Reviewerly/exHarmony huggingface dataset:
Dynamic Attribute Package: Crowd Behavior Recognition in Complex Scene

Crowd behavior recognition under complex surveillance scenarios is a fundamental and important problem in crowd management application. In this paper, a comprehensive and specific overall-level dynamic attribute package is proposed by considering local pattern-related motion and group-level motion together to represent crowd movement. Curl and divergence map of normalized average motion vector field act as local pattern-related motion, which represents physical movement tendency of each particle. Group-level motion explores crowd interaction of inter-/intra-group, which focus on depicting crowd’s social dynamic property. The complementary characteristic of two motion representation in different level is analyzed and verified. Single frames in video clips and the corresponding dynamic attribute packages are sent into two-branch structured ConvNet, which can extract more discriminative spatial-temporal feature for behavior recognition. Experiment results conducted on CUHK dataset show that the proposed crowd behavior recognition framework outperforms than existing approaches and obtains the state-of-art performance.
Digital image encryption using chaotic DNA encoding in frequency-domain

This paper proposes a digital image encryption algorithm based on dynamic DNA encoding and operations using hyper digital chaos in frequency-domain. The chaotic DNA encoding and operations are applied to diffuse and scramble both of the amplitude and phase components in frequency-domain. The proposed multi-fold encryption algorithm is evaluated via numerical verification, in which the ideal performances of the encrypted images are achieved, in terms of uniformity in histogram, entropy as well as correlation. The results show that, since the chaotic diffusion and scrambling are introduced in the proposed encryption, each cipher-pixel is influenced by all of the plain and cipher-pixels, which improves the plain-text sensitivity of the encrypted image, and the secure performances against the differential attacks. Moreover, high security level of the encrypted image is achieved with a huge key space, due to the extremely high sensitivity of hyper digital chaos applied in the proposed encryption scheme, therefore it has the strong capability to resist brute-force attacks. In addition, the statistical characteristic of the original image is destroyed completely, thus it improves the security against the statistical attacks.
Temporal attention quality aware network for video-based person re-identification

Person re-identification (Re-ID) is an object recognition method based on visual appearance information. It is mainly restricted by the changes in person posture, shooting angles, the changes in the front, back and light of people that are mainly captured and the noises caused by shake or blur. Currently, single-frame person Re-ID is still the mainstream research. In view of the limited information of single-frame images, this paper adopts temporal attention sequence modeling to conduct research on person Re-ID based on video sequences, considering not only the content information of images but also the movement information between frames, etc. In this paper, a temporal attention quality aware network (TA-QAN) is proposed. By extracting the temporal information between frames, all the frame sequences in the complementary information are effectively aggregated, and the influence of the quality image region is significantly reduced. The temporal attention quality aware network is used to extract temporal information between frames through temporal convolution. The comparison experiment with other feature extraction methods shows that the proposed method has the best performance in PRID 2011 and iLIDS-VID2014 data sets.
Object detection and segmentation using DenseNets and SIFT Keypoint Match

In this paper, we propose a model based on DenseNets and scale invariant feature transform (SIFT) keypoint match technology for object detection and segmentation. DenseNets is built on Convolutional Neural Networks (CNNs) with dense connections and used for semantic image segmentation. Our main idea is that, on the basis of the DenseNets model, we conduct the morphological processing, and apply the SIFT keypoint match technology to detect the object pixels. Opening operation and closing operation are the basic operations of morphological processing. They all consist of erosion operation and dilation operation but the order is different between them. The morphological processing combines two kinds of operations and can form a morphological filter which can filter the noise. The SIFT keypoint match algorithm is widely used to extract the invariant of position, scale and rotation so we use it to eliminate the misjudgment. Our experiments show that our method can acquire more accurate results compared with DenseNets model.
THE ORGANIZATION OF PROJECT ACTIVITIES WITHIN THE STUDENTS' WORK EXPERIENCE INTERNSHIP OF SPECIALTY "E-LEARNING MANAGEMENT IN THE INTERCULTURAL SPACE

