Reviewerly/exHarmony huggingface dataset:
The performance comparison of different feature to kernelized correlation filter tracker

Feature extraction plays an important role in the tracking process. However, most attention is paid to the performance of the tracker without considering the sensitivity of feature to the environment. In this paper, the tracker based on the Kernelized Correlation Filter (KCF) is chosen to compare the tracking performance of different features such as the grayscale, the Histogram of Oriented Gradients (HOG) and the Color Names on scenarios with different attributes. The tracking accuracies corresponding to different features on sequences with various attributes are compared and validated through the OTB-100 dataset and the ALOV300++ dataset. The results show that the HOG gets the best tracking performance on image sequences with attributes such as background clutters, illumination variation, out-of-plane rotation, occlusion, deformation compared with grayscale and the Color Names. And the grayscale gets the best performance for motion blur, in-plane rotation. The Color Names obtains the best result with scale variation. And the reasons for performance differences between the three features are analyzed. It can be concluded that the accuracy of a tracker can be improved by choosing a proper feature according to the attributes of scenes.
A new algorithm for small moving target detection on dynamic water surface

Aiming at the problem of small moving object detection on dynamic water surface background, a moving object detection algorithm of interval background model is proposed. The algorithm first learns the range of background values on each coordinate point in the video sequence from a video of a non-moving object, and forms an interval background model. when the moving target is detected, the detection is carried out by point-by-point scanning, the value of the current pixel is compared with the value of the background model, if the value of the pixel point is within the range of the background module, the pixel point is the background, otherwise, the moving object is the background. After all pixel points are detected, they are combined into an initial moving target image. Finally, initial movement The target image is subjected to de-interference processing to obtain a final moving target image. The experimental results show that the algorithm has good detection effect on dynamic water surface moving targets and is fast in speed.
EXPLORING MOTHER'S HEALTHY FEEDING PRACTICES OF CHILDREN, AGES 4-6 YEARS: A QUALITATIVE STUDY

Feeding or eating good upbringing which affects the development growth and intelligence of children. For that it is necessary feeding patterns or parenting eat right and balanced in order to stay healthy and avoid infection or other diseases related to nutrition. A balanced diet is eating according to composition of food that our body needs in portions adapted to needs of children at each age. Respondents in this study are the parents of kindergarten children Rabbani North Indaralaya with range of aged 4-6 years. Implementation of FGD was attended by 12 people, consisting; three researchers, two minutes, and seven respondents. Respondents selected through purposive sampling, based on predetermined criteria and is considered capable of answering research problems. The purpose of study is to explore more deeply about parenting feeding mothers in Ogan Ilir associated with understanding (the concept of balanced nutrition, quantity of feeding practices in terms, frequency, form, variety and type of food). This study used a qualitative approach. The data collection technique are Focus Group Discussion. Data processing was done manually using content analysis method (analysis). The results showed majority of mothers do not understand that concept of balanced nutrition, for this aspect the number of feeding infants in Ogan Ilir still do not meet the nutritional needs to children as seen from the children often do not prepared food to consume by their mother if the child does not like the dishes being served. From the aspect of the frequency almost mothers only give meal 2-3 times and 1 times a additional food during the day. From this aspect of mother form has given the same food with family to meal but this menu give only food in like a child. In terms of variety of food provided to children are still less and almost children do not consume vegetables and fruit. Mother feeding patterns in children is not good, lack of parental support in providing food resulted in children accustomed to eating that is not good. Appropriate educational media is required age of children to increase the child's knowledge about nutritious food and motivate children to want to eat nutritious food.
Django Framework and Python-Gammu as Middleware SMS Broadcast

Because the growing of mobile technology in recently days, technology practitioners are required to be able to create a variety of devices and media that support the mobile technology. Application developers compete to create mobile applications that have a variety of features, including video call, voice sending, image sending or text messaging. Developing of mobile applications have an impact on using of Short Message Service, although using of Short Message Service began to shift, but the Short Message Service feature cannot be abandoned, because Short Message Service is a standard feature carried by mobile devices (mobile phones). For this reason, we need to develop Short Message Service Broadcast middleware as an intermediary for Short Message Service communication between web technology and mobile devices. This technology is using for disseminating information between systems that have been built with mobile devices via Short message service. Broadcast system will produce information about new student admissions, That will be received directly by Senior High School Students through their mobile phones. The Middleware can send message broadcast in real time to more than 500 cellular numbers automatically with an average time of 13 seconds for one message. The advantage of using this technology is direct configuration with Python Back-End. Meanwhile, Technology for message inbox still requires further configuration.
Mixed Tiling Scheme for Adaptive VR Streaming

