Reviewerly/exHarmony huggingface dataset:
Dispelling the Blunt Perception of Social Technology

Participatory design prescribes intensive stakeholder involvement in the design process. One challenge in such projects is to enable stakeholders to develop an open mind for novel solutions of the design problem at hand. When designing social technology, this is further complicated by prejudices about technology as being too blunt and inadequate to interfere with the sensitivity of social context. In this paper, we present a novel approach that supports a more neutral and open discussion on the benefits and pitfalls of social technology. The approach helps stakeholders see social technology in a broader perspective, which in turn enables the design of solutions with improved social sensitivity.
Sentiment Analysis of Thai Sounds in Social Media Videos by using Support Vector Machine

Objective: To examine the sounds in Thai videos on YouTube, to analyze consumer opinion on beauty products.Statistical Analysis: The data was collected to analyze the sentiments of Thai sounds from 500 YouTube videos which were the reviews of beauty products; the length of each video being approximately 2-5 minutes.Findings: The accuracy rate of SVM (SVM) appears greater than those from the Naïve Bayes (NB) and K-Nearest Neighbor (KNN) techniques.The SVM used the RBF Kernel-typed Sequential Minimal Optimization (SMO) function, where c=50000 and gamma=0.1; the accuracy rate was 94.40%, when using K-fold Cross Validation, where K =10, that had 293 attributes.Application/Improvement: The SVM used the RBF Kernel-typed SMO function, where c=50000 and gamma=0.1; it can be applied to analysis of sentiments studies in social media derived from Thai videos which are evaluate processes need to be fast and able to provide negative, neutral, or positive results in a timely manner, in which it will become a purchase decision-making guide for consumers.
Rendering Thermal Sensation of Fingertip by Using Spatial Information of Heat Sources

To encourage interaction between a human and a robot, a technique of giving a thermal sensation is needed. In particular, the technique is important for the case of offering a sense of security such as nursing care robot and rehabilitation robot. Thermal sensation is perceived from the temporal and spatial information of heat sources. Therefore, the model included such information is needed to control multiple heat sources as a single system. In this paper, the fingerstall attached four heat sources was developed and its control method for rendering spatial thermal sensation is proposed. The four heat sources are attached to the copper plate, and the human finger is thermally stimulated by touching the plate. The temperature distribution on the copper plate is modeled based on a two- dimensional thermal diffusion equation, and spatial thermal sensation can be rendered using the proposed system. Some experiments were conducted to verify the proposed thermal system and its control system.
Face Reconstruction with Generative Adversarial Network

Generative Adversarial Network (GAN) is a framework of deep learning in generative models. The generative model aims to synthesize a new data so that it has a distribution of distribution according to the original data distribution. In the current development, GAN is not only used to synthesize data from noise alone, but in the current development it has begun to be used to translate data from a domain to data with a different domain. Several studies have been developed, such as CycleGAN, and Pix2pix. In this study, the face has not been used as an object of translation. In this study a model for translating images of face sketches into face images will be made.
End-to-end online handwriting signature verification

This paper describes an new method for online handwriting signatures verification. The algorithm is based on "Siamese" deep neural network. This network consists of two identical sub-networks joined at their outputs. During verification the two sub-networks extract features from two signatures, while the joining fully-connected network measures the distance between the two feature vectors to determine whether the signature is genuine. The most remarkable advantage of the system is that it can be trained end-to-end without any handcraft feature extraction except some necessary preprocessing. Experiments on the publicly dataset yielded the performance of 4.5% equal error rate (ERR).
Deep learning architecture advancements for accurate and robust image registration

Registration of image collections and video sequences is a critical component in algorithms designed to extract actionable intelligence from remotely sensed data. While methodologies for registration continue to evolve, the accuracy of alignment remains dependent on how well the approach tolerates changes in capture geometry, sensor characteristics, and scene content. Differences in imaging modality and field-of-view present additional challenges. Registration techniques have progressed from simple, global correlation-based algorithms, to higher-order model fitting using salient image features, to two-stage approaches leveraging high-fidelity sensor geometry, to new methods that exploit high-performance computing and convolutional neural networks (ConvNets). The latter offers important advantages by removing model assumptions and learning feature extraction directly through the minimization of a registration cost function. Deep learning approaches to image registration are still relatively unexplored for overhead imaging, and their ability to accommodate a large problem domain offers potential for several new developments. This work presents a new network architecture that improves accuracy and generalization capabilities over our modality-agnostic deep learning approach to registration that recently advanced the state of the art. A thoroughly tested ConvNet pyramid remains the core of our network approach, and has been optimized for registration and generalized to begin addressing derivative applications such as mosaic generation. Further modifications, such as objective function masking and reduced interpolation, have also been implemented to improve the overall registration process. As before, the trained network ingests image frames, applies a vector field, and returns a version of the input image that has been warped to the reference. Qualitative and quantitative performance of the new architecture is evaluated using several overhead still and full-motion video (FMV) data sets.
Neural Network Comparison for Paint Errors Classification for Automotive Industry in Compliance with Industry 4.0 Concept