Modern labor market demands new educational and innovative skills, ability to work with the large volumes of data, skills of cooperation, communication, ability to think critically, life and career skills from a new generation of students. The main function of modern education is the formation of these skills and a task of the institution of higher education (IHE) is the formation of the effective educational environment. In the information-oriented society, statements and informational process are becoming one of the most important components of human life and society that is caused by the growing processes of global informatization and computerization of various fields of activity of society and the person. Already today, based on digital technologies, scientific and production technologies, business technologies, educational technologies, etc. are created, developed and improved. The article presents the features of the project method and project activities. The emphasis is on wiki-technology, as one of the forms of innovative methodological learning technologies to support the educational process, which enables content to communicate in the web space, discuss significant issues, share the experience, collaborate, and use various educational Web resources. In addition, the article states that students can be active participants in the project activity, determine its purpose, tasks and seek ways to solve them. For representation of results of the practice of students of specialty "E-learning management in the intercultural space", Borys Grinchenko Kyiv University has chosen a wiki-platform and has created a project to support the master's student internship "Teaching internship".
IDCDP: a novel data preprocessing method

Correct tracks obtained through effective data preprocessing are important to multi-target data fusion, especially in the circumstance of dense targets and strong interference. Outliers and target switch arise in the uncertainty of measurement data in the practical applications. In this paper, we propose a novel real-time data preprocessing method for outlier detection and target switch identification to obtain correct tracks, named as IDCDP (i.e., innovation and density combined data preprocessing). Experimental results demonstrate the effectiveness of IDCDP, which achieves outlier-free adaptive filtering and provides authentic tracks for multi-target data fusion.
A fast hough transform to real-timely extract axes of the axes symmetry target in high-speed image processing system

In range test, for measuring the target attitude in target terminal trajectory, we developed a fast Hough transform to real-timely extract axes of the axes symmetry target in high-speed image processing system. The theoretical analysis shows that the accuracy of this scheme is higher than that of standard Hough transform, and the closer to one the absolute value of slope is, the lower the error is. Experimental result shows that the computing speed for line detection of this scheme is higher than standard Hough transform, and the greater the noise is, the more obvious the advantage of speed is. In addition, the error distribution of the experiment is in agreement with the theoretical error distribution.
A deep learning model compression algorithm based on optimal clustering

Nowadays, the models of deep learning are increasingly used in various industrial applications. However, the storage space of the models is too large, which makes it quite difficult to apply to mobile devices. In order to solve this problem, a model compression algorithm based on optimal clustering is proposed in this paper. Firstly, the model parameters of fully connected layer in deep convolutional neural network are clustered according to the best clustering method. Then the cluster center of the parameters is selected as the representative of the original parameter matrix. At last, the parameters of the cluster center are transformed differently in the forward calculation of the model to achieve the effect of compressing the parameters of the model and ensured the accuracy of the model. The compression algorithm proposed here is compared with other model compression algorithms in several common deep learning models such as Alexnet, VGG16 and so on. The results show that the algorithm proposed in this paper can compress the memory of the model greatly and improve the accuracy.
Depth estimation via light field camera with a hybrid stereo matching method

Light field camera has the ability to capture ray’s position and direction information through a single shot. The multiview images generated from the light field image provides unique advantage for depth estimation. In this paper, a method for 4D light field depth estimation was proposed by exploiting a hybrid stereo matching method with multidirectional multi-baseline stereo matching and K-Means segmentation. The multi-baseline stereo matching is adopted to effectively utilize the information among multiple low resolution sub-images. The multi-directional consistency checking is used to identify the fallible matching results due to occlusion or lack of texture. The K-Means segmentation method is used for locate the object boundary and hence ensure that the match window lies within a same object. A weighted median filtering method is adopted to further increase the quality of the disparity map. The experiment verifies the effectiveness of the method used. We evaluated the performance of our method on a publicly available dataset. Result shows that the proposed method can achieve a competitive result with a reasonable running time.
Sparse Manifold Learning based on Laplacian Matrix

