Reviewerly/exHarmony huggingface dataset:
Distant Supervision Relation Extraction via Reinforcement Learning with Potential Energy Function

Distant supervision has become an essential method for relation extraction. Although distant supervision is very effective, there is a large amount of noise in the dataset produced by distant supervision. To filter out the noise in the dataset, most recent models try to handle it by reinforcement learning, which require waiting for all sentences in the bags to be selected when calculating the reward, resulting in large time consumption in model training. To solve the problem, this paper proposes a method called distant supervision relation extraction via Reinforcement Learning with Potential Energy Function (PEF-RL). The model calculates the semantic similarity of sentences and relations through the relational alias table, and we pass the semantic similarity of sentences and relations as an additional reward to the potential energy function to guide the decision making process of the instance selector during reinforcement learning, which improves the learning efficiency of the reinforcement learning relation extraction model. Experiments show that our model has significant improvements in several dimensions compared to existing models.
Spectral Characteristics of Solar Radiation in Timisoara, Romania

Abstract This study deals with the spectral distribution of solar radiation in Timisoara, Romania. Solar spectrum at the ground level was estimated based on Leckner’s spectral solar irradiance model and measured atmospheric parameters over the years 2019-2020. The average photon energy index (APE) was used to capture the characteristic signature of the solar radiation spectrum. The results emphasize considerable differences between the solar radiation spectrum in Timisoara and the standard AM1.5G spectrum. During 2019-2020, APE has taken values between 1.841 eV and 1.929 eV, indicating both red- and blue-shift from the standard AM1.5G spectrum. To our best knowledge this is the first study which discusses the signature of solar radiation spectrum in terms of APE for a location in Romania.
A Quantitative Analysis of Basic vs. Deep Learning-based Image Data Augmentation Techniques

Our proposed work is a research project that does quantitative analysis of various basic image manipulation techniques as processes for augmentation of image type data on the accuracy of deep learning task of hand-written digit recognition on MNIST dataset. The paper also presents a detailed comparison of various parameters such as computation burden, storage requirements for model storage, accuracy, and loss function value of the results obtained by using basic image manipulation techniques as image data augmentation techniques with those data augmentation mechanisms that are rooted in deep learning. The results that we have obtained on MNIST dataset without data augmentation applied are accuracy of 97.80% and loss of 0.320, whereas the highest accuracy was achieved by adjusting brightness as the data augmentation technique with 98.57% accuracy and 0.301 loss value. In the view of our results, we recommend that basic image manipulation-based data augmentation techniques must be used to address overfitting instead of memory or computationally expensive deep learning-based image augmentation techniques. This strategy also helps enhance the performance of various image data-based deep learning pipelines and makes these models more robust.
Flood Detection in Social Media Using Multimodal Fusion on Multilingual Dataset

Social media has emerged as a information source for natural disaster detection and assessment, such as flooding. Although visual features from images and textual information have been used for this purpose before, research shows that fusion of these two modes can further improve the detection and assessment. Moreover, due to advancement of social media, users now can write in these social media using their native language. Therefore, it is important to design a methodology that can handle multiliguality, specially for global natural disasters assessment such as flooding. In addition, social media contents, such as image and text, contain dissimilar credibility based on the nature of the media. Here, we propose a fusion methodology to fuse visual features and textual features using a novel machine learning approach, and test it in multiple language datasets, to detect flooding in three different types of social media. The results show that the proposed methodology fuses the two modes efficiently and improves the classification performance in all experimented languages.
SinBlowUp: Outpainting from Learning a Single Image

We propose an outpainting method that generates the outer region of an image only by learning the input image itself, without a training stage in advance. By modifying the previous SinGAN, we generate various outpainting images of the same style as the input image. We call the proposed method SinBlowUp and we showed that this can be used for both a photo-image and an art painting with various experimental results. Proposed method can be used to create exhibition contents for daily display in home appliances such as frame-type TVs or VR devices.
Enclave-Based Secure Programming with J<sub>E</sub>

Over the past few years, major hardware vendors have started offering processors that support Trusted Execution Environments (TEEs) allowing confidential computations over sensitive data on untrusted hosts. Unfortunately, developing applications that use TEEs remains challenging. Current solutions require using low-level languages (e.g., C/C&#x002B;&#x002B;) to handle the TEE management process manually &#x2013; a complex and error-prone task. Worse, the separation of the application into components that run inside and outside the TEE may lead to information leaks. In summary, TEEs are a powerful means to design secure applications, but there is still a long way to building secure software with TEEs alone.In this work, we present J <inf>E</inf>, a programming model for developing TEE-enabled applications where developers only need to annotate Java programs to define application-level security policies and run them securely inside enclaves.
Visualization Analysis of Meteorological Big Data through Deep Learning and Network Model

