Reviewerly/exHarmony huggingface dataset:
Real-Time Face Occlusion Recognition Algorithm Based on Feature Fusion

The real-time face occlusion recognition is an important computer vision problem, especially for the public safety field. In order to construct a real-time face occlusion recognition system, this paper first established a large occlusion face database. Then, this paper proposed a face occlusion recognition algorithm based on the fusion of histogram of oriented gradient(HOG) and local binary pattern(LBP), the experimental results show that the occlusion face recall rate and the unobstructed face recall rate are 92.03% and 93.58% respectively, the speed is about 12.26 ms. Finally, taking into account time factor, this paper established a lightweight deep neural network based on AlexNet with an occlusion face recall rate and an unobstructed face recall rate of 91.79% and 91.42% respectively, and the speed is approximately 22.92 ms. The experimental results show that the face occlusion recognition method based on HOG+LBP features not only improves the recognition rate of occlusion face, but also reduces the time complexity, and illustrates the effectiveness of the algorithm.
On the Applicability of the Modernized Method of Latent-Semantic Analysis to Identify Negative Content in Multimedia Objects

The possibility of applying a modernized method of latent semantic analysis (MLSA) to identify negative content in multimedia objects of web space is considered. A method for analyzing the dynamics of changes in the singular numbers of the original matrix with automatic selection of the range of used rank values is described. The positive dynamics of application of the MLSA method in different directions is shown, where semantic analysis of information is required.
Dense point cloud generation of urban scenes from nadir RGB images in a remote sensing system

A near real-time airborne 3D scanning system has been successfully implemented at Fraunhofer IOSB. This remote sensing system consists of a small aircraft and a ground control station. The aircraft is equipped with the following components: a Digital Acquisition System (DAQ), Inertial Navigation and Global Positioning Systems (INS/GPS), an Airborne Laser Scanner (ALS), and four industrial cameras. Two of these cameras (RGB and near-infrared ones) are nadir oriented, while the other two RGB cameras have an oblique orientation. The acquired LiDAR point clouds, images, and corresponding metadata are sent from the aircraft to the ground control station for further post-processing procedures, such as radiometric correction, boresight correction, and point cloud generation from images. In this paper, the procedure regarding point cloud generation of urban scenes, with images from the nadir RGB camera, is described in detail. To produce dense point clouds three main steps are necessary: generation of disparity maps, creation of depth maps, and calculation of world coordinates (X, Y, and Z). To create disparity maps, two adjacent images (stereopair) were rectified. Afterwards, the PatchMatch Stereo (PMS) algorithm for 3D reconstruction was executed, since it is easy to implement and provides good results according to the Middlebury Computer Vision dataset. Some steps were parallelized to optimize execution speed. Since depth is inversely proportional to disparity, depth maps were calculated from disparity maps. The height of scene elements Z was obtained by subtracting their depth to the camera height. To calculate the remaining world coordinates X and Y, the back-projection equation and the camera intrinsic and extrinsic parameters were used. To validate the PMS algorithm, its resulting point cloud was compared with a LiDAR point cloud and a PhotoScan point cloud. The root mean square errors of both comparisons showed similar values.
Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database

In the present time, Graph Mining has become the most research-oriented field in the advance technologies for its importance in many areas, such as citation graphs, web data mining, chemical structures, protein interaction, social networks, etc. The rapid change in Graph Mining research work is fully dependent on the field of Graph Partitioning (GP) as well as Frequent Subgraph Mining (FSM). In this paper, we define Geometric Multi-Way Frequent Subgraph Mining (GMFSM) approach, which is based on Geometric Partition of a Single Large Graph Database with Frequent Subgraph Mining (FSM) approach that uses filtration technique to reduce number of candidate subgraphs. After partitioning the large graph database, we execute FSM algorithm simultaneously on each subparts which produce the desire result much faster (one-third to half) than existing algorithms. In addition, we use two-way partitioning algorithm recursively to obtain multi-way partition which drastically changes the performance of the algorithm.
A Recommendation Mechanism based on Positive Preferences

In the era of big data how to precisely and efficiently recommend the items that a user really needs is an ongoing research topic. Collaborative filtering based on similarity measure is one of common approaches to achieve this purpose. But similarity comparison becomes not so efficient when the amount of data becomes vast. In this paper we propose a recommendation mechanism which goes through multiple stages of filtering based on user's preferences. We differentiate positive and negative preferences and identify similarity based on them. The users for further collaborative filtering can be reduced by excluding those under the designated threshold. Furthermore, we can set different weights for positive and negative preferences to accommodate different application fields. The experiments show our method can reduce the processing time when there is a large volume of data without loss of the recommendation quality.
Skip The Question You Don’t Know: An Embedding Space Approach

