Reviewerly/exHarmony huggingface dataset:
Comparative Assessment of Image Captioning Models

Image Captioning is the combination of Computer Vision and Natural Language Processing (NLP) in which simple sentences have been automatically generated describing the content of the image. This paper presents the comparative analysis of different models used for the generation of descriptive English captions for a given image. Feature extractions of the images are done using Convolutional Neural Networks (CNN). These features are then, passed onto Recurrent Neural Networks (RNN) or Long Short-term Memory (LSTM) to generate captions in English language. The evaluation metrics used to appraise the conduct of the models are BLEU score, CIDEr and METEOR.
Poster: a novel mechanism for rewards distribution in pool mining of proof of work

Proof of Work(PoW) as a critical consensus algorithm plays an essential role in Cryptocurrency fields. In PoW, computation power is the most vital resources. The security of a PoW system heavily relies on the computation power all honest users controlled. PoW systems distribute some rewards among participants according to their contributions as an incentive. Users who focus on earning such rewards are known as miners. Miners can join pools to share and reduce the variance of their rewards. In this work, we will compare different reward distribution mechanisms in a long-term condition. The result shows that all existed mechanisms cannot motivate miners continuously mining in the pool. For solving this flaw, a novel mechanism is proposed to improve the utility and motivation of miners.
From Inspection to State Control (Supervision) in the Field of Education: Stages of a Long Way

The article discusses the stages of formation and development of such forms of control over educational institutions in Russia as inspection and state control (supervision) in the field of education
A Semantic Expansion-Based Joint Model for Answer Ranking in Chinese Question Answering Systems

Answer ranking is one of essential steps in open domain question answering systems. The ranking of the retrieved answers directly affects user satisfaction. This paper proposes a new joint model for answer ranking by leveraging context semantic features, which balances both question-answer similarities and answer ranking scores. A publicly available dataset containing 40,000 Chinese questions and 369,919 corresponding answer passages from Sogou Lab is used for experiments. Evaluation on the joint model shows a Precison@1 of 72.6%, which outperforms the state-of-the-art baseline methods.
Fast Self-Organizing Maps Training

Self-organizing maps are an unsupervised machine learning technique that offers interpretable results by identifying topological properties in high-dimensional datasets and projecting them on a 2-dimensional grid. An important problem of self-organizing maps is the computational expensiveness of their training phase. In this paper, we propose a fast approach to train self-organizing maps. The approach consists of 2 steps. First, a small map identifies the most relevant areas from the entire high-dimensional input space. Then a larger map (initialized from the small one) is fine-tuned to further explore the local areas identified in the first step. The resulting map has performance (measured in terms of accuracy and quantization error) on par with self-organizing maps trained with the standard approach, but with a significantly reduced training time.
Data Accumulation System of Obstacle Avoidance Behavior on Bicycle Trip for Transportation Engineering

This paper proposes a data accumulation system of obstacle avoidance behavior on bicycle trips in videos using gesture based on deep learning-based features. In the field of transportation engineering, realization of image/video processing-based methods for analyzing of various situations in transportation scenes has been expected. Therefore, many videos taken by CCTV and dashcams are accumulated in various transportation organizations. However, since these accumulates only raw videos which are huge size, that transmission, accumulate device and analysis of accumulated videos are very high cost as social infrastructures. Therefore, this paper constructs a system for accumulating small-sized videos, extracted features and analyzed results of the transportation scene, which is the obstacle avoidance behavior on bicycle trips. This system is constructed on a small size computer which can be worked by using the battery in order to equip on various structures. Also, an algorithm for detecting obstacle avoidance behavior on bicycle trips in videos are realized by utilizing deep learning-based features and support vector machines.
Pedestrian Detection using Fuzzy Clustering and Histogram of Oriented Gradients

Pedestrian detection with complex scenes from a video is an challenging task due to illumination variations and dynamic background changes. In this context, this paper proposes a efficient approach of using fuzzy clustering and Histogram of Gradients for detecting humans. For this task, the paper first initializes a background frame where only static objects are present using fuzzy c-means clustering. Then a background subtraction and foreground detection algorithm (binary threshold) is proposed for detecting the moving objects. A contour is fetched for these foreground objects and passed through HOG classifier for pedestrian detection. The proposed method has been tested on various complex scenes from different data-sets.And it has presented better results over literature methods in terms of classification accuracy of 92%.
Femininities and Technologies

