Reviewerly/exHarmony huggingface dataset:
Push Content Selection Method Based on Dynamic Threshold

With the rapid development of power communication network, the problems of insufficient operation and maintenance human resources and low level of informatization of on-site operation and maintenance have become increasingly prominent. According to the actual situation of power communication operation and maintenance, this paper proposes a dynamic threshold based push content selection method. The method fully considers the task information and the personnel information, and dynamically adjusts the push threshold according to the effect of the operation and maintenance, thereby improving the efficiency of information push. The simulation experiments are carried out on the proposed algorithm. Simulation results show that the proposed algorithm can not only adapt to the changing scenarios, but also improve the accuracy and recall rate of information push, and improve the quality and efficiency of on-site operation and maintenance of the power communication network.
Utilizing Template Diversity for Fusion Of Face Recognizers

If multiple face images are available for the creation of person's biometric template, some averaging method could be used to combine the feature vectors extracted from each image into a single template feature vector. Resulting average feature vector does not retain the information about image feature vector distribution. In this paper we consider the augmentation of such templates by the information about diversity of constituent face images, e.g. sample standard deviation of image feature vectors. We consider the theoretical model describing the conditions of the usefulness of template diversity measure, and see if such conditions hold in real life templates. We perform our experiments using IARPA face image datasets and deep CNN face recognizers.
Towards Specifying Symbolic Computation

Many interesting and useful symbolic computation algorithms manipulate mathematical expressions in mathematically meaningful ways. Although these algorithms are commonplace in computer algebra systems, they can be surprisingly difficult to specify in a formal logic since they involve an interplay of syntax and semantics. In this paper we discuss several examples of syntax-based mathematical algorithms, and we show how to specify them in a formal logic with undefinedness, quotation, and evaluation.
Multimedia QoE Modeling and Prediction

In this chapter, we focus on our research of multimedia QoE modeling and prediction. Firstly, we introduce two designed multimedia user complaint prediction algorithms: GMM-based oversampling algorithm and decision tree-based cost-sensitive algorithm. Subsequently, we discuss the proposed multimedia QoE modeling and prediction algorithms which are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively. Finally, we briefly describe the newly established multimedia QoE modeling and prediction based on broad learning system (BLS).
PEMBELAJARAN MENULIS KARANGAN DESKRIPSI DENGAN MODEL KOOPERATIF TIPE STAD TERHADAP SISWA KELAS X MAN 3 ACEH UTARA

The aim of the study was to determine the influence of the application of learning descriptive writing by using cooperative model of Student Teams Achievement Division (STAD) type. The research method used was experimental method, with a quasi-experimental design . This research was limited to the third year of class X students of MAN 3 in North Aceh District, while the sample of the study was the students of social class X2. The data analysis used in this study was t-test analysis by the Paired Samples T-Test model. As a result, the study showed that learning descriptive writing by using STAD type of cooperative model went well and provided better learning outcomes. It is proven by the average value of pre-test and post-test results. The results of pre-test test obtained an average value was 70.21, while the results of the post-test test obtained an average value was 87.79. Thus, there was a split about 17.58 between the results of pre-test and post-test. The Paired Sample t-test results showed significance of sig-0.00. This was less than a significant level of 0.05, meaning that H0 was rejected and Hi was accepted, and there was a positive influence on the application of learning descriptive writing by using STAD type of cooperative model.
Stage-oriented, Mixed Design Methodology for Image Processing Using VHDL and Python

The data analysis could be a very time consuming process during the hardware design on FPGA platforms. The verification process of designed modules is important at each step of a design process. In some cases standard methods for data analysis are insufficient, especially when the data representation are taken into assessment methods. In image processing systems based on FPGA there are various methods to support engineers in development of desired architecture. Some of them are based on scoping hardware signals in running device. It is also possible to scope signals in a simulation environment. In addition there are also high-level abstraction layer of data analysis methods based on Matlab, Python and similar tools. The unique image processing architecture developed by authors could not be upgraded with support of existing co-design methods. This is why stage-oriented, mixed design methodology was performed to support FPGA hardware development for faster prototyping and debugging with Vivado simulator tool and Python language. Presented approach was used to improve image processing design operating with ultra high resolution images (from 5 Mpix up to 70 Mpix).
New Star Detector and Descriptor for Deep-sky Image Live Registration

