Reviewerly/exHarmony huggingface dataset:
The Realization of Thematic Progression in the Students’ Final Project Proposal

The use of thematic progression seems important to be applied for among the students' writing projects to maximize the script's organization.This research reported how the realization of thematic progression model to achieve both cohesion and coherence.This study was qualitatively designed to inform these three models of thematic progression as the units of the analysis: 1) constant theme; 2) linier theme, and 3) multiple theme which they were realized in single text of students' projects.The data analysis was taken from 20 manuscripts of the English students in UIN Walisongo in Academic Year 2019/2020.The findings then showed that the most realization of thematic progression in the students' final project proposal was constant theme, it occurred 248 times, linier theme was 183 times, and multiple theme was only 57 occurrences.Those occurrences represented that the text was wellorganized to realize the concept of coherence and cohesion.
RECA: Relation Extraction Based on Cross-Attention Neural Network

Extracting entities and relations, as a crucial part of many tasks in natural language processing, transforms the unstructured text information into structured information and provides corresponding data support for knowledge graph (KG) and knowledge vault (KV) construction. Nevertheless, the mainstream relation-extraction methods, the pipeline method and the joint method, ignore the dependency between the subject entity and the object entity. This work introduces a pre-trained BERT model and a dilated gated convolutional neural network (DGCNN) as an encoder to distinguish the long-range semantics representation from the input sequence. In addition, we propose a cross-attention neural network as a decoder to learn the importance of each subject word for each word of the input sequence. Experiments were undertaken with two extensive datasets, the New York Times Corpus (NYT) and WebNLG Corpus, and showed that our model performs significantly better than the CasRel model, outperforming the baseline by 1.9% and 0.7% absolute gain in terms of F1-score.
High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator

Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today. With convolution operations being computationally intensive, next generation hardware accelerators need to offer parallelization and algorithmic-hardware homomorphism. Fortunately, diffractive display optics is capable of million-channel parallel data processing at low latency, however, thus far only showed tens of Hertz slow single image and kernel capability, thereby significantly underdelivering from its performance potential. Here, we demonstrate an operation-parallelized high-throughput Fourier optic convolutional neural network accelerator. For the first time simultaneously processing of multiple kernels in Fourier domain enabled by optical diffraction has been achieved alongside with already conventional in the field input parallelism. Additionally, we show an about one hundred times system speed up over existing optical diffraction-based processors and this demonstration rivals performance of modern electronic solutions. Therefore, this system is capable of processing large-scale matrices about ten times faster than state of art electronic systems.
Re-ranking for Writer Identification and Writer Retrieval

Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets.
Pragmatics Study on Deixis Analysis in Narrative Texts in a Textbook of SMP Nasima Semarang

Deixis is a branch of pragmatics field.It needs to be learnt by the learners.By learning deixis, they will be able to use deictic words properly.Therefore, the aids used by the learners should support them in their learning activities.This research aims to investigate the deictic words which used in narrative texts in a textbook.This research also analyzed their interpretation and the contribution of the dominant deictic word used.This research was a descriptive qualitative research.The source of the data was three narrative texts which adopted from a textbook of SMP Nasima Semarang.In analyzing the texts the researcher employed the deixis theory suggested by Stephen C Levinson (1983).There are five types of deixis such as person, time, place, social and discourse deixis.The findings revealed all types of deixis are used in the texts.The total occurrences of deictic words used in among texts are 96 occurrences.The dominant deictic word used in among texts is 'he'.It is a kind of person deixis especially third person deixis.It is because commonly narrative text is talk about retelling someone else story.In short, each type has their own roles in written or spoken languages.
Determinants of Satisfaction in Virtual Reality Sports: Focusing on Interactivity, Presence and Flow Experience

Virtual reality (VR) technologies enable a new media consumption experience.Although VR's origins trace back at least to the 1960s, it is still unclear how VR's postulated key features immersion, presence, and interactivity contribute to that experience.Furthermore, it is unclear whether flow as a construct closely related to immersion offers explanatory power in investigating VR.On the basis of a quantitative survey in a VR center with 294 participants, I analyze the interplay of the key features and exemplify their influence in a VR context by relating them to satisfaction with the VR experience.Using a flowbased conceptualization of immersion, I find that presence as well as interactivity contribute to immersion.In addition, interactivity contributes to presence.Furthermore, my results show that immersion influences satisfaction with a VR experience, indicating that a flow-based conceptualization of immersion is a suitable predictor in VR contexts.
COVID-19 Dashboard

