Reviewerly/exHarmony huggingface dataset:
Implementation of architecture LeNet – 5 for Javanese scripts handwriting recognition

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dewi Rosmala, Muhamad Rizky Rifaldy; Implementation of architecture LeNet – 5 for Javanese scripts handwriting recognition. AIP Conf. Proc. 24 February 2023; 2772 (1): 040007. https://doi.org/10.1063/5.0124461 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
End-to-end unsupervised clustering neural networks for image clustering

&lt;p&gt;In this paper, we propose a new clustering module that can be trained jointly with existing neural network layers. Specifically, we have designed a generic clustering module with a competitive update mechanism. The module consists of a Gaussian unit and a maximum pooling layer. The Gaussian unit forward propagation conforms to the joint Gaussian distribution and contains two sets of trainable parameters. It requires no tedious setup and has a plug-and-play feature. To improve the representation capability of the network, we used an auto-encoder to extract the hidden semantics of the input features and combined the clustering module with the auto-encoder to construct an end-to-end unsupervised clustering neural network. We refer to this as HGL_CAE(High-dimensional Gaussian distribution layers combined with convolutional autoencoders).The network is highly adaptable to different input feature dimensions and can cope with situations where the number of clusters cannot be determined in advance. We conducted experiments on the MNIST dataset and the Fashion_MNIST dataset with a clustering accuracy of 93.38% and 72.83% respectively. It is highly competitive with existing methods. &lt;/p&gt;
Human Activity Recognition using Deep Learning

Human activity recognition (HAR) is a difficult task that entails recognizing and categorizing human actions using sensor data gathered by mobile phones and wearable. Because of recent developments in deep learning, deep neural networks (DNNs) have surpassed other methods as the gold standard for HAR. In this study, we provide a high-level introduction to HAR by making use of deep learning methods. We begin with an overview of the HAR problem and its obstacles, such as the inherent uncertainty of human behaviour and the enormous dimensionality of sensor data. Then, we talk about how deep learning plays a part in HAR and provide a thorough assessment of recent studies that have used several kinds of deep learning architectures, such as CNNs, RNNs, and hybrid architectures, to solve HAR problems. We also discuss some of the important issues and on-going research questions in HAR utilizing deep learning, including how to handle imbalanced datasets, how to interpret DNN models, and how to cope with the sparsely and variability of sensor data. We wrap up by talking about where this type of deep learning-based HAR could go in the future, including applications like personalised care monitoring, activity- based intelligent assistant, and smart home automation.
Deliverable D12.9 Data Management Plan

The main goal of the BiCIKL project is to improve, for the ﬁrst time, seamless access, linking and usage tracking of data within a network of Research Infrastructures managing diﬀerent data classes (literature, specimens, samples, occurrences, sequences, taxon names and Operational Taxonomic Units (OTU)), ultimately represented also in a biodiversity knowledge graph. To achieve this, the consortium members will operate with huge amount of data during and after the end of the project. As a Horizon 2020 project, BiCIKL conforms to the Open Research Data Pilot (ORDP)1 and Article 29.3 of the H2020 Model Grant Agreement by default, hence the consortium aims to improve and maximise access, sharing, linking and reuse of FAIR Open Research Data (ORD), generated or managed by the project. A detailed Data Management Plan is a critical part of the ORDP. The DMP described in the present document is developed in BiCIKL within the ﬁrst six months of the project and it will evolve as a “ living document ” during the lifetime of the project and beyond in order to present the status of the project's reﬂections on data management. The BiCIKL DMP outlines the handling of research data and provides the basis of the project consortium’s data management life cycle for the data collected, generated and processed by the participants in the project. The DMP also covers the methodologies and standards previously developed for data sharing and open access, curation and preservation. The subject of the DMP is the management of research data. Personal data management is covered by deliverable D9.1 Protection of Personal Data . The BiCIKL DMP was developed in close collaboration with all project partners and involved Research Infrastructures (RI) who provided information on their data management practices and policies in a questionnaire and planned generation, collection, and processing of data for the purposes of building a resilient data management strategy of the project which meets all criteria for open research. This DMP aims to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) data management criteria of Horizon 20202.
The Modification of the Quantum-Resistant AJPS-1 Cryptographic Primitive