Recently, a new image registration application named live stacking appears in deep-sky imaging fields. This kind of live registration mainly handles translation and rotation caused by vibration of imaging devices and the Earth's rotation. The existing star-based registration methods mainly have two defects: highly parameter related and very complicated matching mechanism to cope with image rotation instead of using rotation invariant descriptor. In order to make up these defects, in this work, a live registration algorithm via star detection and description is proposed. Firstly, an automatic detection method based on Otsu, centralization and contour tracing is proposed to implement star detection. Then, a translation and rotation invariant binary descriptor is constructed to serve feature matching. Extensive experiments illustrate that our proposed method is feasible in deep-sky image live registration compared with these state-of-the-art feature-based methods used in computer vision fields.
A Brief Overview of Methodologies and Applications in Visual Internet of Things

Internet of Things is a widespread concept associated with devices equipped with sensors, connected to the Internet, and able to communicate to each other and with the end user. The ultimate goal of Internet of Things is to make people's lives easier, simpler and safer, and a number of applications that are steering Internet of Things in that direction already exist. This paper presents a brief overview of Internet of Things methodologies and applications that use visual data, i.e. where the main focus is on Internet of Things methodologies and applications where one of the primary sensors that is being used is a digital camera. This sub-field of Internet of Things is also known as Visual Internet of Things.
Research on Small Sample Target Detection Technology in Natural Scenes

In order to accurately detect the target in a small number of samples, this paper uses the traditional machine learning method and the migration learning method to detect under a small number of sample conditions in the natural scene. In the traditional machine learning method, we use the haar feature and the AdaBoost method to detect the target. In the migration learning method, we use convolutional neural network to quickly learn the characteristics of target objects in natural scenes, adopt fine-tuning, segmentation training and multi-scale combination strategies to enhance the learning ability of the network, and learn the target features as much as possible under small sample conditions. Experiments show that these two methods can effectively detect targets under small sample conditions.
Two-Person Interaction Recognition Based on Video Sparse Representation and Improved Spatio-Temporal Feature

In order to solve the problems of two-person interaction recognition, a novel human interaction recognition method was proposed. Firstly, a distance extreme value algorithm based on HOG feature description was proposed to extract the key frames of the video. That is the sparse representation of the action video in temporal dimension. Secondly, the algorithm based on active curve is used to obtain the sparse representation of the key frames of the video. That is the sparse representation of the video in spatial dimension. Thirdly, STIPs were selected to be spatio-temporal feature. The STIPs of the key frames are extracted based on the sparse representation of the videos as well. Finally, two algorithms are used to identify the interaction behavior, include the BOW model combing with the nearest neighbor classifier and the improved SRC algorithm. Experimental results show that the improved SRC algorithm has a better recognition rate for the sparse representation in videos.
A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.
Second Classroom Teaching of College English Based on “We Media”

The new network media provides great technical convenience for the existing college English teaching, but it also challenges the traditional teaching methods. Under the environment of “We Media”, it is necessary to relocate the second classroom activities of college English, extend foreign language teaching from the first classroom to the second classroom, integrate and promote the two kinds of teaching activities with the help of “We Media” technology. It will greatly promote students’ participation, enhance teachers’ sense of knowledge authority, then promote the development of college English teaching at the present stage from Multimedia to large-scale network, and further improve college students’ ability of knowledge cognition and foreign language autonomous learning.
Local Optical Flow Considering Object Boundaries by Adaptive Window Positioning