The proposed paper focuses on utilization of neural networks for paint errors classification in the area of automotive industry. The paper utilizes hypothesis, that outdoor weather has significant impact on the number of paint errors, as a basis for comparison of neural network algorithms. For the neural network algorithms comparison we used real production data from the paint shop process. The paper deals also with definition of classification classes and attributes selection, as well as the data integration process itself that utilizes Hadoop platform as an intermediate data storage.
Algorithm for spectral-spatial remote sensing image super-resolution: multi-sensor case

Multi-sensor remote sensing image super-resolution aims to provide better characteristics for different types of resolution and compensate the limitations of the particular imaging systems. However, existing super-resolution techniques consider spectral and spatial resolution enhancement separately, i.e. only spatial or only spectral resolution can be enhanced. Among spatial super-resolution methods maximum a posteriori estimation approach with B-TV regularization stands out as one of the best method for spatial resolution enhancement. But existing implementations were designed only for RGB and grayscale photographic imagery. Unlike photographic RGB imagery, multispectral remote sensing images captured by optical sensors often contain more than three spectral channels (red, green and blue) and, moreover, different remote sensing systems produce a different spectral response for the similar spectral components. Therefore, a more complex image acquisition model should be regarded to take into account the variations in bandwidth and number of spectral channels in the case of remote sensing images. In this article, we propose an algorithm aiming to provide spectral-spatial multi-sensor remote sensing image super-resolution. We apply a joint spectral-spatial image acquisition model, that is typical for remote sensing systems, and investigate the super-resolution algorithm streaming from this model and the maximum a posteriori estimation approach with B-TV regularization. We propose a simple way to adapt B-TV regularization in the case of multiple spectral channels. Our experimental results confirm the enhancement in the spectral and spatial resolution of the output image in comparison with the input images. The results of our research demonstrate that the proposed method achieves both spectral and spatial super-resolution.
Teaching Assistant and Class Attendance Analysis Using Surveillance Camera

In order to reduce the time spent on class attendance in colleges and universities, an auxiliary sign-in technology based on surveillance cameras was investigated and designed. This paper proposes to use deep learning algorithm to detect and count current number of students and calculate the class attendance rate. Due to the insufficient classroom illumination, densely occluded faces and the blurred image, the system first performs light compensation on the input image, and uses the InceptionV1 convolutional network to generate multiple region proposal for the specific location of the faces, the Long Short-Term Memory is further used to resolve the region proposal to obtain a face frame with maximum confidence. Finally the class attendance statistic is calculated by counting the number of real face frames. Experimental results show that the proposed method significantly improves the detection speed, reducing the computational time from 119 ms to 50 ms per frame on the gtx1060, meanwhile, the detection accuracy is high and robust, which meets the requirements of real-time class attendance detection.
Discussions About Construction of Digital Campus in the Development of Higher Vocational Colleges

In higher vocational colleges, construction of digital campus system, which includes teaching, scientific researches, management, service, and many other functions, is one of the most important issues. We should focus on four aspects which are creating a new concept of construction, innovating a new mode of educating people, bringing birth a smart management mode and carrying out a development mechanism, and regard them not only as goals, but also as mainly means of achieving goals to achieve a new kind of smart digital campus.
Stitching video streams captured by multi-UAVs with stabilization

A novel video stitching algorithm is proposed which stitches video streams captured by multi-UAVs into panorama video streams. Each UAV captures one video streams, there are independent shakiness and parallax between each temporally aligned frames. A pure 2D homography does not have the capability to solve parallax like spatial-variant homographies, where each input video frame is sliced into grids and each grid is transformed with a local 2D homography. Video stitching has to consider video stabilization due to strong temporal correlations between frames. Video stitching and stabilization are combined, since they are both about putting the grids from video sources into panorama properly. Good stitching results are generated by optimizing the camera paths which describe the grid transformation between girds of frames before and after. In 4 aspects they are optimized: 1) spatially with the grids around, 2) temporally with the grid at the same place from before and after frames, 3) geometrically with the grids from another video, 4) reliably not too far from the original transformations. We focus on video stream processing which does not need the whole video frames, only 7 frames later than the input, state-of-the-art results are generated.
WebVOS-A WebGIS Application for Volunteer Observation Ships

