Reviewerly/exHarmony huggingface dataset:
Spectral Normalization for Domain Adaptation

The transfer learning method is used to extend our existing model to more difficult scenarios, thereby accelerating the training process and improving learning performance. The conditional adversarial domain adaptation method proposed in 2018 is a particular type of transfer learning. It uses the domain discriminator to identify which images the extracted features belong to. The features are obtained from the feature extraction network. The stability of the domain discriminator directly affects the classification accuracy. Here, we propose a new algorithm to improve the predictive accuracy. First, we introduce the Lipschitz constraint condition into domain adaptation. If the constraint condition can be satisfied, the method will be stable. Second, we analyze how to make the gradient satisfy the condition, thereby deducing the modified gradient via the spectrum regularization method. The modified gradient is then used to update the parameter matrix. The proposed method is compared to the ResNet-50, deep adaptation network, domain adversarial neural network, joint adaptation network, and conditional domain adversarial network methods using the datasets that are found in Office-31, ImageCLEF-DA, and Office-Home. The simulations demonstrate that the proposed method has a better performance than other methods with respect to accuracy.
Non-linear Aggregation of Filters to Improve Image Denoising

We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.
Hiragana Handwriting Recognition Using Deep Neural Network Search

&lt;span id="orcid-id" class="orcid-id-https"&gt;These days there is a huge demand in “storing the information available in paper documents into a computer storage disk”. Digitizing manual filled forms lead to handwriting recognition, a process of translating handwriting into machine editable text. The main objective of this research is to to create an Android application able to recognize and predict the output of handwritten characters by training a neural network model. This research will implement deep neural network in recognizing handwritten text recognition especially to recognize digits, Latin / Alphabet and Hiragana, capture an image or choose the image from gallery to scan the handwritten text from the image, use the live camera to detect the handwritten text real – time without capturing an image and could copy the results of the output from the off-line recognition and share it to other platforms such as notes, Email, and social media. &lt;/span&gt;
An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features.
AI-Tutor: Generating Tailored Remedial Questions and Answers Based on Cognitive Diagnostic Assessment

Developing AI-powered intelligent tutoring systems to facilitate adaptive learning has been a hot research topic due to that these systems can provide personalized learning guidance by taking individual learner's status and needs into consideration to improve their learning performance. Based on this perspective, this paper proposes a novel personalized adaptive tutoring system named AI-Tutor, which not only incorporates the basic functions of general tutoring systems that providing adaptive course learning to learners, but also can generate tailored remedial questions and answers based on cognitive diagnostic assessment. This unique and tailored tutoring service will potentially help learners master the deficient knowledge points in a much shorter time. This paper presents the preliminary research and development in implementing such an AI-powered tutor. First, it gives the system design with the function description of main modules and the operating procedure. Second, it identifies four main research problems behind this system and proposes approaches to solve these problems in developing the AI-Tutor.
Super-Resolution reconstruction of Bone Micro-Structure Micro-CT Image based on Auto-Encoder Structure

Computed Tomography (CT) is widely used for screening, diagnostic and image-guided therapy for clinical and research purposes. The current Finite Element Analysis for Research Microstructure based on Micro-CT scan data has been considered the gold standard for non-invasive studies of bone microstructure. In medical diagnosis and treatment, the resolution of clinical medical images is low, and it is impossible to directly observe the fine structure of bone through Micro-CT. However, there are many problems in high resolution image, such as long scan time, high radiation dose and complicated processing steps. Therefore, the research goal of this paper is to propose a new network structure which can reconstruct high-resolution bone microstructure images with fine structure while reducing X-ray radiation. The network model proposed in this paper combines the auto-encoder structure and adopts a staged up-sampling strategy to accurately reconstruct high-resolution images from low-resolution images.Moreover, in order to be more realistic, this paper constructs a real training data set according to strict operation. In the data set of this paper, a variety of existing super-resolution reconstruction methods are used for comparison test. The results show that the network model proposed in this paper achieves better reconstruction results. We found in the experiment that the noise of the sample itself has a certain influence on the evaluation of the reconstruction effect and the impact of noise reduction is also discussed on super-resolution reconstruction.
Robot Control in Human Environment using Deep Reinforcement Learning and Convolutional Neural Network

Deep reinforcement learning (DRL) has been employed in numerous applications where complex decision-making is needed. Robot control in a human environment is an example. Such algorithm offers possibilities to achieve end-to-end training which learns from image directly. However, training on a physical robotic system under human environments using DRL is inefficient and even dangerous. Several recent works have used simulators for training models before implementing to physical robots. Although simulation provides efficiency to obtain DRL trained models, it poses challenges for the transformation from simulation to reality. Since a human environment is often cluttered, dynamic and complex, the policy trained with simulation images is not applicable for reality. Therefore, in this paper, we propose a DRL method to achieve end-to-end training in simulation, as well as to adapt to reality without any further finetune. Firstly, a Deep Deterministic Policy Gradient algorithm (DDPG) is employed to learn policy for robot control. Secondly, a pre-trained Convolutional Neural Network algorithm (CNN) is used to visually track the target in image. This technique provides the efficient and safe DRL training in simulation while offering robust application when a real robot is placed in dynamic human environment. Simulation and experiment are conducted for validation and can be seen in the attached video. The results have shown successful demonstration under various complex environments.
SECURED DATA AGGREGATION USING FIBONACCI NUMBERS AND UNICODE SYMBOLS FOR WSN 