VR technology is rapidly progressing in recent years with incremental consumption demand of immersive media. Transmission is one major challenge to be overcome for ultra high resolution 360 VR video streaming, requiring large throughput and strict latency. Conventional monolithic streaming scheme transfers the entire panorama and impose heavy burden to network load. Tiling based video streaming tackles such problem that it discriminates the FOV (field of view) and surrounded field out of sight then tiles are transmitted in appropriate quality according to viewing probability and network condition. Existing tiling method divides video frame into fixed-size uniform rectangles and encoding efficiency is inevitably decremented. The optimal mesh of video frame tiling is content dependent but typically tiling dimension is fixedly configured by service provider, without elasticity and content awareness. Several proposed optimization methods for tile dimension needs to enumerate all possibility and these offline manner requires specific transcoded samples to be prepared beforehand. In this paper, a bandwidth-efficient mixed tiling scheme is proposed to improve the situation where diverse tiles are spliced into integral frame and tile selection is processed within multiple correlative meshes. We apply a graph algorithm to search the optimal case within multiple meshes and the online manner demands neither exhaustive enumeration nor intensive calculation. We develop an adaptive VR video streaming system and mixed tiling scheme reflects advantage in bandwidth consumption and pixel overheads, compared to existing fixed size tiling scheme and traditional monolithic video streaming approach.
Intelligence augmentation for urban warfare operation planning using deep reinforcement learning

The density, diversity, connectedness and scale of urban environments make military operations challenging. This paper shows that different artificial intelligence techniques can be combined to provide the commander with various form of intelligence augmentation and to support the decision making process. A warfare model has been developed where an AI system, representing a red unit, learns how to select the position for a target and for several improvised explosive devices (IEDs) in order to prevent the blue unit to locate the target. The blue unit is trained to reach the target by using deep reinforcement learning, while an evolutionary algorithm is used to train the red unit. These techniques do not rely on large amounts of historical data. Different approaches have been used and discussed to optimise the co-learning of the two agents, showing that optimal behaviour can be learned in an urban environment. Information about the most likely positions of the target and the IEDs can be extracted from the policy learned by the system, and used by the commander to provide intelligence augmentation while planning an operation and evaluating different possible courses of action. The reliability of this information depends on the realism of the AI system simulating the red unit, that is strictly dependent on the model used for the blue unit during the training.
Binary Image Steganalysis Based on Local Residual Patterns

A binary image steganalysis scheme based on the statistic model of local residual pattern (LRP) is proposed in this paper. LRP means the pattern of a local area of the residual map of the binary image, which is calculated with the XOR operation. The XOR operation is sensitive to the difference between adjacent pixels, which leads to the emphasis on edge property of the residual map. The neighbouring LRPs of the modified pixel will be affect, which makes the statistic model of LRPs change. Thus the trace of steganography can be detected according to the difference between the statistic models. Finally, the experiments we conducted show that our proposed scheme is effective on binary image steganalysis.
Rule-Based Arabic Stemmer as an R package: arStemmer1

Stemming is the processes of removing prefixes, suffixes, and infixes of a word to give the base form of the word. In this paper, we developed a new stemmer for Arabic language and introduced it as an R package called arStemmer1. We compared our stemmer with the well-known stemmer, Khoja stemmer which is one of the best performing stemmers. Our stemmer arStemmer1 outperformed Khoja in six out of seven experiments. We employed deep learning (skip-gram model) to build stop words lists with some manual filtration. The R package arStemmer1 is available for researches to use and test.
USING FORMATIVE ASSESSMENT IN THE E-LEARNING COURSE