This paper presents a WebGIS-based application WebVOS which supports management of volunteer observation ships. The design and architecture of WebVOS is fully described, which covers ship equipment, server and client. The server provides ship info, tile map, observation data collection and trajectory data services. Client is responsible for data requesting from server data services and display rendering. WebVOS supports commonly operations of volunteer observation ship management, which increases its availability. A real project is carried on to validate the application and result shows its availability.
Business Incubator as a Tool for Developing Regional Network Projects of Cooperation Between Schools and Businesses

The chapter describes the work experience of the Penza region (Russia) in support of innovative development in the region. A lot of work is being done in the region to involve young people in innovative technical and entrepreneurial activities. For this purpose, a comprehensive program has been developed in the region, in which schools and business enterprises, universities, and additional education centers cooperate. To coordinate this activity, a special department of youth innovation programs has been established on the basis of the business center. In particular, the objectives of the program include the support of projects of schoolchildren working in cooperation with specialists of enterprises – on the basis of the Centers for Youth Innovation Creativity, during STEM summer schools and other events organized in the region. The chapter describes the mechanisms for supporting techno-entrepreneurial projects of schoolchildren on the basis of a business incubator, and the conditions of the organization, the results, and effects of such activities are analyzed.
A Novel Probabilistic Appearance Model for Cigarette Detection Under Illumination Change

An effective appearance model is proposed for detecting cigarettes individually in a tightly packed bundle based on an intensity variance probabilistic model. Conventional image intensity threshold based segmentation is brittle and susceptible to environmental variation such as an illumination change. To mitigate this problem, a geometrical snow model is designed to describe the image intensity variances of an individual cigarette. It is then combined with a Gaussian function to generate a probabilistic model of a cigarette detector. The experimental results show that the proposed appearance model provides accurate detection performances and that it is robust to illumination change compared with other conventional methods.
Have Films Become More Visually Intense?

The purpose of this study was to test the hypothesis that motion pictures today are more visually intense than those of the past. We compute the visual intensity for a sample of 550 films over the last 100 years. Our results show that, on average, recent movies are 2.5 times as visually intense as those from the Golden Age of Hollywood. We also observed a substantial increase in all genres of film. Finally, we propose a simple five-point rating system for visual intensity as a guide for viewers.
An Efficient JPEG Steganalysis Model Based on Deep Learning

Convolutional neural networks (CNN) have gained an overwhelming advantage in many domains of pattern recognition. CNN’s excellent data learning ability and automatic feature extraction ability are urgently needed for image steganalysis research. However, the application of CNN in image steganalysis is still in its infancy, especially in the field of JPEG steganalysis. This paper presents an efficient CNN-based JPEG steganographic analysis model which is called JPEGCNN. According to the pixel neighborhood model, JPEGCNN calculates the pixel residual as a network input with a 3 × 3 kernel function. In this way, JPEGCNN not only solves the problem that direct analysis of DCT coefficients is greatly affected by image content, but also solves the problem that larger kernel functions such as 5 × 5 do not effectively capture neighborhood correlation changes. Compared with the JPEG steganographic analysis model HCNN proposed by the predecessors, JPEGCNN is a lightweight structure. The JPEGCNN training parameters are about 60,000, and the number of parameters is much lower than the number of parameters of the HCNN. At the same time of structural simplification, the simulation results show that JPEGCNN still maintains accuracy close to HCNN.
Let's Make Some Music

This paper proposes a new approach to represent music using a fixed time-step with a view to improve the quality of the music generated through recurrent neural networks. The proposed model architecture trains separate layers of chord and note data in parallel before combining the output of those layers to produce new music, ensuring both inputs are considered at all steps of generation. The music generated is evaluated using human evaluation technique and has been found effective at producing likeable music. The subjective paired comparison test revealed that 43% of participants could not distinguish between the original existing piece of music and new music generated using our proposed technique and 67% of all participants very much liked the generated music.
Uniform Resource Locator (URL) Detection Security System Based on Android