&#x0D; &#x0D; &#x0D; In recent years, quantum-resistant cryptography has been steadily developing, which is due, in particular, to the post-quantum cryptosystems competition of the National Institute of Standards and Technology (NIST), which has been ongoing since 2017. One of the participants in the first round of the competition is the AJPS cryptosystem. In this work, we propose the modification of the AJPS cryptosystem for bit-by-bit encryption by changing the numbers class used in the cryptosystem as a module. This modification increases the variability of the cryptosystem parameters.&#x0D; &#x0D; &#x0D;
On the Security of Qalqan Cipher Against Differential Cryptanalysis

&#x0D; &#x0D; &#x0D; In 2021, the first version of block cipher Qalqan was presented. It is positioned as a candidate to the future national encryption standard of the Republic of Kazakhstan. This cipher features the usage of addition by different modules for mixing the round keys and for linear layer. In this work, we consider some cryptographic properties of Qalqan, related with the security against differential and linear cryptanalysis. We present variations of cipher’s S-box with better cryptographic properties. We prove that branch number of Qalqan’s linear layer is equal to 3, and the layer itself has a significant amount of fixed points. Also, we build a set of multi-round differential characteristics with high probabilities for the modified version of the Qalqan cipher, which uses only addition modulo 256. With these results, we can argue that the declared security of Qalqan against differential and linear cryptanalysis should be reconsidered.&#x0D; &#x0D; &#x0D;
The Relationship Between Reading And Writing Activities Carried Out By Parents In Pre Primary Education And Their Children's Achievement For Those Skills

The current study aimed to investigate the degree to which parents in the Kingdom of Saudi Arabia practice reading and writing activities in the pre-school stage with their children to develop their reading and writing skills in the general education stages. In addition, studying the impact of these activities on students’ achievement in reading and writing skills when they reach the fourth grade of primary school. The data of Saudi Arabia was published by IEA, it was based on PIRLS 2016. The questionnaire of the student’s guardians in the study included a scale containing several reading and writing activities for the pre-primary stage to determine the extent to which parents practice them. Using multiple linear regression analysis, the results showed that building cubes and playing with shapes are among the most important activities that positively affect reading and writing skills. In addition, followed by reading stories and writing letters of words as another set of important activities, which parents provide to their children before they enter school. The study recommended the necessity of implementing developmental programs for parents to show the importance of their participation in developing children's reading and writing skills, as well as providing courses to teach them effective strategies that contribute to developing children’s reading and writing skills.
Modern Code Reviews—Survey of Literature and Practice

Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist, it is u a nknown whether the research community has targeted themes that practitioners consider important. Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners’ opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues. Method: We conducted a systematic mapping study to survey state of the art until and including 2021, employed the Q-Methodology to analyze the practitioners’ perception of the relevance of MCR research, and analyzed the primary studies’ research impact. Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1,300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor– and support systems–related research. Conclusion: These results indicate a misalignment between the state of the art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda that can potentially increase the impact of MCR research.
Gaze estimation using Convolutional Neural Networks

Abstract Numerous investigations on gaze estimate techniques for analyzing human behavior have been made in recent years. The majority of which have focused on gaze tracking techniques. This article proposes a new method for gaze estimation. The proposed system is divided into three phases: (i) Estimation of head position using Con-volutional neural networks CNN (VGG16, Resnet50, InceptionV3), (ii) Detection of eyes area using Viola Jones’ algorithm, and in phase (iii) gaze estimation using three different models: pre-trained CNN, CNN from scratch, as well as Bilinear Convolutional Neural Networks (B-CNNs). Columbia gaze database is used in the validation experiments. When compared to earlier efforts, the experimental results demonstrate that the proposed method produces a more exact outcome.
Improving Average and Median Filters

Average and median filters are the most popular filters used to reduce the negative effects of salt and pepper noise affected gray and color images, these filters work good when the noise affected the image has a small noise ratio. Average and median filters has some disadvantages, which are negatively affect the quality of the denoised image, they treat all the pixels regardless the pixel is a clean pixel or a noisy pixel. In this paper research an improvements will be added to the two filters making them capable to handle salt and pepper noise with high noise ratios. The improvements will be based on using a special mask and special logical index matrices to detect and process only noisy pixels. The improved modified filters will be implemented using noisy images with various noise ratios, the obtained quality factor of the modified filters will be compared with the results of using average and median filters to prove the enhancements provided by the proposed filters.
THE IMPLEMENTATION OF ONLINE NEWSPAPER IN TEACHING READING