Wireless Sensor Network is a combination of one and more nodes basically used for data collection and data aggregation.The collected data is aggregated through a data aggregator and it will be sent to bases station.While data transfer takes place, the intruders sniff the data that are being transmitted from one node to another.In order to prevent data sniffing by the intruder a study is made to prevent data sniffing and data breaching its boundaries.A two-fold cryptographic method is proposed using Fibonacci number and Unicode systems.The proposed method is two-fold viz, converting the normal simple text to secret message ii) convert secret message to Unicode text.The proposed method uses a two-fold security key encryption algorithm for transfer of data in the Wireless sensor networks and a decryption algorithm is also provided to convert back to its normal plain text.
IDE Interaction Support With Command Recommender Systems

Software developers' knowledge of integrated development environment (IDE) directly impacts on their productivity. IDE command recommender systems aim at identifying and convincingly presenting to software developers functionality that can help them to accomplish their daily tasks, without overloading them with well known or useless information. Command recommendation requires the estimation of both the utility of commands and the acceptance of the user for new command recommendations. In this paper, we focus on how and when such recommendations should be presented. We performed a long-term user study and our results show that IDE command recommendation must be presented with adequate descriptions of the commands and good usage examples. It seems that a higher frequency of recommendation notifications could be useful, but it should not be too intrusive, especially while developers are focusing on more demanding tasks. To improve recommendation acceptance rate, researchers should also focus on context-aware algorithms and tailor command recommendation timing.
Request Expressions in Japanese Language for Educational Purpose

This research analyzes request expressions in Japanese language in correlation with situational factor.Situational factor may be related to age, position and closeness level factors.The request expressions analyzed are conversations in the context of services, about how a request expression is said by a speaker (the one to give service) to hearer (the one to receive service).The objective of a request expression is to persuade the hearer to do what the speaker instructs.If the request language used does not comfort the hearer, it is likely that the objective of a request expression is not communicated.
The Teaching of Reading in Senior Secondary School

Reading English is an important skill that students need to learn. The teaching of reading in secondary school will help student to get a purpose for reading, focus on what they are learning, think actively as they read, monitor their comprehension, and review content and relate what they have learned to what they already know. In order to apply the strategies effectively in new instructional settings, teachers must have a clear understanding of the reading comprehension process and the natural reading strategies that commonly applied by our students. By knowing this, they can select and demonstrate appropriate reading strategies that their students need in reading the text.
The Correlation Between Blood Parameters as Early Detection on Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) in Children

Background: Dengue Shock Syndrome (DSS) is a severe manifestation of Dengue Hemorrhagic Fever (DHF) and affects more children and young adults. The age of susceptibility to DSS is 5-15 years old, due to an imperfect immune system. DHF patients with manifestations of DSS have propensity that can affect the platelet and hematocrit values as an initial laboratory parameter.&#x0D; Objective: This study is intended to find out the most effective platelet and hematocrit blood parameters and to find the correlation between platelet values and hematocrit on the incidence of DHF in Wirosaban Hospital of Yogyakarta Municipality in the period f March 2015 to July 2016. The data used were secondary data from medical records of pediatric patients aged 5-15 years old. Material and Methods: The research method used was to determine the correlation between platelet values and hematocrit on the incidence of DHF and DSS in children. Data analysis used chi-square to determine the correlation between platelet values and hematocrit on the incidence of DHF and DSS in children.&#x0D; Results and Discussion: In this study, the number of samples that fulfilled the inclusion and exclusion criteria was 74 on platelet examination while in the hematocrit examination there were 77 that met the criteria. The results of chi-square test for platelet examination obtained p value&gt; 0.05, which means there was no significant correlation between platelet counts and the incidence of DHF, DHF grade II, and DSS. The results of the chi-square test analysis on hematocrit examination obtained p &lt;0.05, which means that there was a significant correlation between hematocrit values and the incidence of DHF and DSS and it obtainedd r = 0.707.&#x0D; Conclusions: In this study the increase in hematocrit is one of the parameters to detect DHF with propensity to DSS.&#x0D; Bangladesh Journal of Medical Science Vol.19(2) 2020 p.273-277
Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization

