Reviewerly/exHarmony huggingface dataset:
Junction Point Detection and Identification of Broken Character in Touching Arabic Handwritten Text using Overlapping Set Theory

Touching characters are formed when two or more characters share the same space with each other.Therefore, segmentation of these touching character is very challenging research topic especially for handwritten Arabic degraded documents.This is one of the key issue in recognition of the handwritten Arabic text.In order to make the recognition system more effective segmentation of these touching handwritten Arabic characters is considered to be very important research area.In this research, a new method is proposed, which is used to identify the junction or common point of Arabic touching word image by applying overlapping or intersection set theory operation, which will help to trace the correct boundary of the touching characters, identify the broken characters and also segmented these touching handwritten text in an efficient way.The proposed method has been evaluated on Arabic touching handwritten characters taken from handwritten datasets.The results show the efficiency of the proposed method.The proposed method is applicable to both degraded handwritten documents and printed documents.
USER REQUIREMENT ANALYSIS OF A MOBILE AUGMENTED REALITY APPLICATION TO SUPPORT LITERACY DEVELOPMENT AMONG CHILDREN WITH HEARING IMPAIRMENTS

Literacy is fundamental for children's growth and development, as it affects their educational, societal, and vocational progress.However, the mapping of language to printed text is different for children with hearing impairments.When reading, a hearingimpaired child maps text to sign language (SL) which is a visual language that can benefit from technological advancements, such as augmented reality (AR).Several research efforts that utilises AR for advancing the educational needs of people who are hearing impaired for different SLs are evident.Nevertheless, only a few directly elicit the visual needs of children who are hearing impaired.This study aims to address this gap in the literature with a series of user studies to elicit user requirements for the development of an AR application that supports the literacy development of Arab children who are hearing impaired.Three instruments were utilised in these user studies, each targeting a different group of literacy influencers: questionnaires issued to parents of children with hearing impairments, interviews with teachers, and observations of children who were deaf or hard of hearing.The findings indicated that the parents and teachers preferred Arabic SL (ArSL), pictures, and videos, whereas the children struggled with ArSL and preferred fingerspelling.These preferences highlighted the importance of integrating various resources to strengthen written Arabic and ArSL literacy of Arab children.The findings have contributed to the literature on the preferences of Arab children who are hearing impaired, their educators, and their parents.They also showed the importance of establishing requirements directly from intended disabled users in proactively supporting their learning process.The results of the study was used in the preliminary development of Word & Sign, an AR mobile application intended to aid Arab children who are hearing impaired in their linguistic development.
Improving Human Needs Categorization of Events with Semantic Classification

Human Needs categories have been used to characterize the reason why an affective event is positive or negative.For example, "I got the flu" and "I got fired" are both negative (undesirable) events, but getting the flu is a Health problem while getting fired is a Financial problem.Previous work created learning models to assign events to Human Needs categories based on their words and contexts.In this paper, we introduce an intermediate step that assigns words to relevant semantic concepts.We create lightly supervised models that learn to label words with respect to 10 semantic concepts associated with Human Needs categories, and incorporate these labels as features for event categorization.Our results show that recognizing relevant semantic concepts improves both the recall and precision of Human Needs categorization for events.
Virtualizing a Cluster to Optimize the Problems of High Scientific Complexity within an Organization

The Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades has several research projects related to computer science needing high computational resources.Some of these projects are associated with climate prediction, molecule modeling, physical simulations, and others these applications generate a significant amount of data, regarding the big data issue, despite having excellent hardware features, the final result is obtained after hours or days of calculation depending on the algorithm complexity.For this reason, it is not possible to present optimal solutions at an ideal time..In this work, we propose the virtualization and configuration of a high-performance cluster (HPC) known commercially as a "supercomputer" that is composed of several computers connected to a high-speed network to behave like a single computer.The virtualization is used to run a scientific algorithm that will apply performance tests using four virtual computers to demonstrate that the reduction of time is achieved by using more machines and thus be able to be implemented in the laboratories of the institution.
Perancangan Sistem Pakar Diagnosis Penyakit pada Bayi dengan Metode Forward Chaining Berbasis Android