With the continuous progress of meteorological data observation technology, meteorological data showed explosive growth, which brought new opportunities and challenges to the development of meteorological forecasting technology. In view of the complexity of multidimensional visualization analysis of meteorological big data resources, the business object model, visual analysis dimension and visual monitoring index of meteorological big data resources are constructed. In this paper, RBM, CNN and network model based on long and short memory are used to predict the refined temperature in the next 24 hours, and the prediction results of the models are compared and analyzed. The results show that compared with traditional RBM, the deep learning algorithm model can show good prediction ability and can be used as a common structure for refined temperature prediction.
Decision of Line Structure beyond Junctions Using U-Net-Based CNN for Line Drawing Rendering

This paper introduces a U-Net-based neural network to determine line structure beyond junctions more accurately than the previous work [Guo et al. 2019]. In addition to the input of the previous work, we input 3D information to our neural network. We also propose a method to generate the training dataset automatically. The rendering results by the stylized line rendering [Uchida and Saito 2020] show that our neural network improves the streams of strokes.
A Comparative Analysis of Breast Cancer Diagnosis by Fusing Visual and Semantic Feature Descriptors

Computer-aided Diagnosis (CAD) systems have become a significant assistance tool, that are used to help identify abnormal/normal regions of interest in mammograms faster and more effectively than human readers. In this work, we propose a new approach for breast cancer identification of all type of lesions in digital mammograms by combining low-and high-level mammogram descriptors in a compact form. The proposed method consists of two major stages: Initially, a feature extraction process that utilizes two dimensional discrete transforms based on ART, Shapelets and textural representations based on Gabor filter banks, is used to extract low-level visual descriptors. To further improve our method's performance, the semantic information of each mammogram given by radiologists is encoded in a 16-bit length word high-level feature vector. All features are stored in a quaternion and fused using the L2 norm prior to their presentation to the classification module. For the classification task, each ROS is recognized using two different classification models, Ada Boost and Random Forest. The proposed method is evaluated on regions taken from the DDSM database. The results show that Ada Boost outperforms Random Forest in terms of accuracy (99.2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\pm 0.527)$</tex> against 93.78 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\pm 1.659))$</tex> , precision, recall and F-measure. Both classifiers achieve a mean accuracy of 33% and 38% higher than using only visual descriptors, showing that semantic information can indeed improve the diagnosis when it is combined with standard visual features.
RealShooting: Expanding the experience of point-and-click target shooting games

Shooting games underwent several transformations throughout their long history. However, the use of light guns in shooting games has not experienced significant development in the last decade. This article proposes a laser capture solution to achieve a new interaction with regular point and click shooting games. For that, we developed an intermediate solution that can adapt gameplay with no need to modify the original games. We used four different games to evaluate the proposed solution: two titles explicitly developed to test the model, and two commercially published third-party games. We tried different weapons (toys, airsoft, 3D printed etc.), and sowed that the obtained results were very satisfactory. No loss of performance was observed in the games - all the tested games ran stably at 60 FPS. Moreover, the players did not notice any lag between the gun shot and the corresponding mouse click performed by the system.
Nonlocal Regularized CNN for Inpainting Using Edge Prior

Image inpainting, filling missing regions of an image, is a crucial and hard task. Numerous existing works concerning generative adversarial network (GAN) have made outstanding performance, especially those methods that use image edge as prior information. However, better application of edge prior and long-distance correlation of deteriorated images are still open problems. In our paper, a nonlocal regularized convolutional neural network with edge prior is proposed. We fuse low-level features and high-level features of the edge image, which aims at providing more details such as lines and textures for image inpainting. Furthermore, we introduce a non-local block to enhance the correlation among the missing regions and existing regions to improve visual consistency. Experimental results are reported qualitatively and quantitatively to show that our proposed framework outperforms previous well-known ones.
Application Peculiarities of Deep Learning Methods in the Problem of Big Datasets Classification

