Reviewerly/exHarmony huggingface dataset:
Two-stage structure aware image inpainting based on generative adversarial networks

In recent years, the image inpainting technology based on deep learning has made remarkable progress, which can better complete the complex image inpainting task compared with traditional methods. However, most of the existing methods can not generate reasonable structure and fine texture details at the same time. To solve this problem, in this paper we propose a two-stage image inpainting method with structure awareness based on Generative Adversarial Networks, which divides the inpainting process into two sub tasks, namely, image structure generation and image content generation. In the former stage, the network generates the structural information of the missing area; while in the latter stage, the network uses this structural information as a prior, and combines the existing texture and color information to complete the image. Extensive experiments are conducted to evaluate the performance of our proposed method on Places2, CelebA and Paris Streetview datasets. The experimental results show the superior performance of the proposed method compared with other state-of-the-art methods qualitatively and quantitatively.
Smart Diseases Prediction System using Data Mining

This system is used to report information related to accident location and personal profile. Personal profile contains name, blood group, address, phone number of family members. So it will be easy to provide help and identify the victim.
Facial Expression Recognition In The Wild Using Bidirectional Convolutional Neural Network

The Static Facial Expressions In The Wild database (SFEW) contains unconstrained facial expressions close to the real world. In former research, current machine learning techniques are not robust enough for such an uncontrolled environment and it remains challenging nowadays. Coping with such task, we augment the state-of-art model which achieved the best performance for in the wild dataset and proposed two boosting algorithms of adding bidirectionality to convolution neural network based on the bidirectional neural network prototype, which is the first to integrate these two notions in literature. We also conducted experiments applying the decision fusion framework for classification, the proposed framework is trained simultaneously forward and backward, the final output is generated through voting mechanism. In this paper, two algorithms of adding bidirectionality to CNN are proposed, a framework for the facial expression recognition task (ensemble of HOG face detector and CNN with decision fusion and bidirectionality) is introduced and the classification result is listed, compared, and analyzed. The empirical results affirmed that the bidirectional boosting achieved good performance on the SFEW benchmark. Furthermore, some future works for precision improvement based on the existing deficiency of the current model are presented.
Extracurricular Reading for Children from Primary School Course in Bulgaria: Results of a Survey

The aim of the research is to obtain information about the current state of children's extracurricular reading in Bulgaria-basis for further development of information skills and competencies.The study refers to the reading practices and interests of primary school students (7 -11 years).It was implemented in the period March-June 2020 through electronic questionnaires filled out by the parents of 1169 students.The obtained data were processed with the software product SPSS, v19.A comparative analysis between the reading habits and preferences of children of different ages has been prepared.The followings are established: the thematic scope, the types of preferred readings, the motives for independent choice of reading, as well the preferred format, as the influence of the adults in the formation of the young reader.Particular attention is paid to reading in electronic format (e-books or other readings on the Internet).
Study on Energy Absorption Characteristics of 220 kV Cable End Arrester

Taking 220 kV cable as the research object, the influence of some parameters, such as grounding resistance, lead inductance, source impedance ratio, and rated voltage of arrester on the energy absorption characteristics of arrester is studied under the condition of single-phase grounding at the end, and the selection of energy absorption capacity of arrester is discussed. The results show that the energy absorbed by arrester is significantly affected by grounding resistance and rated voltage of arrester. At the same time, the calculation results also show that the lower the rated voltage, the more energy the arrester can bear, and the higher the requirement for its energy absorption capacity.
Navigation Path Estimation using UFIR Filter over G28U7FTTL GPS Receiver Data

A previously plotted path is an important requirement in real navigation of unmanned vehicles to avoid diversion or collisions. An important challenge is to obtain specific paths in small farmlands with the minimal economical investment. With this aim, the Global Position System (GPS) technology can be used that allows an object position determination on the entire Earth with highest precision. The cost of the GPS receiver is proportional to accuracy, which is susceptible to disturbances and noise. The GPS sampling time in commercial devices is one pulse-per-second that changes the measurement size per lapse according to the vehicle velocity. In this work we propose using low cost GPS receivers to obtain strategic points around a small farmland and build a high-resolution path. Using these points, a reference path is obtained using a cubic interpolation of GPS lapses and matching the length of samples. The iterative unbiased finite impulse response (UFIR) filter is applied to smooth the reference path. The results are compared to the Savitzky-Golay (S-G) filter-based estimates. It is shown that the two- and three-state UFIR filters produce an acceptable accuracy and outperform the S-G filter.
A Skewness Fitting Model for Noise Level Estimation and the Applications in Image Denoising