Deep neural network gives people power to generalize hidden patterns behind training data. However, due to limitations on available data collection methods, what neural networks learn should never be expected to deal with all the scenarios: predicting on samples that rarely appear in training set will have very low accuracy. Thus, we design an end-to-end neural network. It learns an inherent discriminative embedding on the training set to perform out-of-distribution(OOD) detection and classification at the same time: both OOD data points and points that resemble those with different labels can be visually observed in this embedding space. Based on this model, we also devise a training scheme that trains on only inliers. Experiments on various datasets and metrics validate that our method outperforms the state-of-art OOD detector.
Knowledge Adaptive Neural Network for Natural Language Inference

Natural language inference (NLI) has received widespread attention in recent years due to its contribution to various natural language processing tasks, such as question answering, abstract text summarization, and video caption. Most existing works focus on modeling the sentence interaction information, while the use of commonsense knowledge is not well studied for NLI. In this paper, we propose knowledge adaptive neural network (KANN) that adaptively incorporates commonsense knowledge at sentence encoding and inference stages. We first perform knowledge collection and representation to identify the relevant knowledge. Then we use a knowledge absorption gate to embed knowledge into neural network models. Experiments on two benchmark datasets, namely SNLI and MultiNLI for natural language inference, show the advantages of our proposed model. Furthermore, our model is comparable to if not better than the recent neural network based approaches on NLI.
Satellite monitoring features of crystalline rocks mining territories within Zhitomyr region based on optical and radar remote sensing data

Opportunities of optical and radar satellite data application for ecological state and reclamation quality assessment of disturbed by crystalline rocks open cast mining lands are considered in the article. Digital elevation models analysis is proposed for the problem of identification of mines among rock dumps and rocky outcrops solution. Radar interferograms were used as source data for DEM creating. The research was performed on three study sites within Korostishiv, Cherniahiv and Khoroshiv districts of Zhitomir region. As result of the study an abandoned quarry with no reclamation done and an illegal stone processing waste dump were found. It is stated that environmental impact of crystalline rock open cast mining not as serious as ilmenite or amber open cast mining impacts and facts of violation of law caused by mining activities were not found.
An advanced orthoimage mosaicking method for urban areas based on edge thickening

This work is an improved version of the previous work. In order to solve the problem that the seamlines are too close to the edges of buildings, a thickened edge diagram are firstly generated and then used for searching seamlines with A* algorithm. The height gradient term is replaced by the root mean square value of height gradients term to generate thickened edge diagram. The thickened edges also bring some troubles that the edges of the adjacent buildings may be connected to each other. It will block the searching algorithm from finding a path through the two buildings. To tackle this problem, the height term is added to the cost function. By doing this, the searching algorithm will find a path across the area between two adjacent buildings since the area between the buildings have small height than on the building. We also defined constrained areas for searching the seamlines. The constrained areas make sure that the seamlines will not intersect to each other. Preliminary results show that the proposed method produces better results than the previous work by both visual and quantitative comparison.
Effectiveness of Speech Mode Adaptation for Improving Dialogue Speech Synthesis

The effectiveness of model adaptation in dialogue speech synthesis is explored. The proposed adaptation method is based on a conversion from a base model learned with a large dataset into a target, dialogue-style speech model. The proposed method is shown to improve the intelligibility of synthesized dialogue speech, while maintaining the speaking style of dialogue.
MATERIAL OBJECTS AS AN ELEMENT OF HISTORICAL REALITY

The article justifies the thesis that material objects, being an element of historical reality, are a form of objective existence of historical past in the present. It is shown that actual past of a material object includes all the transformations it has undergone during its existence, and, consequently, its variability is a feature of its historicism. Among the elements of historical reality transferred into the present by means of material objects, the author identifies the following ones: physical features and characteristics of an object, inherent conceptions as well as relational statuses and qualities imparted by the society.
Research and Development of B/S-Based Data Mining System for Petroleum Information

