious for people to select and manage desired character photos manually. Therefore, automatic photo selection became extremely significant. Most of the existing methods select best character photos by assigning a absolute score or binary label, regardless of the photo’s content. The selected character photos are hence not satisfied. In this paper, we propose an effective automatic framework to select best character photos, which first automatically eliminates photos with unattractive character, then ranks photos and assists users to select the desired ones. Moreover, our framework is especially useful for mobile camera in practical application scenarios. To reduce the burden of photo selection and improve the efficiency of automatic selection from a large number of photos on mobile platform, we divide the photo selection into two stages: eliminating of unattractive photos based on designed efficient and effective features related to faces and human postures, and ranking the remaining photos using a two-stream convolution neural network. To ensure that informative features are selected and less useful ones are suppressed, we design and utilize the attention mechanism module in network. Experiments demonstrate the effectiveness of our method on the automatic selection of the satisfied photos from a large number of photos.
Voting Ensemble Classifier for Sentiment Analysis

This paper proposes use of ensemble voting learning classification approach for sentiment classification of movie review dataset collected from IMDB. Sentiment analysis is a technique used to extract opinions from text including reviews, social messaging, etc. Generally, the opinion is classified into positive and negative polarity. The approach has been used in variety of domains including financial, educational, and other areas. Over the years many researchers have worked on sentiment classification to predict the opinion of text using several machine learning algorithms. The work carried out in this paper proposes the use of ensemble voting learning approach in which Naïve Bayes (NB), K-nearest neighbor (KNN), random forest (RF), and decision tree (DT). The results are compared with individual classification algorithm and voting approach. We have also used normalized and denormalized data to compare the accuracy results. The results show that using voting approach decreases root mean square error and increases precision of the classifier. Maximum accuracy of 80.13% is obtained with voting and normalized data.
Object tracking in siamese network with attention mechanism and Mish function

In order to improve the recognition and tracking ability of the fully-convolutional siamese networks for object tracking in complex scenes, this paper proposes an improved object tracking algorithm with channel attention mechanism and Mish activation function.First, the channel attention mechanism is introduced into the model, and different weights are assigned to each channel to improve the network's representation ability.At the same time, the Mish function is used to replace the ReLU activation function in the network.The smooth Mish function can make better information enter the network, thereby obtaining better accuracy and generalization.Finally, the gradient centralization is embedded in the stochastic gradient function, so as to improve the generalization performance of the network and make the training more efficient and stable.The experiment was performed on the OTB50 and VOT2018 data sets, and the improved algorithm achieved better performance than the original algorithm.
Survey on Template Engines in Java

In today’s fast paced world every minute is very important. In the corporate world and also otherwise Documentation is very important for various purposes like version control, proofs, consent, copyrights and expectations and outcomes/reports. So, because of these reasons template engines have become very important and extremely necessary for the world. Template engines are basically software that help us create result documents from data models and templates by combining them. This paper presents a survey on the newest development of research work on template engines for java, along with an in-depth outline of its research. To the best of what developments have been achieved till now, this survey paper has been written. Finally, the differences, advantages and disadvantages of the various template engines for java, have been tabulated as a part of the results.
A Comprehensive Survey on Effective Feature Selection Approaches for Text Sentiment Classification Process

Feature Selection (FS) is the selection of certain features by using a criterion assessment application. In the age of technological advancements and innovations, there has been a significant rise in the use of different social networking applications such as Facebook, YouTube, Twitter, and e-commerce platforms such as Amazon, Alibaba, Flipkart, and others. It has increased the use of sentiment analysis in the different real-world applications so that there is a classification of text in different preset polarities such as optimistic, pessimistic, and impartial. The current research aims to find out the selection process involved in choosing the optimal features for classification using sentimental textual data. The study presents the scope of the sentimental analysis using textual data, fundamentals of the classification process, and the significance of the feature selection schemes so that there is an implementation of effective feature selection for text classification and analysis of sentiments.
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks

This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style. In contrast to related approaches, our method concurrently predicts all masks, one for each pixel, and thus resolves any conflict jointly across the entire image. Specifically, predictions from overlapping masks are combined into edge weights of a signed graph that is subsequently partitioned to obtain all final instances concurrently. The result is a parameter-free method that is strongly robust to noise and prioritizes predictions with the highest consensus across overlapping masks. All masks are decoded from a low dimensional latent representation, which results in great memory savings strictly required for applications to large volumetric images. We test our method on the challenging CREMI 2016 neuron segmentation benchmark where it achieves competitive scores.
Discriminative semantic region selection for fine-grained recognition

Performances of fine-grained recognition have been greatly improved thanks to the fast developments of deep convolutional neural networks (DCNN). DCNN methods often treat each image region equally. Besides, researchers often rely on visual information for classification. To solve these problems, we propose a novel discriminative semantic region selection method for fine-grained recognition (DSRS). We first select a few image regions and then use the pre-trained DCNN models to predict their semantic correlations with corresponding classes. We use both visual and semantic representations to represent image regions. The visual and semantic representations are then linearly combined for joint representation. The combination parameters are determined by considering both semantic distinctiveness and spatial-semantic correlations. We use the joint representations for classifier training. A testing image can be classified by obtaining the visual and semantic representations and encoded for joint representation and classification. Experiments on several publicly available datasets demonstrate the proposed method's superiority.
An Under-Sampling Method of Unbalanced Data Classification Based on Support Vector

To address the problem of unbalanced class distribution of power grid transmission line fault data, the number of fault classes is relatively smaller compared to the number of normal classes, an algorithm based on support vector under-sampling is proposed for transmission line fault classification. The method obtains the support vector on the original data, calculates the distance from the majority of the classes to the $$k$$ nearest neighbor support vector, then calculates the class bit statistics to measure the local density information according to the distance, and finally under-samples based on the size of the sample class bit statistics. The bird nest dataset and insulator dataset are selected for performance evaluation, and the results show that this method has a good classification effect on unbalanced data, provides a theoretical reference for unbalanced data classification research, and has a certain practical value in the problem of grid transmission line fault classification.
FaceHub: Facial Recognition Data Management in Blockchain

Real-time facial recognition systems are being widely implemented all around the world because of being efficient, reliable and very accurate systems. The Facial recognition softwares are everywhere we look and the best examples are payment methods, where we can securely make our payments without the need to enter our passwords or we can just make our groceries and the payments have done through payment methods by our identity has recognized through camera at a shop. Nowadays, Universities all around the world are also starting to use these very useful systems for taking their attendances or to authenticate their school stuff or students. This system challenge is securing the face data in database. In this chapter, we have discussed the facial recognition system (FaceHub). The requirements modelling and the real-time facial recognition system (FaceHub) that we created in order to make use of it in our University attendance system and introducing blockchain technology for facial data management, which manage the facial data in the permissioned distributed server to securely store the facial data of student. Also, we discussed the benefits of blockchain in FaceHub Data. To further, improve the security of the software by implementing the blockchain into our software.
School information system

Education is an important area of development and electronics. There are several systems that are used to manage school processes. However, each system covers only a certain part of school processes. Within my diploma thesis, I worked on development of complex information system, that covers all processes in the school facility. These processes include leisure activity management, canteen and management of educational system, which consists of electronic grade book, class register, attendance, excuses, timetable, substitution, evidence of employees, subjects and subject plans.
Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests

Random forests are one of the most popular machine learning methods due to their accuracy and variable importance assessment. However, random forests only provide variable importance in a global sense. There is an increasing need for such assessments at a local level, motivated by applications in personalized medicine, policy-making, and bioinformatics. We propose a new nonparametric estimator that pairs the flexible random forest kernel with local sufficient dimension reduction to adapt to a regression function’s local structure. This allows us to estimate a meaningful directional local variable importance measure at each prediction point. We develop a computationally efficient fitting procedure and provide sufficient conditions for the recovery of the splitting directions. We demonstrate significant accuracy gains of our proposed estimator over competing methods on simulated and real regression problems. Finally, we apply the proposed method to seasonal particulate matter concentration data collected in Beijing, China, which yields meaningful local importance measures. The methods presented here are available in the drforest Python package. Supplementary materials for this article are available online.
Research on Particle Swarm Optimization Algorithm