The chapter proposes a new approach to estimate the quality of training datasets for convolution neural networks with deep learning. It is shown that the accuracy of image classification depends on the following parameters such as completeness, imbalance, uniformity of coverage of feature vectors, data compactness and particular class accuracy. A comparative assessment of the dependence of the training time of the model on the sample size of the proposed approach is carried out in comparison with VGG net, Alex net, CNN ensemble, Google net. It is established that the minimization of the Kulbak-Leibler distance allows forming the parameters of the training sample with much lower computational costs and a more compact representation of the feature space. This allows creating highly efficient information systems that carry out the process of classification of big data with high accuracy.
A Feature Extraction Method for Arabic Offline Handwritten Recognition System Using Naïve Bayes Classifier

Handwriting recognition in the Arabic language is considered one of the most challenging problems and the accuracies in recognizing still need more enhancements due to the Arabic character’s nature, cursive writing, style, and size of writing in contrast to working with other languages. In this paper, we propose a system for Arabic Offline Handwritten Character Recognition based on Naïve Bayes classifier (NB). Extraction features preceded by divided the image of character into three horizontal and vertical zones and 3x3 zones in one and two dimensions respectively, then classified by Naïve Bayes. The performance of the system proposes evaluated by using the benchmark CENPARMI database reached up to 97.05% accuracy rate. Experimental results confirm a high enhancement inaccuracy rate in comparison with other Arabic Optical Character Recognition systems.
RUN-BOT: mobile robot for running exercises

Abstract This article describes the operating principle of a mobile robot for running exercises RUN-BOT, shows a visual model, demonstrates photos of a testing prototype, describes the algorithm of the robot’s work on a real work-area. The functional model of the robot’s operation and a brief justification for the choice of electronic components are also described.
Multi-service Provisioning over Endogenous Secure Optical Transport Networks

This paper proposes a method for multi-service provisioning over the endogenous secure optical transport network (ESOTN), where the optical channel encryption unit (OEUk) is defined and added between the optical channel data unit (ODUk) and the optical channel transport unit (OTUk) to achieve multi-service secure provisioning over an ESOTN. The proposed method enables the self-generation and the self-consumption of secure keys, improving the efficiency of multi-service secure provisioning.
Design and Implementation of Gesture Recognition Rehabilitation System Based on Deep Learning

Gesture recognition is an important way of human-computer interaction. In view of the problems such as the single function of rehabilitation equipment, repetitive and boring movements in the training process, and slow recovery process, a gesture recognition rehabilitation system based on deep learning was proposed. Firstly, the key technologies of computer vision such as image acquisition, segmentation, smooth processing, classification and recognition are introduced. Secondly, the implementation details of gesture rehabilitation system are emphasized. Gesture sample data of people of different ages and genders were collected by camera to establish a rehabilitation gesture database. The convolutional neural network training model and PyQt graphical interface were used to build a rehabilitation system. The experimental results show that the system is reliable and accurate, which can not only improve the interest and enthusiasm of patients to rehabilitation training, but also is cheaper than other rehabilitation equipment and has a broader application prospect.
Mobile Computing in the Developing World

Nagapattinam is one of the main places for the catching and export of fish in Tamil Nadu State, in the south of India. In the local markets, fisherwomen buy fish, which are then sold in the streets as well as door-to-door. These “street selling fisherwomen” face various difficulties, including means of transport, carrying heavy fish baskets, and challenges in fish selling during sickness. To overcome these problems, a project was set up with support from the Tamil Nadu State Council for Science and Technology to develop a mobile app with native (Tamil) language to simplify the different processes involved in fish selling. Special features, like adding fish detail using images, voice-based searches on market conditions, and location tracking for the delivery of fish, are helping the fisherwomen to reduce the complexity of the selling process. This chapter provides detail on this project, examines the benefits, and discusses the difficulties encountered in the adoption of the mobile app, which is enabling fisherwomen to boost their revenues and improve their quality of life.
Clustering Users by exploiting Activity Tracks in Recommender Systems for SME

Personalized recommendation of products is an essential feature in any e-commerce service and is becoming more and more important for SME as well. The main problem is to be able to exploit limited amount of data concerning both user interactions and item availability. In the present paper, we introduce and evaluate some k-means based clustering strategies able to recognize different user categories even in presence of limited data. The ability to recognize users on the basis of their activity on the e-commerce site is fundamental to get insights about their preferences, in such a way that suitable products (or categories of products) can be recommended to them. We report on the experiments we have performed in the evaluation of three different clustering strategies, having the goal of grouping users showing similar behavior in interacting with items. We conclude that a standard k-means based on the Frobenius norm of the user matrix can provide good performances in terms of clustering users in the corresponding categories of interest.
Efficient Software Design for an Automatic Signal Measurement System with Path Planning