The amount of applications based on motion information has been growing rapidly, whereas accurate motion estimation is essential. Local optical flow methods, being typically based on the method of Lucas and Kanade, estimate the motion independently for each desired image position. Hereby, the mean squared error is minimized for a window symmetrically arranged around the considered position assuming constant motion within the whole window. However, this assumption is violated if the window contains objects of different motions. Furthermore, at object boundaries occlusions and disclosures may occur within the window. Consequently, large estimation errors are the result. In this paper, it is investigated if these disturbing influences can be reduced by windows, which are adaptively arranged avoiding object boundaries within them. This technique reduces the average absolute motion estimation error from 0.628 to 0.354 picture elements for the Middlebury and from 7.98 to 2.97 picture elements for the MPI Sintel dataset.
Detection of Divergence Point of the Optical Flow Vectors Considering to Gaze Point While Vehicle Cornering

This paper discusses on the relationship between a gaze direction and an optical flow and proposes a divergence point detection from optical flow vectors in a cornering behavior. Previously, we express optical flow radiates outward from the gaze center using the movie of the real vehicle. However, it is difficult to detect a divergence point corresponding with gaze position by applying vector analysis, because the acquired optical flow vectors considering with gaze position includes blank area and noise by matching error. Therefore, we propose the divergence point detection method based on particle swarm optimization. For verifying the proposed method, we perform the experiment using real driving movie. We discuss the effectivity and issue of the proposed method.
Stability control in virtual machine: Resource allocation for cloud computing

Cloud Computing becomes very popular after the growth of digital world take place, especially with the existence of smart phone, android box, smart watch, IR 4.0 and many more. Current researchers executed plenty of study on producing the algorithm to create an efficient VM resource allocation, but ignoring the aspect of the stability. In this study, it will be solving the issue of hosting a variety of application at one time by first step is, to execute the Dynamics VM allocation by applying a flexi threshold control limit on the fly, and then the next step is to do some controlling mechanism on the stability by applying the concept of process capability in the power Consumption.
High dynamic range image tone mapping technique based on the improved bitonic filter

The field of High dynamic range (HDR) imaging technique tries to solve the problem of capturing images and display in the devices, which are limited in dynamic range. Tone mapping technique approximates the appearance of an HDR image based on the logarithm of the luminance value. The tone mapping t echnique leads to detail loss, artifacts and higher computation time in local tone mapping. The aim of the research is to increase the performance of the tone mapping and to increases the image quality. The new technique is proposed such as Improved Bitonic Tone Mapping (IBTM) for efficient tone mapping. The weight factor is added to the bitonic filter and edges having high weight, when compared to the flat surface. The proposed method is compared with different tone mapping algorithms (Reinhard, Drago, Durand, Mantiuk, Pattanaik, Exposure and Gamma, Local adaptation, Equalize Histogram, Adaptive reference value) with quality. The experimental result shows that higher performance can be achieved by the proposed technique in a) preserving the quality of the image which is measured by PSNR and TMQM metrics and in b) execution time taken to generate the image measured in seconds.
Corporate Social Responsibility and Earnings Management: The Moderating Role of Family Ownership

This study aims to explore the impact of family ownership on the relationship among corporate social responsibility (CSR) and earning management (EM) in Pakistan. Data is collected from nonfinancial listed firms on Pakistan Stock Exchange (PSE) for the period 2009-2017. Our results of pooled ordinary least square regression indicate that CSR has significant negative impact on EM. Furthermore, results also indicate that association between CSR and EM is moderated by family ownership. Family firms which perform CSR activities are less involved in EM as compare to nonfamily firms perform CSR activities. This variation in behavior of EM in family and non-family firms can possibly be explained by socioemotional wealth theory. Keywords: Corporate Social Responsibility, Earnings Management, Family Ownership
Control Method Of perceptual Elements Based On Universal Neural Style Transfer

Universal style transfer has been implemented on the VGG network. Our work proposes control methods for universal style transfer. In general, the proposed methods consists of a texture segmentation method, and a method to control the weights of different layers in style transfer. Based on the results of texture segmentation, spatial control and background control can be realized. Increasing these control dimensions results in a more diverse result.
Study on the Difference of Lightning Impact on Intelligent Substation and Conventional Substation