This study was conducted at SMP Plus Al Madaniyah. The participants of this study were seventh grade students of SMP Plus Al Madaniyah..The data were obtained through classroom observation and interview. It was found that through the activity, the students not only using the language, but also reading the language and speaking with the language.The last, the final observation. It was found that the students were able to rewrite the news with their own words.The last, the final observation. It was found that the students were able to rewrite the news with their own words. The lessons observed were reading lessons. The result of interview found some benefits from the use of online newspaper in teaching reading. The benefits attained the development of students’ reading ability, fun learning’s atmosphere and the increasing of students’ reading interest.
STUDENT'S SPEAKING SKILL IN ENGLISH MATHEMATICS COURSES WITH MNEMONIC METHOD

From the experience of the researchers while teaching the English Mathematics course, it was found that the main obstacle faced by students in communicating in English lies in their minimal vocabulary of English mathematical concepts. Departing from this fact, this study has two objectives, namely to find out how students respond to the application of the mnemonic method in English mathematics courses and the second is to find out students' speaking abilities, especially in remembering new vocabulary obtained from the learned mathematical concepts. Based on research data both in the form of written tests and interviews, it can be concluded that overall it is of good value. Student responses to the application of the mnemonic method in Mathematics English courses on the topic of Statistics and Opportunity are positive. Meanwhile, the students' English speaking ability in the application of the mnemonic method on the topic of Statistics and Opportunity includes 5 aspects as follows: for grammar mastery, a score of 20% of students obtained 3 points or very good category was obtained, for the vocabulary aspect, a score of 64% was obtained, which received a very good score. good, 48% who scored very well also in the comprehension mastery aspect. As for the fluency and pronunciation aspects, 40% were in the good category and 48% were in the very good category, respectively.
A Computational Meta-Learning Inspired Model for Sketch-based Video Retrieval

Objectives: To design and develop an efficient computing framework for sketch-based video retrieval using fine-grained intrinsic computational approach.Methods: The primary method of sketch-based video retrieval adopts multi-stream multi-modality of joint embedding method for improved P-SBVR from improved fine-grained KTH and TSF related dataset.It considers the potential aspects of the computation of significant visual intrinsic appearance details for sketch objects.The extracted appearance and motion-based features are used to train three different CNN baselines under strong and weak supervision.The system also implements a meta-learning model for different supervised settings to attain better performance of sketch-based video retrieval along with a relational module to overcome the problem of overfitting.Findings: The study derives specific sketch sequences from its formulated dataset to compute instance-level query processing for video retrieval.Further, it also addresses the limitations arising in the context of coarse-grained video retrieval models and sketch-based still image retrieval.The aggregated dataset for rich annotation assisted in the experimental simulation.The experimental evaluation with respect to the performance metric evaluates the 3D CNN baselines under strong supervision and weak-supervision where CNN BL-Type-2 attains maximum video retrieval accuracy of 99.96% for triplet grading feature under relational schema.CNN BL-Type-1 attains maximum retrieval accuracy of 97.40% considering the triplet grading features from the improved SBVR.The evaluation metric for the instance level retrieval process also considers true matching of sketches with the videos, it clearly shows that the appropriate appearance and motion based feature selection has enhanced the video retrieval accuracy up to 96.90% with 99.28% accuracy in action identification considering motion stream, 98.17% for appearance module and 98.45% for fusion module.Another important aspect of the proposed research context is that it addresses the problem of cross-modality while executing the simultaneous matching paradigm for visual appearances of the object with its movement appearing on particular video scenes.The experimental outcome shows https://www.indjst.org/
Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Objectives: Designing dynamic computer systems that are effective, efficient, simple, and satisfying to use is becoming extremely important in this age of information and communication technology.Text to Speech or Speech Synthesis is one of the many methods being investigated by researchers to improve Human-Computer Interaction.The goal here is to improve the text processing component of the Tamil voice synthesizer by including a text normalizer and loan word identification that is efficient and reliable.Methods: Text normalization is conducted on unconstrained Tamil text to turn non-standard terms into common words to reduce confusing utterances during intermediate word processing.Loan/Native words in Tamil literature are detected to enhance the Tamil voice synthesizer system's pronunciation model.Findings: During normalization, non-standard Tamil words are replaced with standard ones to reduce ambiguous utterances during interim processing.A pronunciation model is built to improve the Tamil speech synthesizer system by identifying loan words in Tamil text.A syllable classifier is presented in this study, based on a decision list approach, which can handle various types of non-stationary sounds.Novelty: We also disclose a 'loan/native word classifier' based on multiple linear regressions that perform well even with small words of three syllables.Such sophisticated text processors are required in today's dominating Digital, Information-Communication Technology, and Human-Computer Interaction age.
FTP Server Client Using Android Application