In infancy, the age of 0-12 months is a very important period in the process of human growth and development. The process of growth and development at this time affects the child's growth and development in the next period and to reduce mortality in infants. Quick and precise information needs from a baby health expert are needed. The method used in making this expert system is forward chaining, where the system is driven by existing facts. These facts are obtained from the patient's physical condition, or referred to as symptoms. With the existence of an android-based expert system, users everywhere can access to the internet and connect. The process of extracting knowledge by means of interviews. Based on the analysis, this expert system has a percentage of program eligibility for use by laymen is 82.7%. Besides that, based on the results of the questionnaire, the ease of the program also gets a large percentage as well
A Compression-based BiLSTM for Treating Teenagers’ Depression Chatbot

Anxiety is a psychological condition that often occurs during adolescence. Due to lack of relief and counseling, teenagerâ€™s psychological anxiety may gradually develop into anxiety. Chabot can be used as a new tool to relieve anxiety among teenagers. However, the natural language understanding techniques currently applied to Chabot still have problems, such as lack of effective data, high training complexity, and lack of interpretability of the network. This paper proposes a compression-based bidirectional Long Short-Term Memory depth neural network structure. The main objective is to reduce the complexity of the parameters further, and to make the network of each layer have certain interpretability by means of the reduction of sparsity. Under our own collection of teen depression text data, this structure shows a better performance than traditional networks.
Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking

Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don't offer tools for quickly identifying which log dialogues contain problems.This is important since corrections to log dialogues provide a means to improve performance after deployment.A log dialogue ranker, which ranks problematic dialogues higher, is an essential tool due to the sheer volume of log dialogues that could be generated.However, training a ranker typically requires labelling a substantial amount of data, which is not feasible for most users.In this paper, we present a novel unsupervised approach for dialogue ranking using GANs and release a corpus of labelled dialogues for evaluation and comparison with supervised methods.The evaluation result shows that our method compares favorably to supervised methods without any labelled data.
Fostering Students’ Reading Comprehension Ability through Directed Reading Thinking Activities (DRTA) Strategy

&lt;p&gt;&lt;em&gt;T&lt;em&gt;he current curriculum school reform in Indonesia supported the students to be a critical thinker in the sense that the students become a centered of teaching instruction in the school life. This changing becomes a new demand for the teacher as an educator to facilitate their learners with teaching aids practices that adjust learners to be actively participate in the classroom. Thus, this study used Directed Reading Thinking Activities (DRTA) to reveal the effectiveness of this strategy on the students’ reading comprehension teaching. A quasi-experimental research design with pretest and posttest was used in this study. Two classes were randomly selected as the sample of this research. They are class 8C which used Directed Reading Activities (DRA) as the control group, while class 8H becomes the experimental group which used Directed Reading Thinking Activities (DRTA). Independent and dependent (paired) sample t-tests were used to analyze the data. The research finding showed: 1) there were no significant differences of posttest score among the students taught by using DRTA and those taught by using DRA strategy in reading comprehension teaching, 2) there were significant effects of using DRTA in reading comprehension teaching for grade eight students. DRTA strategy was believed to give more contribution (82%) than DRA strategy which contributed only 77%. In conclusion, DRTA was effective strategy in reading comprehension teaching.&lt;/em&gt;&lt;br /&gt;&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing

Our submission for Task 1 'Cross-lingual Semantic Parsing with UCCA' at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transitionbased directed acyclic graph parser.We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children.We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks.We submitted results to all open tracks (English, in-and out-of-domain, German in-domain and French in-domain, low-resource).Our system achieves competitive performance in all settings besides the French, where we did not augment the data.Post-evaluation experiments showed that data augmentation is especially crucial in this setting.
Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalciﬁcations appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for womens quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this paper, we have presented a novel approach to identify the presence of breast cancer lumps in mammograms. The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented. MST initialization method for the intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images and Breast cancer patients symptoms used to predictive probability calculated by Pearson Chi-Square (χ2) test at 0.05 significance level indicate a highly significant correlation between mammography performance and clinical symptoms of breast cancer. Our findings suggest that mammography is highly efficient and promising technique.
Nominal vs copular clauses in a diachronic corpus of Ancient Greek historians