The role of gender in the design of technologies has been a topic of growing importance in fields such as interaction design, HCI, and games. Understanding that technology development and usage practices emerge within the cultural processes, the authors propose in this chapter a discussion about the notions of traditional femininity, its relation to video games, as well as new approaches to female representation. It is also assessed the cultural understanding of gender, sex, and sexuality, as well as how these notions may influence the players experience. The issues discussed and briefly analyzed here point to a production and regulation of gender by technologies such as video games. Therefore, the goal is to assess how gender notions and relations influence the design and use of games in terms of visuals, narrative and sociability.
A conceptual similarity and correlation discrimination method based on HowNet

The similarity and correlation analysis of word concepts has a wide range of applications in natural language processing, and has important research significance in information retrieval, text classification, data mining, and other application fields. This paper analyzes and summarizes the information of sememes relationship through the definition of words in HowNet and proposes a method to distinguish the similarity and correlation of words. Firstly, using a combination of the part of speech and sememes to distinguish the similarity and correlation between words concept. Secondly, the similarity and correlation calculation results between vocabulary concepts are used to further optimize the judgment results. Finally, the similarity and correlation distinction and discrimination between vocabulary concepts are realized. The experimental results show that the method reduces the complexity of the algorithm and greatly improves the work efficiency. The semantic similarity and correlation judgment results are more in line with the human intuitive experience and improve the accuracy of computer understanding of natural language. which provides an important theoretical basis for the development of natural language.
Spectrum data acquisition and modeling of materials based on compressed sensing

Based on the characteristics of multispectral color data (the uniqueness of multispectral data, which does not depend on specific media, application gamut, observer and other factors, and can achieve accurate color reproduction and reproduction), the research scheme of this paper uses compression sensing technology to achieve the acquisition of surface material data of complex objects, and combines multispectral data processing, analysis and other technologies to build multispectral data. Expression model of data.
The Study of Improvement Prediction Accuracy of the CNN Based on the Surface Correlation Degree Method(SCDM)

In this paper, a method for selecting neural network samples is proposed, which is to construct a sample training multivariate model by selecting a set of data points with high correlation degree. Choosing the right sample has a great influence on the prediction accuracy of the final model, and it is also an important task for forecasters. In this paper, the temperature anomaly of ocean subsurface is predicted by the Convolution Neural Network (CNN) model, and the CNN model uses the Surface Correlation Degree Method (SCDM) proposed in this paper and the data blank area zero padding method to select the sample training, so as to verify the feasibility of the method proposed in this paper. The final experimental results show that the model prediction ability trained by the method proposed in this paper surpass the zero padding method, and the prediction accuracy of Subsurface Temperature Anomaly (STA) in deeper seas is also higher. Therefore, the SCDM proposed in this paper can be an effective method to improve the prediction accuracy of the CNN.
Research and ICT Mediated Learning Styles

Taking into account technology goes ahead quickly and the teaching-learning processes for each person are different and the way to carry out this process should be at vanguard, Information and communications technology (ICT) play a role very important because they allow to create virtual objects for learning through software platforms, where they make knowledge is captured in a way more interactive, also making investigative process more dynamic and efficient. By an autonomous learning, a building critical thinking and well-structured forming purposeful, creative and innovative professionals who contribute suitable solutions to their environment. Besides all above, the professor also takes technological knowledge and developing new strategies and group dynamics, not only inside the classroom, but outside too, thus building a strong string between knowledge and working in site. Regarding above, it is made a study with a focus group to watch how ITC contributes to develop knowledge and investigative processes, concluding the students use these artefacts as a complementary tool to make their knowledge stronger.
An Overview of Quality of Service with Load Balancing in Cloud Computing Environment

The exponentially extensive and emerging expansion of the cloud computing network (CCN) and its prolific application result in an ample number of users/clients. The frugal superintend of the client’s requests for resources is troublesome due to a precise number of resources and to actualize the engrossment of Quality of Service (QOS). In aisle to achieve QOS, it is mandatory to maximize the efficiency and reliability of CNN resources. The zero-ground affair is to find an efficient load balancing (LB) algorithm (LBA) that obliged to parcel out data traffic amidst multitudinous resources. Here, in this paper, a multitudinous proposed scheme for QOS has been analyzed.
Automatic Color Image Segmentation Using Clustering Technique

K-Means and Fuzzy C-Means (FCM) algorithms are the most widely used centroid-based clustering algorithms. But, for both of the algorithms, the cluster count is to be specified in advance, which is a major drawback. This paper discusses the currently available methods for determining the optimal value of the cluster count, proposes three hybrid algorithms based on those methods coupled with Adaptive Particle Swarm Optimization and Fuzzy C-Means (APSOF) for the task of color image segmentation. These algorithms are evaluated using qualitative and quantitative analysis methods and the results are compared, stating the best performing hybrid algorithm.
Data Hiding Based on Adaptive Threshold Technique