Abstract: Applying a digital strategy is proven to be one way to reduce the infection rate of COVID-19. As a fundamental part of it, database is capable to create a data repository to predict uncertain impacts of the pandemic. The research proposed a database design for project C-Beta that emphasized on creating an ideal system to record and monitor the daily health condition of people with COVID-19. Mainly, the research adopted Connolly and begg principles of Database Software Development Life Cycle. As a result, the database design activities produced blueprints in the forms of data dictionary, ERD, and user views. The database design delivered in the research included with several features namely user verification, restricted research membership, and flexibility in survey creation. Based on analysis and design phase, C-Beta provides a method to collect data for COVID-19 clinical study. Researchers who utilize C-Beta is able to manage multiple surveys and extract all collected records at the same time.
Explicit Contextual and Morphemic Analysis Instruction, Vocabulary, and Reading Comprehension Among Grade Four Students

The schema theory, cognitive load theory, interactive model of reading, and lexical quality hypothesis recognizes the primacy of vocabulary in reading comprehension. Substantial empirical research affirms this relationship. Despite these, vocabulary teaching in the classroom remains sparse. The present study sought to find out if explicit instruction in contextual and morphemic analysis would improve vocabulary and reading comprehension. It also examined which aspects of vocabulary and reading comprehension would improve the most. This study used a quasi-experimental, pretest-posttest within-subjects design. Participants were 38 students from two comparable Grade 4 classes. Explicit instruction in contextual and morphemic analysis was given through twelve expert-validated lesson plans that were implemented across 18 weeks. The tools used were the expert-validated and field-tested Researcher-Constructed Vocabulary Test and Researcher-Constructed Reading Comprehension Test. Dependent-samples t-test results revealed a significant increase in the students’ vocabulary and reading comprehension scores. These results are generally consistent with most theoretical and empirical literature. In terms of vocabulary, contextual analysis-wise, the biggest gain was in example clues, while morphemic analysis-wise, the largest increase was in the over/under word part family. In terms of reading comprehension, the biggest increase was in the integrative level. Overall, the findings suggest that explicit instruction in contextual and morphemic analysis is an effective method of teaching vocabulary strategies which impacts not only vocabulary but also reading comprehension. This has implications for instruction, curriculum planning, pre-service and in-service teacher training, and research.
Scanning the Issue

Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges
Digital Handwriting Recognition Using Hand Tracking by Using Media Pipe and OPENCV Libraries

Abstract: While in taking online classes or in day-to-day activities writing is more important task, as we are living in digital age which means most of our tasks are been shifted to digital trends such as like paying bills, booking tickets, emotion tracking etc., among this writing is at most important task weather you share ideas plan for a particular task taking notes, in this data age stats show humans more often prefer to write and use about 100 pages per average. It is quite our instinct to understand and represent any information which is written in handwriting, but when it comes to digitalworld though they are gesture based, touch based methods they couldn’t match the natural hand-write representation. So, we come with solution for this problem forsome extent by using hand tracking and hand landmark position representation whichis offered by media pipe library form google. This technique uses image processing (OpenCV) to capture video from webcam which will be given to our python application which uses media pipe library to recognize hand and represents it as 0-20 landmark positions. Which will be used to find index finger for writing and use two fingers for removing or erasing pre-written information. This type of application isvery useful in a day to day basic activates for students, teachers and makers in order to easily plan and organize projects, notes etc., Though this technique neither match areal writing experience nor the writing tablet for computers and smartphones which can translate our writing into digital notebook, it does not require any additional equipment and can be easily used to if practiced. This type of application can improvise the understanding by allowing users to explain in a whiteboard like environment in online education.
Nonlinear three-state quantum walks

The dynamics of a three-state quantum walk with amplitude-dependent phase shifts is investigated. We consider two representative inputs whose linear evolution is known to display either full dispersion of the wave packet or intrinsic localization on the initial position. The nonlinear counterpart presents much more involved dynamics featuring self-trapping, solitonic pulses, radiation, and chaotic-like behavior. We show that nonlinearity leads to a metastable self-trapped wavepacket component that radiates in the long-time regime with the survival probability $\propto t^{-1/2}$. A sudden dynamical transition from such metastable state to the point when the radiation process is triggered is found for a set of parameters.
Design and Implementation of Management Information System for Research and Community Engagement Activities