Compared with conventional substations, smart substations use a large number of sensors, which leads to the impact of lightning on smart substations is significantly different from that of conventional substations. Firstly, this paper analyses the impact of lightning strike on intelligent substation from four aspects, which is obviously different from the impact of lightning strike on conventional substation. According to different situations, the mechanism of its generation is analyzed and elaborated, and the corresponding calculation methods and solving steps are given. This paper has a strong theoretical guidance for lightning analysis of intelligent substation.
Lambda: the ultimate sublanguage (experience report)

We describe our experience teaching an advanced typed functional programming course based around the use of System Fω as a programming language.
Supporting Instruction of Formulaic Sequences Using Videos at Scale

To help language learners achieve fluency, instructors often focus on teaching formulaic sequences (FS)--phrases such as idioms or phrasal verbs that are processed, stored, and retrieved holistically. Teaching FS effectively is challenging as it heavily involves instructors' intuition, prior knowledge, and manual efforts to identify a set of FSs with high utility. In this paper, we present FSIST, a tool that supports instructors for video-based instruction of FS. The core idea of FSIST is to utilize videos at scale to build a list of FSs along with videos that include example usages. To evaluate how FSIST can effectively support instructors, we conducted a user study with three English instructors. Results show that the browsing interactions provided in FSIST support instructors to efficiently find parts of videos that show example usages of FSs.-
Multimodal Fusion Architectures for Pedestrian Detection

Pedestrian detection provides a crucial functionality in many human-centric applications, such as video surveillance, urban scene analysis, and autonomous driving. Recently, multimodal pedestrian detection has received extensive attention since the fusion of complementary information captured by visible and infrared sensors enables robust human target detection under daytime and nighttime scenes. In this chapter, we systematically evaluate the performance of different multimodal fusion architectures in order to identify the optimal solutions for pedestrian detection. We made two important observations: (1) it is useful to combine the most commonly used concatenation fusion scheme with a global scene-aware mechanism to learn both human-related features and correlation between visible and thermal feature maps; (2) the two-stream segmentation supervision without multimodal fusion provides the most effective scheme to infuse segmentation information as supervision for learning human-related features. Based on these studies, we present a unified multimodal fusion framework for joint training of target detection and segmentation supervision which achieves the state-of-the-art multimodal pedestrian detection performance on the public KAIST benchmark dataset.
Sentiment Calculation of Chinese Microblog Based on Dependency Parsing Analysis

There are usually complex sentences in some Chinese micro-blog, the situation would decrease the accuracy of sentiment value. To solve this problem, sentiment calculation of microblog based on dependency parsing analysis is proposed. First, the dependence distance is presented to describe the relationship between sentiment word and its decorated words. The sentiment value of sentiment word pair is computed by using dependency syntax. Second, the sentiment value of sentence is obtained according to the sentence-type rule. The sentiment tendency value of micro-blog is obtained from the word level to the sentence level. Finally, the microblog are divided into positive, negative and neutral based on the threshold of the classification. Experiments demonstrate the effectiveness of the method.
Double Pyramid Field-aware Blind Deconvolution Framework

Blind deconvolution is a challenging problem of recovering a signal from their noisy convolution and it is prevalent in various fields including astronomical imaging, medical imaging, signal processing and computational optics. This paper discusses a more practical blind deconvolution problem which copes with the noisy convolution by spatially variant kernels. We found that the simple scheme used to participate the blurred images into regions for estimating spatially variant kernels is inaccurate. Otherwise, without using efficient kernel refinement and boundary condition, most of the current algorithms suffer more artifacts. In this paper, we proposed a double pyramid field-aware blind deconvolution framework. The proposed framework divides the given image to several patches by different field and estimate the coarse kernels of those patches by total variation-based algorithm. Then a new kernel refinement algorithm is proposed to refine the coarse kernels. After that, the non-blind deconvolution method with Hyper-Laplacian priors is used to get the deconvoluted whole image by their own refined kernels. Finally, the high-quality image is reconstructed by interpolating the several patch-kernel restored images together with the field-weighted interpolation method. Experiments illustrated that this framework can alleviate the restoration inaccuracy by single kernel, handle the unknown distortions to kernels and significantly improve visual quality.