Abstract In this paper, a novel noise level estimation technique is presented based on skewness fitting. The difference between exact skewness and observed value on each channel contributes to an objective function. The skewness concentration is helpful to guarantee the reasonability of the model. The optimal solution means an estimation of the noise variance and it is easy to be applied in some parametric denoising model. The accuracy and efficiency of proposed algorithm were verified by the numerical experiments.
Blind image deblurring for a close scene under a 6-DOF motion path

Purpose How to model blind image deblurring that arises when a camera undergoes ego-motion while observing a static and close scene. In particular, this paper aims to detail how the blurry image can be restored under a sequence of the linear model of the point spread function (PSF) that are derived from the 6-degree of freedom (DOF) camera’s accurate path during the long exposure time. Design/methodology/approach There are two existing techniques, namely, an estimation of the PSF and a blind image deconvolution. Based on online and short-period inertial measurement unit (IMU) self-calibration, this motion path has discretized a sequence of the uniform speed of 3-DOF rectilinear motion, which unites with a 3-DOF rotational motion to form a discrete 6-DOF camera’s path. These PSFs are evaluated through the discrete path, then combine with a blurry image to restoration through deconvolution. Findings This paper describes to build a hardware attachment, which is composed of a consumer camera, an inexpensive IMU and a 3-DOF motion mechanism to the best of the knowledge, together with experimental results demonstrating its overall effectiveness. Originality/value First, the paper proposes that a high-precision 6-DOF motion platform periodically adjusts the speed of a three-axis rotational motion and a three-axis rectilinear motion in a short time to compensate the bias of the gyroscope and the accelerometer. Second, this paper establishes a model of 6-DOF motion and emphasizes on rotational motion, translational motion and scene depth motion. Third, this paper addresses a novel model of the discrete path that the motion during long exposure time is discretized at a uniform speed, then to estimate a sequence of PSFs.
Application of Augmented Reality in Campus Navigation

Augmented reality(AR) has the unique quality of providing a direct link between the physical reality and virtual information about that reality. In the application of a visitor visiting the campus with a smartphone, the location and pose information of the phone are acquired and calculated in real time. Then registration in 3D is realized by tracking the visitor's viewpoint and the phone post based on the result data. Also the location and orientation of the campus buildings relative to the visitor are identified. When the visitor is watching campus buildings, additional digital information appears to become part of the real world in the visitor's perception utilized by AR technology. As a result, the visitor not only appreciates the appearances and styles of the buildings directly, but also learn their history from the additional information which they can't intuitively understand.
Help ID car prowlers seen stealing military equipment from service member’s vehicle

Stealing from a service member of the military will never sit well with Washington's Most Wanted viewers
Research on college students’ satisfaction with their majors : ——Empirical analysis based on structural equation model

Taking some universities in Guangdong Province as an example, this article uses factor analysis to construct an index system of college students' satisfaction with their majors, and then uses the structural equation model to assign satisfaction weights. Through Amos software, a model of college students' satisfaction with their majors contains 5 latent variables. From the perspective of college students' satisfaction with their majors, this article puts forward some suggestions and measures for college talent training programs. The results show that the main factors that affect college students' satisfaction with their majors are: the degree of students' cognition of the major, satisfaction with learning resources, recognition of professional teachers' professional level, recognition of the school's teaching training plan, the compatibility of the evaluation of the future prospects of one's own profession. Among them, recognition of the school's teaching training plan is the primary factor affecting their satisfaction, followed by recognition of professional teachers' professional level, and the least impact is the degree of students' cognition of the major.
Research on Intelligent Algorithm for Regional Detection and Statistics of Sports Videos

Combining with the training needs of athletes, the region detection and characteristic statistics and analysis of video images are performed, and the whole idea of designing automatic key posture extraction is put forward in this paper. Aiming at the video of weightlifting training, an approach of target detection based on inter-frame difference accumulation is proposed. Firstly, the candidate foreground area is extracted from the shooter as an example to provide effective preprocessing for the subsequent motion statistical analysis, to reduce background interference and improve the running speed. Then, a weighted histogram based on amplitude intensity is defined to describe the degree of direction disorder of optical flow characteristics in the region. The entropy of the histogram is calculated and applied to motion video analysis. Finally, the statistic characteristics of the optical flow tracking results are analyzed, and the close relationship between the extreme point of the channel strength and the key attitude is proposed. The experimental results show that the intelligent algorithm has high accuracy and real-time, which also effectively solves the problem of frame similarity.
Flourishing Financial Operations Through Smarter Intelligence