Using e-learning gives students the opportunity to study at anytime and anywhere, they can master new disciplines and form additional competencies have already learned. Modern educators appreciate this type of education and place great hopes on it. It should be noted that e-learning is one of the most effective in training students in various fields and areas of education around the world. Famous teachers call e-learning as leading link in the 21st century. However, taking into account all the benefits of this type of training, it should be noted that electronic assessment is a challenge for the teacher, since it is difficult to assess the work that the teacher does not observe clearly. There is no guarantee that the student independently did this or that type of work, honestly fulfilled the task and understood the material provided. In the article a detailed analysis of the notion and process of evaluation of the student's level of education is made in general, and the solution of the problem of evaluation in electronic mode is proposed through the so-called formative assessment. The formative (internal) assessment is aimed at identifying the individual achievements of each student and does not imply either a comparison of the results demonstrated by different students and administrative interventions based on learning outcomes. The comparison of the evaluation process from the traditional to the formative has been made. A number of resources are proposed that will help to introduce a system of formative assessment in an electronic training course. The article also presents the tables that are used when introducing a formative assessment in an electronic training course. The author's example of the application of formative assessment in the electronic training course "Management of the system of electronic education of students of higher educational institutions" is demonstrated.
Tiny face hallucination via boundary equilibrium generative adversarial networks

It is known that actual performance of most previous face hallucination approaches will drop dramatically as a very low-resolution tiny face is provided. Inspired by the latest progress in deep unsupervised learning, this paper works on tiny faces of size 16×16 pixels and magnifies them into their 8× upsampling ones by exploiting the boundary equilibrium generative adverarial networks (BEGAN). Besides imposing a pixel-wise <i>L</i>2 regularization term to the generative model, it is found that our targeted auto-encoding generator with residual blocks and skip connections is a key component for BEGAN achieving state-of-the-art hallucination performance. The cropped CelebA face dataset is preliminarily used in our experiments. The results demonstrate that the proposed approach is not only of fast and stable convergence, but also robust to pose, expression, illuminance and occluded variations.
Digital image forensics of non-uniform deblurring

Non-uniform image deblurring has been extensively studied, but forensics of whether an image is non-uniform deblurred is still an untouched area. In this paper, we firstly propose an approach to localize the non-uniform deblurring in digital images. Firstly, taking advantage of the properties of convolution derivation, multi-derivative gray level co-occurrence matrix (MGLCM) features are proposed to reveal the deblurring artifacts of images. The MGLCM features are extracted from the first and the second order derivative of images. Then sliding window strategy is used. For each sliding window, MGLCM features are extracted and SVMs are exploited to score the detection probability. By changing the scale of the sliding windows, a series of detection probability maps at different scales are obtained. Finally, top-down multi-scale boundary fusion (TMBF) is proposed to get the final detection map. The experimental results demonstrate that the proposed approach successfully localize the deblurred regions with a satisfactory performance.
A New Multi-criteria Recommendation Algorithm for Groups of Users

GroupGuo, Shenghui recommendation algorithms have the advantage of helpingJi, Shujuan groups of usersZhang, Chunjin findWang, Xin favoriteZhao, Jianli items under less time. And, the multi-criteria recommendation system aims at obtaining the user’s preferences over various aspects and make accurate recommendation. This paper presents a new group-oriented multi-criteria recommendation algorithm called GMURec. This algorithm first uses K-means algorithm which generates the groups (i.e., sets of users who have similar interests). Then, it uses the BP neural network to aggregate the groupś preferences and learn the implicit relationship between multi-ratings and overall rating of each group. The performance of GMURec algorithm is compared with three baseline algorithms. Experimental results show that: (1) the precision of GMURec is only lower than the individual-targeted personal multi-criteria recommendation algorithm, but is higher than the other two group ones; (2) the recall of GMURec is as good as or better than other algorithms; (3) the run time of GMURec is the least one among the compared algorithms.
An Image Encryption Scheme Based on the Discrete Auto-Switched Chaotic System

Aiming at finite computational precision would make digital chaotic binary sequences into a short period sequence, so that the security of multimedia encryption information based on chaotic cipher is not guaranteed. In this paper, we designed a discrete auto-switched chaotic system with a simple structure and good performance in order to increase the period length of chaotic sequences. The autocorrelation and complexity experiments of chaotic sequences were performed to demonstrate the good performance of the sequence. Finally, the simulation experiment results show that this image encryption scheme is both reliable and secure.
On Metods of Graphic Object Recognition Based on DTW Metric