In machine learning, a group of high-dimensional data point set which represents an image set can be looked upon as the point set distributing on a nonlinear manifold. Typically, the manifold dimension is much lower than the dimension of data points. Therefore, it is important to explore the real dimension and the real geometry of high-dimensional data point set. Based on this objective, researchers put forward the concept of manifold learning. Traditional manifold learning can achieve dramatic dimensional reduction on high-dimension points, but there are still some problems of it. In the aspect of processing a large amount of data currently, traditional manifold learning algorithm reveals many weaknesses mainly in time consumption. To improve the deficiency, the paper puts forward a new algorithm, aiming at simplifying the time process of manifold learning algorithm which is also abbreviated as SLEP. In the part of the experiment, the study makes a comparative experiment between the synthetic data set and the real data set. The result shows that the proposed algorithm improves time efficiency.
A novel approach to multi-resolution technique for fast pattern recognition

Multi-resolution is a commonly used acceleration algorithm for pattern search. However, there still exist two unsolved questions: "how many levels of image pyramid should be built" and "how to propagate the badly degraded truth patterns to the next pyramid level". We address these two questions through a novel distinction analysis and propagation strategy based on multimodality analysis. Experimental results show that this method has good practicability and advantage.
Assessing change in access to WASH in Palestinian schools

This paper presents the main results from a knowledge, attitudes, and practices (KAP) survey on water, sanitation, and hygiene (WASH) in government schools in the State of Palestine (SoP). In 2012 a baseline WASH KAP survey was conducted in 411 schools. In 2015–2016 a statistically representative survey was performed; the sample included 381 of the 411 schools originally sampled in 2012. The survey targeted basic and secondary schools in urban and rural areas of all educational directorates in the West Bank and Gaza Strip. The survey was conducted by UNICEF in coordination with the Ministry of Education and Higher Education (MoEHE). The findings of the 2016 survey show positive changes in the availability of potable water in schools, which also enabled hygiene and sanitation activities to improve. For instance, more schools taught hygiene education to students on a daily basis in 2016 than in 2012. However, the survey revealed that ensuring the participation of parents and students in WASH activities and continuity of supply of soap, toilet paper, and sanitary pads remains a challenge. This paper concludes with recommendations to further improve the access to WASH in Palestinian schools.
SPECIFIC ASPECTS OF ALAIN`S TEACHING ACTIVITY

The article deals with the specific aspects of the teaching activity of the French teacher of philosophy Alain (Emily-Auguste Chartier). The scholar emphasized the importance of the influence of family education in the formation of the individual and added the necessity of strict education. According to Alain`s theory, school helps the child to reveal their natural abilities independently of their family ties. The main literary genre (propos), which the scientist used to write his ideas, is highlighted. The scientist underlined the usefulness of writing such works as essays and reflections on the given topic, where students learn to express their thoughts correctly and intelligently, overcome the fear of public speaking. The philosopher added that a class also influence on educational process, that is why he preferred the absence of additional learning material on the walls as they distract student’s attention. It is defined that children develop their natural abilities, learn to cope even with those tasks that initially cause rejection and a desire to retreat, due to hard-working nature. It is emphasized that studying of masterpieces, taking into consideration the experience of previous generations, their knowledge about human nature, the relationship between personality and the surrounding world is very important for formation of healthy and intellectual society. The basic principles of teaching, which were guided by the French teacher, such as the principle of diligence, the principle of freedom of thought, the principle of respect and detailed analysis of the works of famous scholars and writers, and the principle of the connection of learning with life, are determined. These principles are possible only with the presence of a teacher who has professional qualities, the desire to direct his students to work and the aspiration to learn and study the necessary information for their adult life.
A Lightweight SHADE-Based Algorithm for Global Optimization - liteSHADE

In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evolution (SHADE) is presented as the first step towards a simple, user-friendly, metaheuristic algorithm for global optimization. This simplified algorithm is called liteSHADE and is compared to the original SHADE on the CEC2015 benchmark set in three dimensional settings – 10D, 30D and 50D. The results support the idea, that simplification may lead to a successful and understandable algorithm with competitive or even better performance.
Method of Optimal Caemra Path Estimation Based on Optical Flow for Video Stabilization