This paper introduces a data hiding scheme consisting an adaptive threshold part based on difference expansion (DE) method. The threshold value is calculated based on image parameters such as, peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and payload capacity to obtain expected visual quality embedded image. Simulation verification using MATLAB 2015 for different test images are also presented. The simulation results clearly demonstrated that the data hiding scheme provides good quality of visual perception along with high value of payload.
A study on Kangokyokasho(韓語敎科書)

The purpose of this study is to look into the contents of Kangokyokasho (韓語敎科書) published in 1905 in Japan and to take its value into consideration as a research material in regards to the study of both the Korean and Japanese languages. During the Russo󰠏Japanese War, when the book was published, many kinds of Korean language textbooks were produced by Japanese scholars. The author of this book, Kaneshima Taisui(金島苔水), is known to have complied the highest number of Korean language books. As a result, the study confirmed that the explanation of the Korean language in this book provides valuable information in terms of Korean phonology and lexical history. In addition, the Japanese language in this book reflects the characteristics of modern Japanese during the Meiji period and is expected to be used as a resource in dealing with various problems in terms of Japanese grammar and vocabulary from a historical perspective.
Six Wheeled All Terrain Multipurpose Vehicle

This paper reflects the vision of designing a rover that can help people out in life threatening situations. The rover has both the mobility and ability to move on different terrain. The aim was to create a rover that would have 90-degree freedom on its front wheels having its own multi suspension it can move on to any sort of terrain like rough surfaces, broken roads, muddy terrain but most importantly can climb stairs. The rover is implemented with 6 wheels for its movement. The front 4 wheels acts as climbers while the other 2 rear wheels acts as support and holds the weight of the rover. The rover is programmed in such a way that allows the rover to turn and also letting the rover have superior maneuverability by allowing it to rotate 360° at one spot instantly. The rover being able to move on such harsh terrain will give the user an advantage to help out people in need from a distance without risking his/her own life. Practical usage of this rover could be by the fire fighters, military, bomb squad and many more. Due to making the rover light weight and cost effective PVC pipes has been used for constructing the base frame and structure.
A General Survey on Internet of Things based Smart Cities

Now a day’s Global World become a digitalized. Everything will be based on internet. The use of internet will become a part of people life. Evolution of cloud computing and IOT is increase with steady rate. All the devices we are using in our daily life are communicated with IOT. IOT is connected with Smart Homes, Industry, Smart cities, Education and many more. Apart from other one of the major hurdles in most cities is like solid waste management, Air Pollution, Traffic control, Water waste management and so on becomes an integral part of a smart city. Moreover, research survey on the IOT technologies especially for smart cities and the main components and features of a smart city. This paper give general information related smart cities, technology which use in smart cities and application where the IOT use in smart cities. Keywords : Internet of Things (IOT), Radio-Frequency Identification (RFID), smart city, Wireless Sensor Network (WSN) Cite this Article Himanshu Patel. A General Survey on Internet of Things based Smart Cities. Journal of Advances in Shell Programming. 2019; 6(2): 1–7p.
Credit for Data Reuse Is Driven by Making Data FAIR: The PARSEC Project Approach

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Credit for Data Reuse Is Driven by Making Data FAIR: The PARSEC Project ApproachAuthors Shelley Stall iD Alison Specht iD Margaret O’Brien iD Anne Cambon-Thomsen Laurence Mabile iD Yasuhiro Murayama Pedro Luiz Pizzigatti Corrêa iD Helen Glaves Helena Cousijn Eric Olson iDSee all authors Shelley StalliDCorresponding AuthorAmerican Geophysical UnioniDhttps://orcid.org/0000-0003-2926-8353view email addressThe email was not providedcopy email addressAlison SpechtiDUniversity of QueenslandiDhttps://orcid.org/0000-0002-2623-0854view email addressThe email was not providedcopy email addressMargaret O’BrieniDUniversity of California Santa BarbaraiDhttps://orcid.org/0000-0002-1693-8322view email addressThe email was not providedcopy email addressAnne Cambon-ThomsenUniversity Paul Sabatier Toulouse IIIview email addressThe email was not providedcopy email addressLaurence MabileiDUniversity Paul Sabatier Toulouse IIIiDhttps://orcid.org/0000-0002-7724-1721view email addressThe email was not providedcopy email addressYasuhiro MurayamaNational Institute of Information and Communications Technologyview email addressThe email was not providedcopy email addressPedro Luiz Pizzigatti CorrêaiDUniversity of São PauloiDhttps://orcid.org/0000-0002-8743-4244view email addressThe email was not providedcopy email addressHelen GlavesBritish Geological Surveyview email addressThe email was not providedcopy email addressHelena CousijnDataCiteview email addressThe email was not providedcopy email addressEric OlsoniDCenter for Open ScienceiDhttps://orcid.org/0000-0002-5989-8244view email addressThe email was not providedcopy email address
Quality Models: An Experience in the Software Industry