On a computer network, the File Transfer Protocol (FTP) is a common communication protocol used to transfer data from a server to a client. FTP uses distinct control and data connections between the client and the server in client-server architecture. FTP offers two transfer methods: an anonymous technique and a password-based authentication method. The primary goal of FTP is to encourage file sharing. Clients can connect to the server using third-party applications (eg. FileZilla). A single FTP object can manage status, connection, settings, etc. is the centre of the Apache Commons Libraries. Obtaining a remote connection to involves a set of details, including the server's hostname or IP address, a username and a password.
Multiple‐model generalised labelled multi‐Bernoulli filter with distributed sensors for tracking manoeuvring targets using belief propagation

Abstract The multi‐sensor multiple‐model generalised labelled multi‐Bernoulli filter (MS‐MM‐GLMB) is presented for tracking multiple manoeuvring targets. And we develop efficient implementation for computing multi‐target posterior. The proposed implementation of the MS‐MM‐GLMB filter is composed of two main parts including centralised fusion with distributed sensors and posterior calculation using belief propagation. Specifically, the centralised fusion with distributed sensors is performed based on common predicted information to enable a full parallelisation. This fusion strategy yields the exact solution along with independence of the sensor ordering. More importantly, this fusion allows multiple sensor fusion with the MM‐GLMB filters for iterative update step for synchronous measurements. Subsequently, the belief propagation algorithm is adopted to implement the MS‐MM‐GLMB filter by efficiently calculating the marginal posterior distributions of GLMB form with superior tracking performance. The belief propagation solution to solving data association is presented for the computationally efficient centralised tracking of the manoeuvring targets. Compared with Gibbs‐based implementation, the proposed belief‐propagation‐based method performs well or better, especially in more challenging scenario.
Monolingual and Bilingual Reading Processes in Russian: An Exploratory Scanpath Analysis

In the present study, we used a scanpath approach to investigate reading processes and factors that can shape them in monolingual Russian-speaking adults, 8-year-old children, and bilingual Russian-speaking readers. We found that monolingual adults’ eye movement patterns exhibited a fluent scanpath reading process, representing effortless processing of the written material: They read straight from left to right at a fast pace, skipped words, and regressed rarely. Both high-proficiency heritage-language speakers’ and second graders’ eye movement patterns exhibited an intermediate scanpath reading process, characterized by a slower pace, longer fixations, an absence of word skipping, and short regressive saccades. Second-language learners and low-proficiency heritage-language speakers exhibited a beginner reading process that involved the slowest pace, even longer fixations, no word skipping, and frequent rereading of the whole sentence and of particular words. We suggest that unlike intermediate readers who use the respective process to resolve local processing difficulties (e.g., word recognition failure), beginner readers, in addition, experience global-level challenges in semantic and morphosyntactic information integration. Proficiency in Russian for heritage-language speakers and comprehension scores for second-language learners were the only individual difference factors predictive of the scanpath reading process adopted by bilingual speakers. Overall, the scanpath analysis revealed qualitative differences in scanpath reading processes among various groups of readers and thus adds a qualitative dimension to the conventional quantitative evaluation of word-level eye-tracking measures.
On Categoricity of Scattered Linear Orders Of constructive Ranks

Abstract In this article we investigated the complexity of isomorphisms be-tween scattered linear orders of constructive ranks. We gave the general upperbound and proved that this bound is sharp. Also, we constructed examples show-ing that the categoricity level of a given scattered linear order can be an arbitraryordinal from 3 to the upper bound, except for the case when the ordinal is thesuccessor of a limit ordinal. The existence question of the scattered linear orderswhose categoricity level equals to the successor of a limit ordinal is still open.