With continuous enhancements in level of informatization, petroleum industry accumulated massive exploration and development data. How to clarify underlying messages through data mining to provide predictive, decisive and regular information is very urgent problem at the present stage. During implementation of the concerned research, prevailing data mining technologies have been reviewed to develop B/S-based petroleum data mining system with consideration to actual conditions in petroleum industry. The innovative system include designated database, algorithm invoke and result presentation sections. Through application of data fusion techniques, and with consideration to specific mining demands of petroleum industry, the System created designated database; by constructing B/S-based algorithm invoking and presentation platform, MATLAB, R, PYTHON, and other prevailing mining algorithmic languages can be invoked and relevant results can be presented.
Possibility Theory and Description Logics Based New Concept Extraction Approach

The textual information volume is increasing rapidly. Searching for relevant information is becoming more and more difficult in this amount of information "Big Data". Knowledge representation and information extraction are fairly sensitive parts in the information retrieval field for the big databases. In this paper, we propose a new concept extraction approach based on possibility theory and description logics. First, description logics makes it possible to represent information in an efficient and structured manner. Second, possibility theory allows to improve the correspondence between a document and a given concept with the possibility and the necessity measures. As for the possibility measure, it confirms the dissimilarity degree between a document and a given concept, while the necessity measure makes it possible to confirm the similarity between them. The correspondence between the document and the controlled vocabulary is realized with a partial matching allowing the extraction of variant terms different from those existing in the document. The use of the possibilistic and descriptive methods gives more relevance to concepts representing such a document. The irrelevant concepts are filtered in the last step with the use of the semantic relations between concepts. Experiments have been made on different corpora and it prove important results.
WORKING WITH MACHINE TRANSLATION WHILE TRAINING SINGLE-DISCIPLINE SPECIALISTS

The article discusses the advisability of using machine translation systems when training single-discipline specialists, conducts a small-scale study of the result of the work of one of the most popular free systems “Google Translate”, identifies typical errors and provides ways to correct them when processing such translations manually. Illustrative material is taken from the works of senior students majoring in 08.03.01 “Construction (Industrial and Civil Construction)” while studying the discipline “Practicum in the Field of Professional Communication”.
Mutual Information Generation for Improving Generalization and Interpretation in Neural Networks

The present paper aims to show the importance of information augmentation by increasing the number of neurons. We suppose that to cope with new input patterns, we should store information concerning given input patterns as much as possible, even if they are considered to be noises or harmful in improving performance. This is because the noises or unnecessary information may be changed into useful information for new situations. To realize this, we introduce mutual information between input and outputs, and information augmentation is supposed to be realized simply by increasing neurons and correspondingly dimensionality. In addition, all neurons are used as equally and differently as possible to make full use of the redundant number of neurons. The method was applied to the office equipment sales data set. The results confirmed that mutual information could be increased and correspondingly generalization performance could be increased. The reason of improved performance can be explained by the detailed representation of information contained in input patterns created by a large number of neurons. The detailed representation increases a possibility of obtaining necessary information for learning.
Cross-validation of EEG data for cognitive workload evaluation using an eye-tracker in imaging system tasks

In several domains, we found increasing demands to assess cognitive workload and fatigue. This article presents part of the work produced towards the implementation of an integrated system composed of an Electroencephalography (EEG) device and an eye-tracker, focusing on the optical devices. Pupil dilation was found to be related to task difficulty and cognitive workload, where dilation of the pupils indicate greater mental workload. This and other optical parameter can act not only as complement to the EEG system but also as a cross-validation tool. Lenses adjustments and calibration process of the eye-tracker are discussed along with preliminary results from pre-test of the experiment protocol.
Study on Centrifugal Pump Rotor Fault Feature Extraction Based on Fractal Theory

This paper introduces the fundamental theory of fractal and the definition of correlation dimension firstly, then provides the process solving the correlation dimension by utilizing G-P algorithm and the determination method of relevant parameters. Conducting the feature extraction of rotor fault signal by utilizing the combined method of second generation wavelet noise reduction and fractal, the fault feature vector based on correlation dimension for four states of rotor system are analyzed, which are normal, imbalance, collision-friction and misalignment states.
Secure Exchanging of Various Data Types Used for Classification Purposes