Context-aware recommendation (CR) is the task of recommending relevant items by exploring the context information in online systems to alleviate the data sparsity issue of the user-item data. Prior methods mainly studied CR by document-based modeling approaches, that is, making recommendations by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they cannot effectively utilize contextual information of the documents, which results in a shallow understanding of the documents. Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. However, ConvMF only models the document content context from an item view and assumes users are independent and identically distributed (i.i.d). But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most. Moreover, users are more inclined to interact (buy or click) with the items that they have bought (or clicked). The relationships among items are also important factors that can impact the user’s final decision. Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item’s reviews, item’s relationships, user’s social influence, and user’s reviews in a unified framework. More specifically, to explore items’ relationships, we introduce a predefined item relation network into ConvMF by a shared item latent factor and propose a method called convolutional matrix factorization with item relations (CMF-I). To consider user’s social influence, we further integrate the user’s social network into CMF-I by sharing the user latent factor between user’s social network and user-item rating matrix, which can be treated as a regularization term to constrain the recommendation process. Finally, to model the document contextual information of user’s reviews, we exploit another CNN to learn user’s content representations and achieve our final model JCMF. We conduct extensive experiments on the real-world dataset from Yelp. The experimental results demonstrate the superiority of JCMF compared to several state-of-the-art methods in terms of root mean squared error (RMSE) and mean average error (MAE).
The Equipment Nameplate Dataset for Scene Text Detection and Recognition∗

In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly available on the website.
Advanced graphical-based security approach to handle hard AI problems based on visual security

Security is the main aspect to explore human data from different web oriented applications present in artificial intelligence (AI). It is very difficult to use different web applications without security to access data in various places. So that various types of security related approaches were introduced to use services in securely in outside environment, but they have some limitations to protect data from outside attackers (hackers). So that in this paper, we propose and introduce a novel and advanced security model to provide security from outside attackers in AI related web oriented applications. In this approach, we follow the basic features related to Captcha as a graphical password to enable security services in our proposed approach. Using Captcha graphical passwords in our approach, we describe pushing attacks, pass-on attacks and guessing attacks in web applications with random selection of Captcha passwords to use web services. Our experimental results show efficient security relations when compare to existing security approaches in terms of Captcha generation, time and other parameters present in web security applications.
Pelatihan Pembuatan Media Pembelajaran Berbasis AutoPlay bagi Kelompok Kerja Guru Kecamatan Geger di Kabupaten Bangkalan

Training on Creating AutoPlay Based Learning Media for Teacher Working Groups in Geger District, Bangkalan Regency&#x0D; Media becomes a tool that is needed by educators to create a pleasant atmosphere of a learning process. In the learning process, teachers should use interactive media as a medium for delivering information. Media in the learning process is intended to facilitate the teacher in conveying messages, information, or subject matter to students. This service program was aimed to provide mentoring to teachers in creating interactive learning media based on AutoPlay. This service program is carried out by two methods. The first was the public education method, namely training and mentoring for teachers in creating learning media with various applications, especially AutoPlay. This training method was an effort to educate and train partners to be able to use computers to produce exciting learning media. The second was the substitution of a science-and-technology method, which provided an overview of how to use technology in making learning media. The result of the implementation of this service was an increase in the knowledge and skills of teachers, reaching 90% of the 20 teachers who participated in the service program. On average, 80% of teachers were able to implement AutoPlay media in the learning process. The use of AutoPlay media in learning made students more motivated in learning.
Parallel Coordinates Based Visualization for High-Dimensional Data

High-dimensional data is generated in a wide range of scientific, social and economic life. Identifying and analyzing these high-dimensional data efficiently can improve the scientificity of decisions. Visualization can transform abstract data or large scale digital representation into an intuitive visible form, which will greatly enhance the insight of latent schema information for decision makers, and thus provide the basis for in-depth analysis and processing of high-dimensional data. This paper will explore a visualization framework to visualize the high-dimensional data through both visual angle and the intrinsic meaning of data. The rules behind data and information can thus clearly and intuitively be seen through data visualization.
AVBH: Asymmetric Learning to Hash with Variable Bit Encoding

Nearest neighbour search (NNS) is the core of large data retrieval. Learning to hash is an effective way to solve the problems by representing high-dimensional data into a compact binary code. However, existing learning to hash methods needs long bit encoding to ensure the accuracy of query, and long bit encoding brings large cost of storage, which severely restricts the long bit encoding in the application of big data. An asymmetric learning to hash with variable bit encoding algorithm (AVBH) is proposed to solve the problem. The AVBH hash algorithm uses two types of hash mapping functions to encode the dataset and the query set into different length bits. For datasets, the hash code frequencies of datasets after random Fourier feature encoding are statistically analysed. The hash code with high frequency is compressed into a longer coding representation, and the hash code with low frequency is compressed into a shorter coding representation. The query point is quantized to a long bit hash code and compared with the same length cascade concatenated data point. Experiments on public datasets show that the proposed algorithm effectively reduces the cost of storage and improves the accuracy of query.