Abstract We study the distribution of the nominal and copular construction of predicate nominals in a subset of authors from the Ancient Greek Dependency Treebank ( AGDT ). We concentrate on the texts of the historians Herodotus, Thucydides (both 5th century BCE) and Polybius (2nd century BCE). The data comprise a sample of 440 sentences (Hdt = 175, Thuc = 91, Pol = 174). We analyze the impact of four features that have been discussed in the literature and can be observed in the annotation of AGDT : (1) order of constituents, (2) part of speech of the subjects, (3) type of clause and (4) length of the clause. Furthermore, we test how the predictive power of these factors varies in time from Herodotus and Thucydides to Polybius with the help of a logistic-regression model. The analysis shows that, contrary to a simplistic opinion, the nominal construction does not drop into irrelevance in Hellenistic Greek. Moreover, an analysis of the distributions in the authors highlights a remarkable continuity in the usage patterns. Further work is needed to improve the predictive power of our logistic-regression model and to integrate more data in view of a more comprehensive quantitative diachronic study.
Acceleration Techniques for Fully-Homomorphic Encryption Schemes

It is known that cloud computing opened new opportunities for many organizations regarding the cost-savings and the availability rate. Homomorphic encryption schemes are well known for solving the privacy preserving issue, but these schemes bring deficiency in execution times, therefore accelerating these encryption schemes using different technologies is the next step towards the wide use of homomorphic primitives. The main contribution is an implementation of a homomorphic encryption library using multiple GPU's, more precisely an adaptation of the HElib library. While other single GPU accelerations of HElib do exist, the proposed implementation uses multiple graphic processors units to distribute computational tasks.
Developing Red-White Monopoly Games through Integrative Thematic Learning in the Primary School

Playing is a fun activity and liked by children. It can be used as a media of learning. The observations carried out in SD Negeri 3 Sawah Lama Bandar Lampung found that the learning media used was not interesting for students to learn. The aims of the study are to determine the steps in developing a game of red-and-white monopoly media;to know the quality of red-and-white monopoly games; and to find out the responses of students to the red-and-white monopoly game. This study is a research anddevelopment (R & D) using the Borg and Gall model. The steps start from exploring potential aspect and its problems, data collection, product design, design validation, design revision, product testing, and product revision. The techniques of data collection are observations, interviews, and questionnaires.The data analysis technique uses is qualitative descriptive. The results of the analysis show that the assessment of material experts gained an average of 93% "very good",the assessment of media experts gained an average of 92% "very good", the assessment of education practitioners gained an average of 90% "very good". While the questionnaire responses of students in the small class trials gained an average of 96% "very good" andthe results of a large class trial gained an average of 94% "very good". Based on the results of the description above, the products developed by researchers are feasible to be used as learning media. Keywords: media, monopoly games, integrative thematic learning Abstrak Bermain merupakan kegiatan yang menyenangkan dan disukai oleh anak-anak. Bermain juga dapat digunakan sebagai media dalam pembelajaran. Hasil observasi yang dilakukan di SD Negeri 3 Sawah Lama Bandar Lampung didapati bahwa bentuk media pembelajaran yang digunakan tidak menarik peserta didik untuk belajar. Penelitian ini bertujuan untuk mengembangkan media permainan monopoli merah-putih, mengetahui kualitas media permainan monopoli merah-putih, dan untuk mengetahui respon peserta didik terhadap media permainan monopoli merah-putih. Penelitian ini merupakan penelitian pengembangan (R&D) dengan menggunakan model Borg dan Gall. Langkah-langkahnya dimulai dari menggali potensi dan masalah, pengumpulan data, desain produk, validasi desain, revisi desain, uji coba produk, dan revisi produk. Teknik pengumpulan data yang digunakan adalah observasi, wawancara, dan angket. Teknik analisis data yang digunakan adalah deskriptif kualitatif. Hasil penelitian menunjukkan bahwa penilaian ahli materi memperoleh rata-rata 93% "sangat baik", penilaian ahli media memperoleh rata-rata 92% "sangat baik", penilaian praktisi pendidikan memperoleh rata-rata 90% "sangat baik". Sementara itu, respon peserta didik pada uji coba kelas kecil memperoleh rata-rata 96% "sangat baik", dan hasil uji coba kelas besar memperoleh rata-rata 94% "sangat baik". Berdasarkan hasil uraian di atas, maka produk yang dikembangkan oleh peneliti layak untuk digunakan sebagai media pembelajaran. Kata kunci: media, permainan monopoli, pembelajaran tematik integratif
Enhancement AES based on 3D Chaos Theory and DNA Operations Addition