At the moment in the software development companies, teams have been created to guarantee the quality of their products, applying methodologies that have resulted in successful companies, proposing to copy the model and replicate it in your organization strictly in some cases, or trying to adapt it to the reality of your company, into others. In this article, the implementation is analyzed in a quality “live model”, a Quality Management System (QMS) comprehensive and based on the ISO 9001: 2015 “Quality Management Systems” Standard, with emphasis on “Projects Management” and “Service Management” processes of a medium-sized company dedicated to software development. The application of this model called SPF (System Process Framework) implied, among other activities, the exact definition of the areas, procedures and tasks involved, determination of roles and functions of each member of the organization, implementation of technological tools, in general, activities that allowed evaluating indicators that are part of the methodology, tailored solution that is continually adapting and improving “in hot”. By virtue of this, this article intends to present the results of the application of this “live model”, a concept based on the agile project management methodologies, this time applied to the quality assurance of the software product, with the objective that this experience, with its conclusions, be an input when choosing a quality model in the software development industry.
Real-Time Crowd Detection and Surveillance System Using an Arduino Based Flight Controller for Quadcopters

This paper is dedicated to implementing a feedback control system for a UAV equipped with inertial sensors with a focus on Vertical Take-off and Landing and detect the number of people in a given frame using machine learning. The flight controller board has a feedback PID (Proportional-Integral-Derivative) algorithm code burned onto it. The PID controller has an initial set point and the readings from the gyroscope and accelerometer are continuously fed back to the PID. The readings that indicate the inclination along any arm of quadcopter will generate an error after comparing with the initial point and then PID will generate a corresponding output to eliminate the error and balance the quadcopter. Finally, the camera transmits the video to the base station (Laptop) and the machine learning algorithm in the laptop gives the number of people in a frame with heat maps and surveillance video.
Coarse-to-fine fundamental matrix estimation by line correspondence based on the motion barcode

Estimating the fundamental matrix is one of the important techniques for 3D reconstruction using the images. For this purpose, a method using image sequences captured by cameras based on the correspondences of the lines which are randomly generated in each image was proposed. In this paper, we propose a coarse-to-fine method for fundamental matrix estimation using line correspondences based on the motion barcode to improve the accuracy. First, in the coarse step, our method generates the limited number of lines in each images and estimates the epipole in each image. Then, in the fine step, the lines passing near the epipole estimated in the coarse step are generated in each image near the lines selected in the coarse step and the fundamental matrix is calculated. In the both steps, the corresponding pairs of lines are selected based on the similarity of the motion barcodes of the lines and color around the moving objects on the lines.
Fuel Tank Dynamic Strain Measurement Using Computer Vision Analysis

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Stress and strain measurement of high density polyethylene (HDPE) fuel tanks under dynamic loading is challenging. Motion tracking combined with computer vision was employed to evaluate the strain in an HDPE fuel tank being dynamically loaded with a crash pulse. Traditional testing methods such as strain gages are limited to the small strain elastic region and HDPE testing may exceed the range of the strain gage. In addition, strain gages are limited to a localized area and are not able to measure the deformation and strain across a discontinuity such as a pinch seam. Other methods such as shape tape may not have the response time needed for a dynamic event. Motion tracking data analysis was performed by tracking the motion of specified points on a fuel tank during a dynamic test. An HDPE fuel tank was mounted to a vehicle section and a sled test was performed using a Seattle sled to simulate a high deltaV crash. Multiple target markers were placed on the fuel tank. The motion of these markers was captured using high speed video cameras. The high speed videos were processed using the OpenCV computer vision library. Using OpenCV, the high speed videos were imported, and the position of the central location of each target marker was extracted frame by frame from the high speed videos. Once the position was known, the strain was computed using the change in relative position between two marker positions. Results of the testing showed that the acceleration-induced strain is low, generally less than the material yield strain. It was noted that reliable and accurate results require that the camera be placed normal to, or at a shallow angle to, the points being tracked. In addition, curved surfaces lead to limited fidelity of strain data due to the varying focal length of the points being tracked and measurement increased sensitivity. This method is similar to a “typical” tensile test in which displacement is tracked between two pre-established points on a sample. As such, the methodology was replicated on a tensile specimen to validate the methodology.&lt;/div&gt;&lt;/div&gt;