Frequently, in epidemiological studies, it is essential to study a disease of concern through observing the records of many patients. These records are usually the property of some local clinics, medical centers or hospitals providing services within the affected areas. The records are often gathered into datasets and each encompasses detailed information about the causative agent of the epidemic diseases in a specific zone. Therefore, trading such datasets, in a way that preserve the privacy and integrity of their contents, is essential. Since, studying these datasets gives a better understanding of the nature of the diseases and eventually compose a cure. In this paper, we compare four well-known secret-key cryptographic techniques to choose the best cipher that passes different evaluations with highest marks. The selected superior cipher would then be involved in providing secure exchanging of such datasets. The experiments on Wisconsin dataset, using java implementations of the four ciphers, show that there are contrasts between the performances of these ciphers which draw a clear picture of the most suitable cipher to use.
Long Range Pupil Location Algorithm Based on the Improved Circle Fitting Method

Since the pupil will show abnormal contraction or expansion after taking drugs, the technology of long range pupil diameter detection can be used for rapid screening of drug users. In this paper, a long range pupil location algorithm is proposed based on the improved circle fitting method. Firstly, the improved histogram-based binarization method is used to obtain candidate pupil contours, and non-pupil contours are filtered according to the shape or size. Then, the ROI is extracted from the original image according to the pupil contours, and the facula contours are extracted and filled by the features, such as gray value, size and position. Finally, an improved circle fitting method is used to obtain an accurate pupil position. The experimental results on CASIA-IrisV4-Distance database show that the proposed algorithm can locate the pupil more accurately and the average deviation of center and radius is smaller compared with the existing pupil detection algorithms.
Similarity Measurement Between Reconstructed 3D Face and 2D Face Based on Deep Learning

Craniofacial reconstruction technology is very important in the field of criminal investigation. But reconstruction a face from skull is not the end of work. The key technology is the similarity measurement between reconstructed 3D face and 2D face image. It can not only be used to retrieve the most similar face image from missing population database but also can be used to value the reconstruction methods. We built a 3D reconstructed face dataset, trained a deep face feature extraction model and built a neural network for similarity measurement. Firstly, the reconstructed 3D face and 2D face image need to be preprocessing. Secondly, deep network is designed for similarity measurement. Finally, we tested the proposed model. The accuracy of the similarity between two kinds of face images was 96.67%. Experiments show that the proposed neural network model can effectively measure the similarity between two kinds of face images.
Using Local Convolutional Units to Defend Against Adversarial Examples

Deep neural networks are known to be sensitive to adversarial examples - inputs that are created in such a way that they are similar (if viewed by people) to clean inputs, but the neural network has high confidence that they belong to another class. In this paper, we study a new type of neural network unit similar to the convolutional units, but with a more local behavior. The unit is based on the Gaussian radial basis function. We show that if we replace the first convolutional layer in a convolutional network by the new layer (called RBFolutional), we obtain better robustness towards adversarial samples on the MNIST and CIFAR10 datasets, without sacrificing the performance on the clean examples.
Rethinking Static Analysis by Combining Discrete and Continuous Reasoning

Static analyses predominantly use discrete modes of logical reasoning to derive facts about programs. Despite significant strides, this form of reasoning faces new challenges in modern software applications and practices. These challenges concern not only traditional analysis objectives such as scalability, accuracy, and soundness, but also emerging ones such as tailoring analysis conclusions based on relevance or severity of particular code changes, and needs of individual programmers. We advocate seamlessly extending static analyses to leverage continuous modes of logical reasoning in order to address these challenges. Central to our approach is expressing the specification of the static analysis in a constraint language that is amenable to computing provenance information. We use the logic programming language Datalog as proof-of-concept for this purpose. We illustrate the benefits of exploiting provenance even in the discrete setting. Moreover, by associating weights with constraints, we show how to amplify these benefits in the continuous setting. We also present open problems in aspects of analysis usability, language expressiveness, and solver techniques. The overall process constitutes a fundamental rethinking of how to design, implement, deploy, and adapt static analyses.
An Anti-Occlusion Correlation Filter Algorithm for Visual Tracking

Visual tracking is one of the most challenging problems in computer vision, and occlusion is one of the difficulties in visual tracking. Since the appearance of the correlation filter tracker, it has attracted the attention of researchers with its superior speed and performance. But the correlation filter tracker has low robustness to occlusion problem. In order to improve the performance of the tracker, we design a dual-feature and dual-correlation filter based on Kernel Correlation Filter (KCF) by fusing spatio-temporal correlation filter, meanwhile, we propose a novel adaptive model updating method. Finally, the algorithm is tested on the OTB data benchmark. Experimental results show our result is better than many classical algorithms including KCF.