The techniques modern used to encrypt text data play an important role in preventing unauthorized access. The AES algorithm is widely used in various cryptographic applications, but coz the algorithm has been subjected to several successive attacks, the need is necessary to improve the algorithm and make it more powerful the algorithm.In dis search will be inserted plaintext 1024 will be divided into four blocks each Block consists of 256 bits and each Block will pass the four stages represent(substitution,shift,Mix column and round key).A new step will be added to the four stages of permutation. Where chaos theory is adopted in the permutation stage and the shift phase.DNA will be adopted at the phase of the Mix and add round key stage due to it's high storage capacity. The time taken in the coding and decoding in the proposal enhancement algorithm will take a few milliseconds. The output will be measured using a five statistical test and a 16NIST Test. The results showed excellent efficiency in terms of randomness used in the proposal, thus preventing the tractor of sensitive data in an unsafe environment.
Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

The demand for cloud computing resources is increasing due to its accessibility everywhere at any time.When the number of clients for cloud services increases, the load on the cloud nodes becomes high.This status requires load balancing to evenly distribute client requests among the available Virtual Machines (VMs) in a Data Center (DC).There are different standard dynamic load balancing techniques, such as Throttled and Active Monitoring.In this paper, a Genetic Algorithm (GA) is incorporated with a throttled to improve load balancing.The improvement is achieved by enhancing the overall response time, the data center processing time, and the maximum resource utilization.Simulation results show the improved performance of the proposed method compared to the ESCE and Throttled.
Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment

Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcare providers and patients to efficiently store and share their private healthcare information from any place and at any time as per demand.Generally, Cloud services provide the infrastructure by reducing the cost of storing, processing and updating information with improved efficiency and quality.However, the privacy of Electronic Health Records (EHR) is a significant hurdle while outsourcing private health data in the cloud because there is a higher peril of leaking health information to unauthorized parties.Several existing techniques are able to analyse the security and privacy issues associated with e-healthcare services.These methods are designed for single database, or databases, with an authentication centre and thus cannot adequately protect the data from insider attacks.Therefore, this research study mainly focusses on how to ensure the patient privacy while sharing the sensitive data between same or different organisations as well as healthcare providers in a cloud environment.This paper proposes a multi-layer access control mechanism named MLAC Model to construct a secure and privacy-preserving EHR system that enables patients to share their data with stakeholders.In this paper, we use a Dual layer access control model named Pseudo-Role Attribute based access control (PR-ABAC) mechanism that integrates attributes with roles for the secure sharing of EHR between multiple collaborators.The proposed framework also uses the concept of Provenance to ensure the Integrity of patient data.This work is expected to provide a foundation for developing security solutions against cyber-attacks, and thus contribute to the robustness of healthcare information sharing environments.
Secure k-NN query on encrypted cloud data with multiple keys

The K-nearest neighbors (k-NN) query is a core relating for occupying space and being using many channel for communication in the database.It has large-scale of appeal in the location-based utility, grouping and collection and soon.With the assurance of confidentiality and solitude, enormous amount of facts are growing and service are being obtained in a contract way from various suppliers in a formation of code.Which will leads to favorable being fit in the cloud system.Freshly, numerous strategies are kept forward to transfer the converted facts.Yet previous work expect that where all the clients are completely believed that they are going to get the uni facts with the key, where it is used to convert the facts and make it available for the clients to get it.This way of transfer of facts seems to be impracticable where many clients are not truthful nor significant in using the key, in this issue we prefer paperback logic for it, data owner and query user has it assess and identical keys, and they don't share it with one-other meantime, the DO convert and make outsourced data using it with their own key.Our logic is created and a set of rules are fixed in order to provide and safeguard the facts and issue seclusion and it will carry downloaded facts, in regarding of safety and production.
Mobile-mediated communication and students' listening skills: a case study

This research attempted to shed light on the impact of using mobile chat-based applications on the development of language listening skills in the English as a Foreign Language (EFL) classroom. Mobile Instant Messaging (MIM) was used to develop students' listening skills in second language learners as well as to observe students' experience within the application and its potential features for educational use. A total of 61 students that enrolled in an Administration and Business Management course participated in the experiment and were divided into control and experimental group. 20 students were selected to participate in a WhatsApp group on a daily basis as part of their study for a semester. A mixed method research design was used to analyse the data in both groups. Results indicated that voice-based chat conversation helped students adapt to different accents and tones, and gave rise to a high number of language related-episodes which may foster listening comprehension. Students also reported benefits in terms of vocabulary and pronunciation.