Artificial Intelligence (AI) has penetrated every aspect of our lives in this era of Big Data. Nowadays application of AI has become very common. It is also playing a vital role in the field of finance. AI has been helping in every aspect of finance, from banking to investment. AI has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money. Especially during pandemic situations, AI is playing the most important role in the field of finance by helping society to do financial activities digitally more securely, accurately but in a very handy way using data mining operations. This paper will attempt to focus on the emerging applications of AI to optimize different financial services.
Automatic Question and Answer Generation from Text Using Neural Networks

Generating Questions is a major action in educational learning. Questions provide various stages of complication in the educational learning procedure. Knowledge building is costly for computer Assisted evaluation when setting exercise questions, teachers use test producers to build question Banks. The paper introduces a system that produces questions with answers automatically applying natural language processing and openNMT. Previous work question generation automatically, creates questions from sentences applying syntax and semantic parser. The paper presents a new approach for generating question and answer.
Bug Reproduction Based on EFSM Model

Bug reproduction is an important task for uncovering the causes of the bug and providing appropriate fixes. However, this task is usually labor-intensive and time-taking. Several solutions have been proposed to automate this task. The proposed solutions typically either use program runtime data, or bug stack traces to generate a test case that triggers the represented bug. In this paper, we propose a novel bug reproduction approach that combines the bug information and extended finite state machine model to generate effective test cases needed to reproduce a bug. The experimental results of 15 bugs in four common systems show that the proposed method can quickly and accurately reproduce bugs.
Defense Mechanism Against Adversarial Attacks Using Density-based Representation of Images

Adversarial examples are slightly modified inputs devised to cause erroneous inference of deep learning models. Protection against the intervention of adversarial examples is a fundamental issue that needs to be addressed before the wide adoption of deep-learning based intelligent systems. In this research, we utilize the method known as input recharacteri-zation to effectively eliminate the perturbations found in the adversarial examples. By converting images from the intensity domain into density-based representation using half toning operation, performance of the classifier can be properly maintained. With adversarial attacks generated using FGSM, I-FGSM, and PGD, the top-5 accuracy of the hybrid model can still achieve 80.97%, 78.77%, 81.56%, respectively. Although the accuracy has been slightly affected, the influence of adversarial examples is significantly discounted. The average improvement over existing input transform defense mechanisms is approximately 10%.
The Application in Handwritten Digit Recognition of Deep Belief Network Based on Improved Genetic Algorithm

The initial connection weight and threshold value of RBM in DBN have a certain influence on the recognition effect of the network. In this paper, the MNIST database is used as the data sample, and the improved genetic algorithm is applied to optimize the initial weight and threshold value in RBM, and the programming is realized by MATLAB language. The simulation results show that the improved DBN model is better than the traditional DBN network model and the traditional genetic algorithm improved DBN model in handwritten numeral recognition.
Analyzing Automatic Object Classification to Query-Based Image Search Engine

As human can easily differentiate between similar objects of different texture and color, making our machine equally intelligent as human is a difficult task. A machine in today’s time is doing excellent progress with textual data in terms of accuracy by applying several algorithms. The problem of getting good accuracy from image dataset is still not resolved with traditional technologies. This paper gives a brief survey for finding best algorithm suitable for developing query-based image search engine (QBIC) by using deep learning algorithm for object identification within complex images. QBIC system helps to find similar images by indexing and comparing the user query with images present in the database and retrieving the most important images on the top. The survey covers all the current research done related to similarity search and object classification.
Virtual Reality in the Treatment of Acrophobia