Abstract Aiming at the unsatisfactory performance of the particle swarm optimization algorithm in the optimization process of complex problems and being easy to fall into premature convergence, a power law function to improve the weight value of the particle swarm algorithm is proposed in the paper. It is compared with the linearly decreasing weight particle swarm algorithm, and the test is performed on four typical single-peak and multi-peak functions. The test results show that the particle swarm optimization algorithm in this paper has a better optimization effect.
Semantic annotation of resources based on ontologies:application to a knowledge sharing platform on meningitis

Semantic annotation of resources with ontologies plays a decisive role for semantic search, interoperability and data integration. In this paper, we focus on semi-automated web page annotation for a meningitis knowledge sharing platform. This web page annotation refers to the introduction of categories on a page or a set of tags on a page. Thus, we present a semantic web page annotation approach based on the use of an ontology. In this proposal, ontology is used to determine the categories on the web page. Our method extracts the relevant terms called keywords in the page to annotate. Our strategy for identifying the categories focus on ontology's concepts similarity with each keyword. The tags are identified among the keywords that are not mapped to the ontology's concepts. The results of simulation indicate the approach is feasible for practical use in semantic annotation of a new web page.
Multilingual Voice Impersonation Dataset and Evaluation

Well-known vulnerabilities of voice-based biometrics are impersonation, replay attacks, artificial signals/speech synthesis, and voice conversion. Among these, voice impersonation is the obvious and simplest way of attack that can be performed. Though voice impersonation by amateurs is considered not a severe threat to ASV systems, studies show that professional impersonators can successfully influence the performance of the voice-based biometrics system. In this work, we have created a novel voice impersonation attack dataset and studied the impact of voice impersonation on automatic speaker verification systems. The dataset consisting of celebrity speeches from 3 different languages, and their impersonations are acquired from YouTube. The vulnerability of speaker verification is observed among all three languages on both the classical i-vector based method and the deep neural network-based x-vector method.
Tracking Livestock Using a Fully Connected Network and Kalman Filter

Multiple object tracking (MOT) consists of following the trajectories of different objects in a video with either fixed or moving background. In recent years, the use of deep learning for MOT in the videos recorded by unmanned aerial vehicles (UAVs) has introduced more challenges and hence has a lot of room for extensive research. For the tracking-by-detection method, the three main components, object detector, tracker and data associator, play an equally important role and each part should be tuned to the highest efficiency to increase the overall performance. In this paper, the parameter selection of the Kalman filter and Hungarian algorithm for sheep tracking in paddock videos is discussed. An experimental comparison is presented to show that if the detector is already providing good results, a small change in the system can degrade or improve the tracking capabilities of remaining components. The encouraging results provide an important step in an automated UAV-based sheep tracking system.
Graphical data processing in the Clinical Decision Making System for the Respiratory Diseases Diagnosis using ML methods

Abstract This article considers the basic algorithm of graphic data extraction used in the developed system of clinical decision-making in diagnosis of respiratory diseases, methods for processing files in JPEG and DICOM formats, visualizing and preprocessing images as well as constructing neural network models based on a convolutional neural network that provide detection symptoms of pneumonia in patients based on radiographs. Data extraction and processing are performed in the “Python” programming language using additional libraries: “pydicom” for processing DICOM files. “Pillow” for visualization, “Keras” for building a convolutional neural network model. The relevance of the task is due to the large volumes of graphical data supplied to the input of the CDSS and necessary for its effective functioning. The novelty of this development lies in the application of a set of existing and development of new algorithms for extracting and processing graphic information.
Vector Arithmetic in the Triangular Grid

Vector arithmetic is a base of (coordinate) geometry, physics and various other disciplines. The usual method is based on Cartesian coordinate-system which fits both to continuous plane/space and digital rectangular-grids. The triangular grid is also regular, but it is not a point lattice: it is not closed under vector-addition, which gives a challenge. The points of the triangular grid are represented by zero-sum and one-sum coordinate-triplets keeping the symmetry of the grid and reflecting the orientations of the triangles. This system is expanded to the plane using restrictions like, at least one of the coordinates is an integer and the sum of the three coordinates is in the interval [−1,1]. However, the vector arithmetic is still not straightforward; by purely adding two such vectors the result may not fulfill the above conditions. On the other hand, for various applications of digital grids, e.g., in image processing, cartography and physical simulations, one needs to do vector arithmetic. In this paper, we provide formulae that give the sum, difference and scalar product of vectors of the continuous coordinate system. Our work is essential for applications, e.g., to compute discrete rotations or interpolations of images on the triangular grid.
The Transcendental and the Agonistic: A Media Philosophy Perspective