In order to measure the coverage of 5G signals in a room, we have developed a software system to control a mobile robot to carry a system for measuring signals and to navigate autonomously through the room according to the result of path planning. We have proposed a software development process utilizing a robot simulation system to validate the navigation of a mobile robot before deploying the software to the physical robot. Besides the development process, the main research topic presented in this paper is to produce an efficient route for the system to collect signal data while navigating through the room. Each room may have different layout, so the system first explores the room and builds a map. After building a map, a coverage path planning algorithm is executed. This paper presents the requirements of the signal measurement system and the path planning technique for meeting the requirements. For user interface, a web-based sytem provides the main functions for initial configuration, map bulding, measuring point setting, and navigation. The navigation system is implemented in the Robot Operating System (ROS) environment for various experiments.
VR technology is auxiliary to the mentality adjustment of piano performance

With the progress of science and instrument quality, piano performance and color are more varied. The piano performance, receiving and skills also developed with the development of piano art. Virtual Reality technology will also have a certain auxiliary role in the mentality adjustment of piano performance. In terms of such application, Oculus Rift DK2 will become an important auxiliary tool to help piano players achieve good adjustment of mentality. In this paper, the auxiliary role of VR technique in the regulation of the piano mentality.
Cyberphysical system of watering with remote control

In today's reality, the pace of people's lives is much higher than it was 30 years ago and it is still growing. At the same time, the amount of information also growing. This information should to be processed constantly, daily, as soon it is received. Production volumes are also not standing still. Such a lively pace of life requires process consistency and continuity. And these processes must be provided by man. This article describes the system of watering which should automate the process of growing plants. Also the analysis of a new branch, cyber-physical systems is carried out. The analysis of modern systems of autonomous irrigation, principles of their construction and organization of their work are committed. A method of implementing a system that provides the possibility of constant monitoring of the growing environment and provides an opportunity to influence it is suggested. The choice of components for system construction is made. The algorithm of the program operation is described. An analysis of the relationship between system components and the user's relationship with the system is performed.
Tutorial: Sandboxing (unsafe) C code with RLBox

RLBox is a C&#x002B;&#x002B; framework for building secure systems from untrusted libraries. RLBox uses a static type system to (1) abstract isolation mechanisms like WebAssembly (2) make data and control flow across the application-library boundary explicit and safe, and (3) help developers retrofit their application with sandboxing. In this tutorial, we first give an overview of RLBox and demonstrate how the RLBox framework helps sandbox a (buggy) C library in a simple C&#x002B;&#x002B; application. Then, we walk through the process of using RLBox to sandbox libraries in larger C&#x002B;&#x002B; codebases like the Firefox Web browser.
Promoting informal learning by integrating smartphones into the language classroom

Although smartphones have become normalised in people’s everyday behaviours, they remain under-exploited from a language learning perspective. This paper describes a study in an Irish university which explored the nature and extent of language learners’ existing use of smartphones for informal learning purposes through a survey, a case study, and a group interview. The results showed that firstly, smartphones played only a limited and tangential role in their language learning, and secondly, that learners had narrow perceptions of what ‘actual, proper study’ entails, demonstrated by their overwhelming preference for more traditional language learning resources and practices. The paper finishes by making suggestions regarding how smartphones can be integrated into the language classroom, at both third and second level, to help broaden learner perceptions of what language study is, and consequently, allow smartphones to play a more significant role in their learning practices.
Information propagation based on information value

On social networks, the spread of a piece of information may attract a lot of people’s attention. Some people who are not interested in the beginning may become interested and start to spread it after repeated contact. Similarly, those who are interested in the beginning tend to disappear after a while. In this study, a propagation model, in which infective’s and removals can transform form each other, is constructed. We introduce the LIF neuron model in neurology and propose a SEIRI information propagation model based on this model. In the process of a piece of information, its information value will change with the range and time of propagation. To reflect the change of the actual value of information in the process of communication, this paper puts forward the influence of the change of information value with time and the range of propagation. The experimental results show that the propagation process can be divided into initial stage, rapid propagation stage, and stable stage. The process of spreading different messages is different, but the overall trend is similar.