Exposure Therapy using Virtual Reality has been indicated as one of the most promising therapeutic approaches in the treatment of Acrophobia, because Virtual Reality generates exposure conditions systematic controlled and customized desensitization of the individual concerning the phobic incitement and the impulsive responses related to it, potentiating the emotional self-regulated competence and the incorporated cognitive restructuration.This quasi-experimental study had the purpose to analyze the impact of an exposure therapy program using Virtual Reality in the reduction of fear of heights on a sample of the Portuguese population. In this study participated 19 people with and without fear of heights, being distributed by the experimental and control groups. The program was composed of 8 sessions distributed biweekly, corresponding to a total program duration of 4 weeks. The impact of the program was analyzed through the administration of anxiety and acrophobia level assessment instruments and operationalization of psychophysiological procedures, focused on the biofeedback principles, before and after the intervention.The results from both self-report and psychophysiological data revealed a significant reduction in fear of heights in the participants exposed to the exposure therapy program, showing a positive impact of this protocol on the treatment of Acrophobia.
A Survey on Applying Automated Recognition of Touchscreen Stroke Gestures to Children’s Input

Abstract Gesture recognition algorithms help designers create intelligent user interfaces for a number of application areas. However, these recognition algorithms are usually designed to recognize the gestures of adults, not children, and as such they generally do not perform as well for children as adults. Recognition of younger children’s gestures is particularly poor when compared to recognition of older children’s and adults’ gestures. Researchers have begun to examine the aspects of children’s gesture articulation patterns that make recognition difficult. This paper extends the initial work examining child-specific recognition approaches by considering general-purpose approaches and how they might apply to the problem of recognizing children’s touchscreen gestures. This paper presents a survey of existing recognition and analysis techniques for gestures of both adults and children from a human-centered perspective, highlighting ways in which improved recognition can lead to a better experience for children using touchscreen gestures in a variety of contexts.
How to safely store a graphics card when not in use

You may wish to keep your Graphics card away for a few days, months, or years. The reason for storing the computer graphics card can be founded on ...
GSP Distributed Deep Learning Used for the Monitoring System

Monitoring system will collect massive data and store them in many distributed data centers. The traditional distributed deep learning needs to centralize data to the Parameter Server before training, then shuffle the data, and finally divide the data to each computing node uniformly. When training a neural network model with the data from monitoring system, it will result in huge communication overhead. We propose the Grouped-Based-on-Local-Data Synchronous Parallel (GSP) distributed deep learning. The use of local data can greatly reduce the amount of communication. At the same time, it can also bring another advantage, the similarity between the data centers can be calculated based on the background of monitor lens and the annotation of the data, then group computing nodes according to the similarity of the data centers they belong to and set up the grouped synchronous communication mechanism based on the Parameter Server architecture. The result of experiment shows that the iteration time of training by GSP is greatly reduced and the test precision is consistent.
Automatic detection of ironic expressions based on the Implicit Display Theory

It is not easy for a computer to automatically detect irony. Because in order to understand the irony in a sentence, it is necessary to infer the meaning hidden behind the word from the branch. Common sense and common understanding of reality are required as background knowledge for detecting irony. The literal meaning and the meaning intended by a speaker do not often match, and this makes it a difficult task in natural language processing. In this research, the effectiveness of automatic detection of irony based on the Implicit Display Theory was verified. The Implicit Display Theory was proposed in 1997 as a comprehensive irony theory, and its validity and superiority have been shown as a theory representing irony research. We selected ironic sentences for which the Implicit Display Theory holds from a large-scale ironic corpus, and performed two-class classification using the deep learning model to evaluate the application of implicit display theory to natural language processing.
Am I Playing Better Now?

G-SYNC technology matches formerly regular display refreshes to irregular frame updates, improving frame rates and interactive latency. In a previous study of gaming at the 30Hz frame rates common on consoles, players of Battlefield 4 were unable to discern when G-SYNC was in use, but scored higher with G-SYNC and were affected emotionally. We build on that study with the first examination of G-SYNC's effects at the 60Hz frame rate more common in PC gaming and on emerging consoles. Though G-SYNC's effects are less at 60Hz than they were at 30Hz, G-SYNC can still improve the performance of veteran players, particularly when games are challenging. G-SYNC's effects on emotion and experience were limited.
Augmented Reality: An Upcoming Digital Marketing Tool in India

Augmented reality (AR) is a technology that syndicates virtual objects with actual world enabling real-time interaction with virtual objects. AR is an upcoming technology in India which is now widely used by the marketers to attract customers. This research paper will examine the present uses of AR technology as a digital marketing tool in India along with the future scope of this technology. Based on secondary data, this paper will also examine the challenges faced by digital marketers in India to use AR technology. The aim of this paper is also to provide awareness about uses of AR in marketing.