Abstract This critical response to Dominic Smith’s ‘Taking Exception: Philosophy of Technology as a Multidimensional Problem Space’ begins by outlining the key contributions of his essay, namely his insightful approach to the transcendental, on the one hand, and his introduction of the topological problem space as an image for thought, on the other. The response then suggests ways of furthering this approach by addressing potential reservations about determinism. The response concludes by suggesting a way out of these questions of determinism by thinking the transcendental in concert with the agonistic.
Developing an unmanned vehicle local trajectory using a reinforcement learning algorithm

Abstract This article describes the algorithm development for constructing a local trajectory for an unmanned vehicle or for implementation in an ADAS system using the reinforcement learning method. A special part is dedicated to reinforcement learning. One of the methods that is best suitable for the task conditions will also be implemented. This method will allow bypassing obstacles and reaching the specified short target points.
Analysis Of Methods And Systems For Detecting And Countering Uavs

Abstract This article discusses methods for detecting UAVs in the radio frequency range. The analysis of the existing UAV detection systems is carried out. An overview of ways to counter UAVs is presented, in particular, by carrying out attacks on navigation and communication systems. A prototype of the UAV detection system, which is based on the UAV, is presented. The UAV detection function has been implemented based on the analysis of the radio frequency spectrum.
Max-Margin Deep Diverse Latent Dirichlet Allocation With Continual Learning

Deep probabilistic aspect models are widely utilized in document analysis to extract the semantic information and obtain descriptive topics. However, there are two problems that may affect their applications. One is that common words shared among all documents with low representational meaning may reduce the representation ability of learned topics. The other is introducing supervision information to hierarchical topic models to fully utilize the side information of documents that is difficult. To address these problems, in this article, we first propose deep diverse latent Dirichlet allocation (DDLDA), a deep hierarchical topic model that can yield more meaningful semantic topics with less common and meaningless words by introducing shared topics. Moreover, we develop a variational inference network for DDLDA, which helps us to further generalize DDLDA to a supervised deep topic model called max-margin DDLDA (mmDDLDA) by employing max-margin principle as the classification criterion. Compared to DDLDA, mmDDLDA can discover more discriminative topical representations. In addition, a continual hybrid method with stochastic-gradient MCMC and variational inference is put forward for deep latent Dirichlet allocation (DLDA)-based models to make them more practical in real-world applications. The experimental results demonstrate that DDLDA and mmDDLDA are more efficient than existing unsupervised and supervised topic models in discovering highly discriminative topic representations and achieving higher classification accuracy. Meanwhile, DLDA and our proposed models trained by the proposed continual learning approach cannot only show good performance on preventing catastrophic forgetting but also fit the evolving new tasks well.
Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France. The purpose of the workshop was to serve as a platform for publication and discussion of Information Retrieval and NLP research and their applications in the domain of eCommerce. The workshop program was designed to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in the eCommerce domain. A second goal was to run a data challence on real-world eCommerce data. The workshop drew contributions from both industry as well as academia, in total the workshop received 38 submissions, and accepted 24 (63%). There were two keynotes by invited speakers, a poster session where all the accepted submissions were presented, a panel discussion, and three short talks by invited speakers.
One Row Keyboard: The Concept of Designing a Common Layout for Physical and Virtual Keyboards

The development of technology brings the computerization of everyday life. The most common device used to communicate with a computer is the keyboard. Many papers have been devoted to the study of computer keyboards and virtual keyboards. The authors of this study propose a new concept of a single line keyboard named One Row Keyboard (ORK) which can be applied for both computer and virtual keyboards. Applying the proposed concept enables the number of keys on the keyboards to be reduced significantly. This allows for the development of keyboards even on really small mobile devices. The article also presents a new method of assigning characters to keys for the ORK and shows the use of this method to create exemplary ORK layouts. One Row Keyboard is also the first concept that makes it possible to create a fully alternating keyboard. Noteworthy is that the criterion of maximizing typing alternation is the most popular criterion for keyboard optimization used in the studies devoted to computer keyboards as it leads to an increase in typing speed and decrease in the number of errors.