The use of the internet, which is currently increasing dramatically, certainly brings the convenience of finding information. The increase in internet usage also eventually gave rise to cybercrime crimes, one of which was by spreading a URL or fake site to steal someone's personal data. The research done is how to build an Android-based application that can detect the security of a URL. The goal is that internet users, especially social media, can avoid cybercrime crime that wants to steal personal data. Making an application uses the Regular Expression method to analyze each line of the Webpage Source Code in the URL based on 8 criteria taken from the World Wide Web Consortium (W3C). The application was then tested with 10 phishing-charged URLs and compared with Kaspersky, McAfee, and AdBlock applications. Based on the results of trials and comparisons, applications that have been made are able to detect 6 or 60% of the 10 URLs. Kaspersky and McAfee applications can detect 70%, while AdBlock only detects 3 or 30% of 10 URLs that contain phishing.
Convolutional Neural Networks Implemented by TensorFlow for the Segmentation of Left Ventricular MRI Images

Convolutional neuralHsu, Chih-Yu networks appliedTseng, Kuo-Kun to medical image segmentation with low-compute power computers are investigated. A convolutional neural network architecture is implemented by TensorFlow for a synthetic image and left ventricular MRI image segmentation. Finding parameters of a trained model is an optimization problem. A flowchart is proposed to solve the compute resource problem for the training procedure. The main point is to train a model by updating the checkpoint step by step. With the limitation of the low compute power, experiments can also have good enough results. The experiment results in image segmentation are good enough. The accuracy of both of them is above 95%.
Speech-Driven Facial Animation by LSTM-RNN for Communication Use

The goal of this research is developing a system that a rich facial animation can be used in communication is generated from only speech. A high-quality 3DCG character to be used in VR is quite popular. Also, the facial animation is important in enhancing the reality of the high-quality 3DCG character. Generally, a source of the generating facial animation is a camera. The problem is one of selecting optical cameras as an input. Using cameras as an input source, it causes limitations of the angle of view of the camera or problems that cannot be aware of the human face, depending on the orientation of the face. Therefore, it is reasonable for developing a system for generating a facial animation using only voice. In this study, we generate facial expressions from only speech using LSTM-RNN and showed the usefulness of the system in communication via user study.
An Expertise-enhanced Collaborative Filtering Method for Keywords Recommendation in Searching Engine Marketing

Nowadays, with the development of information technology, people like to use searching engine (SE) for help. Thus stores and businessmen start to realize the necessity of marketing through searching engine. Searching engine marketing (SEM) becomes a kind of new marketing tools, which can also be called advertisement campaign. SE business users select keywords in the SE system to attract customers searching of these. How to select the right keyword in SEM is a necessary problem needed to be solved recently for SE business users. Traditional keywords recommendation only recommend semantic similar keywords to business users which still makes the result set too large to choose. The new influential factor - expertise value combined with click rate will be integrated into the traditional collaborative filtering to minimize the keywords set waiting to be selected. The final recommendation is based on our expertise-enhanced collaborative filtering combined with semantic similarity.
Refining initial bounding-box for robust visual tracking

Most of the visual tracking algorithms are very sensitive to the initialized bounding-box of the tracking object, while, how to obtain a precise bounding-box in the first frame needs further research. In this paper, we propose an automatic algorithm to refine the references of the tracking object after a roughly selected bounding-box in the first frame. Based on the input rough location and scale information, the proposed algorithm exploits the region merger algorithm based on maximal similarity to segment the superpixel regions into foreground or background. In order to improve the segmentation effect, a feature clustering strategy is exploited to obtain reliable foreground label and background label and color histogram in HSI space is exploited to describe the superpixel feature. The final refinement bounding-box is the minimal enclosing rectangle of the foreground region. Extensive experiments are performed and the results indicate that the proposed algorithm can reliably refine the initial bounding-box relying only on the first frame information and improve the robustness of the tracking algorithms distinctively.
A new approach for vaginal microbial micrograph classification using convolutional neural network combined with decision-making tree (CNN-DMT)

Vaginitis, the most common disease of female genital tract infections, mainly relies on the morphological detection of the vaginal micro-ecological system to diagnose under the microscope. It affects women's normal life seriously, even their fertility. Since the morphological detection is very dependent on the experience of the observer, while the experienced doctors are mostly concentrated in large cities, the problem of diagnosis of vaginitis in rural women is extremely serious. Convolutional neural network (CNN), the typical algorithm of artificial intelligence, has shown great potential in many visual classification tasks. However, it is difficult to apply CNN method directly to the diagnosis of vaginitis. To solve the problem, this paper proposes an algorithm combining CNN with decision-making tree (CNNDMT) based on medical expert consensus. In a way of incorporating features automatically extracted by the machine and expert knowledge, automatic diagnosis of vaginitis disease is realized. Experimental results show that the CNN-DMT approach improves test accuracy by 8.46% over the leading CNN method, while enhancing the accuracy of normal bacterial flora by more than 15%.