We consider the possibility of using the DTW metric for shape recognition of graphic objects with incomplete group of affine transformations, including shift, scaling and rotation. A DTW-based algorithm is proposed that allows the identification of the object shape invariant with respect to such affine transformations. The statistical studies carried out show that the value of DTW metric under distortions is directly proportional to the distortion magnitude. The modeling shows that in the case of identifying the objects that do not belong to the sample class the variance of DTW metric values is significantly greater than the one observed in the case of comparing the distorted objects of the same class. This fact allows assigning a definite value of classification tolerance for each class of figures providing the required probability of false recognition and missing of objects.
Construction of power industry corpus based on data mining and machine learning intelligent algorithm

With the advent of the mobile Internet era, the dissemination and diffusion of information has become faster and faster, and the dissemination and generation of information has also increased exponentially. More and more information is generated and diffused in the Internet, and the subsequent problem is that the collection and determination of information increases with its complexity. Therefore, it is necessary to propose and apply a new method to complete the analysis and processing of the information on the Internet. This paper uses data mining technology and machine learning intelligent algorithm to obtain and classify the information data of the power industry on the Internet, so as to construct the power industry corpus.
Deep Neural Network Acceleration Method Based on Sparsity

With the development of deep learning, deep learning has become more and more widely used in artificial intelligence. At this stage, the deep neural network (DNN) based on high-performance GPU and CPU devices has achieved remarkable results in the fields of object detection and recognition. The DNNs have also been applied to social media, image processing and video processing. With the improvement of neural networks, the depth and complexity of various neural networks are also increasing. On the basis of the sparsity of DNN weights, our method analyzes the influence of the weights on the feature map and obtains the relations between convolution layers. The sparsity of the network channel is deduced from the L1 norm and the L2 norm. And the weights of the DNN are pruned according to sparsity. In the vgg-16 experiment, we can accelerate the neural network by 2.7 times without affecting the accuracy of the neural network. Compared to the unstructured pruning, structured pruning based on the sparsity can effectively improve the speed of the forward and backward process, which has a certain significance for the application of DNNs.
Identification, Assessment and Automated Classification of Requirements Engineering Techniques

Selection of suitable techniques to be used in requirements engineering or business analysis activities is not easy, especially considering the large number of new proposals that emerged in recent years. This paper provides a summary of techniques recommended by major sources recognized by the industry. A universal attribute structure for the description of techniques is proposed and used to describe 33 techniques most frequently quoted by reviewed sources. A pilot study of automated classification of techniques based on attribute values is also reported. The study used fuzzy c-means clustering algorithm and produced pairings of complementary techniques, most of which successfully passed validation conducted by business analysis practitioners.
Cross-collection Multi-aspect Sentiment Analysis

This paper proposes the use of cross-collection topic models to achieve aspect-based sentiment analysis of multiple entities simultaneously. A topic refinement algorithm that enhances semantic interpretability of topics to match that of visually identifiable aspects is presented. It is shown that, with this refinement, topics elicited from cross-collection topic models align excellently with entity aspects. Finally, the utility of opinion words returned from cross-collection topic models in investigated in the task of sentiment analysis. It is concluded that the use of such words as features for sentiment analysis yields more accurate sentiment scores than supervised counterparts.
Emergency Event Matching using Hierarchical Blocking Method

With the extensive application of the knowledge base (KB), how to complete it is a hot topic on Semantic Web. However, many problems go with the big data, and the event matching is one of these problems, which is finding out the entities referring to the same things in the real world and also the key point in the extending process. To enrich the emergency knowledge base (E-SKB) we constructed before, we need to filter out the news from several web pages and find the same news to avoid data redundancy. In this paper, we proposed a hierarchy blocking method to reduce the times of comparisons and narrow down the scope by extracting the news properties as the blocking keys. The method transforms the event matching problem into a clustering problem. Experimental results show that the proposed method is superior to the existing text clustering algorithm with high precision and less comparison times.
Deep Learning-Based Security System Powered by Low Cost Hardware and Panoramic Cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a 360 $$^\circ $$ camera. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.