Video captured by a moving camera system often suffer from annoying jitters because of undesired motion. This paper presents an optimal camera path estimation method of moving camera for stabilized video generation. The proposed video stabilization algorithm consists of three steps: i) estimation of optical flow between adjacent frame, ii) extraction of original camera path based on optical flow, and iii) optimal camera path estimation and frame warping. Experimental result show that the proposed video stabilization algorithm can significantly remove the undesired motion. The proposed method can be applied to surveillance system, driving assistant system, and various computer vision systems.
Local pattern-based illumination compensation in face images

Facial illumination severely affects the face recognition performance; thus, it should be finely compensated beforehand. This paper mainly studies three representative illumination-insensitive representation methods including GRF, WF and LBP, all of which consider using the local pattern information to extract facial features. We first present the ideas and theories of each method. Then based on the reflectance model, the underlying connections of GRF and WF are discussed through showing the deduction of GRF and WF how they exclude the luminance component and are only related to the intrinsic facial features. We also give the explanation about the correspondence of LBP to the reflectance model. Finally, experiments on a standard but challenging illuminated face database are conducted, in which GRF, WF and LBP are tested and compared to other illumination normalization methods in terms of face recognition rate.
Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images

Nowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the minimization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust fit. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.
Locally Adaptive Noise Covariance Estimation for Color Images

Noise estimation is crucial in many image processing tasks such as denoising. Most of the existing noise estimation methods are specially developed for grayscale images. For color images, these methods simply handle each color channel independently, without considering the correlation across channels. Moreover, these methods often assume a globally fixed noise model throughout the entire image, neglecting the adaptiveness to the local structures. In this work, we propose a locally adaptive multivariate Gaussian approach to model the noise in color images, in which both the content-dependence and inter-dependence among color channels are explicitly considered. We design an effective method for estimating the noise covariance matrices. Specifically, by exploiting the image self-similarity property, we could estimate a distinct noise covariance matrix for each local region via a linear shrinkage estimator. Experimental results show that our method can effectively estimate the noise covariance matrices. The usefulness is demonstrated with real color image denoising.
Reading Contents Estimation from an Indoor Surveillance Camera

In this paper, we propose a system for estimating reading content in a booklet from an indoor surveillance camera. Here, we assume that a reading content can be specified by estimating the following two; which booklet/page, and which region in the page. We implement a reading booklet/page estimation method based on image search, and propose a reading region estimation method based on the slight difference of body pose of the reader. We evaluated the method as a 44 classes classification problem, which consists of eleven pages of booklets and four regions in each pages. We confirmed that the system achieved 25.6% in accuracy of the reading content estimation in our evaluation setting.
Fusing the original and its mirror image to perform collaborative representation for face recognition

More training samples can represent more various changes of face image, owing to the variational illumination, facial expressions and poses. However, in real-world applications, due to the limitation of storage capacity and captured image time, the number of face training samples obtained are often limited. In this paper, for the small size problem, we propose to firstly generate various mirror images by the original face images, viewed as its mirror image. Its mirror image is opposite to the original face image with facial details. Then the original sample and its mirror image is respectively used to perform collaborative representation classification method (CSC). Finally, an adaptive weight selection method is proposed to fuse the original face image and its mirror image based on assigning a better weight to the original face image. The results of experiments show that the presented scheme is effective.
Dense dilation network for saliency detection

Saliency detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the Dense Dilation Network(DDN) -- a novel saliency model based on Dense Convolutional Network(DenseNet), all layers' feature maps are extracted in the same resolution through a step-wise upsampling procedure which contains dilated convolution filters and deconvolution filters. To derive saliency maps, we propose a fusion dense block which contains dilated convolution filters and 1&times;1 Conv layers to fuse all low-level and high-level feature maps. DDN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that DDN outperforms the state-of-the-art methods on saliency detection and it requires less parameters and computation time.