Since 2019, UPN “Veteran” Jawa Timur has been awarded a predicate of “Klaster Mandiri” (Self-Governing Cluster) in terms of research based on the assessment of university research performance in 2016-2018. The status has a direct impact on the increasing number of research funds that can be managed internally by LPPM. However, in managing its activities related to the implementation of independent grants, LPPM faces challenges. Especially in the last two years, the pandemic of Covid-19 has forced any organization, including LPPM, to find an alternative way to the restricted face-to-face interaction. Hence, many activities such as submitting research proposals or progress reports previously carried out offline had to be done online. However, the tools used to exchange and store data (soft-copy files) are still very simple since LPPM only uses e-mail and google drive. The disadvantage of this method is that documents are vulnerable to lose, and if there is an urgent request for data that must be quantified, it is difficult to fulfill. Therefore, it is necessary to transform the business processes to facilitate online interaction as an alternative and complement to offline interaction and improve the quality of the research administration. This change in business processes is implemented by an application named SIMARIS which will be the focus of discussion in this paper. SIMARIS is developed using the V Model and ICONIX process. As a reference in the coding stage, UML diagrams generated from the Iconix process will be used. The application was developed using the PHP CI 3 framework and then tested using the Black-Box method. The results of the black box testing confirm that all features that have been developed match the users requirements. This application is intended to be used by lecturers and mainly by LPPM to better manage its self-governing research and community engagement activities.
Innovative metamodels of interaction between participants of postgraduate teacher education

The article presents the analysis of two actual variants of interaction between teachers and learners in the system of in-service training, which are an interpretation of the idea of S. G. Vershlovsky about the innovative metamodel, the most popular in modern post-graduate pedagogical education.
Scalable Graph Embedding LearningOn A Single GPU

Graph embedding techniques have attracted growing interest since they convert the graph data into continuous and low-dimensional space. Effective graph analytic provides users a deeper understanding of what is behind the data and thus can benefit a variety of machine learning tasks. With the current scale of real-world applications, most graph analytic methods suffer high computation and space costs. These methods and systems can process a network with thousands to a few million nodes. However, scaling to large-scale networks remains a challenge. The complexity of training graph embedding system requires the use of existing accelerators such as GPU. In this paper, we introduce a hybrid CPU-GPU framework that addresses the challenges of learning embedding of large-scale graphs. The performance of our method is compared qualitatively and quantitatively with the existing embedding systems on common benchmarks. We also show that our system can scale training to datasets with an order of magnitude greater than a single machine's total memory capacity. The effectiveness of the learned embedding is evaluated within multiple downstream applications. The experimental results indicate the effectiveness of the learned embedding in terms of performance and accuracy.
Travails of Bangsamoro Learners on Multilingualism: A Phenomenology

This phenomenological study describes the struggles and coping mechanisms in Multilingual learning among the learners in Hadji Ali Baganian Memorial School Inc. It utilized the method described by Colaizzi (1978) to analyze data, including familiarization, identifying significant statements, formulating meanings, clustering themes, developing a detailed description, producing the fundamental structure, and verifying the fundamental structure. The participants were the ten purposively selected Grade 9 students at the said school who performed average to outstanding levels in their respective classes. This school has provided Bangsamoro learners educational perspectives both in the private Madaris and the DepEd curriculum. The findings revealed that the participants encountered difficulties with multilingualism. The difficulties include negative attitude and complexity of the rules in phonology, a poor background of students in word stratification in morphology, poor sentence construction due to the complexity of rules in syntax, and word recognition and correct usage in semantics. Five coping mechanisms were identified to address these difficulties. These were oral practicing, using a dictionary, tutorial, listening attentively, taking notes, and internet surfing. These identified coping mechanisms were summarized into one emergent theme – intrinsic motivation. The researchers concluded that language users and language learners need to know their language needs, background, progress in acquiring the target language, and capability to use it.
Perspectives of Muslim and Minority Canadian Youth on Hate Speech and Social Media

In this article, we highlight the perspectives of marginalized Canadian youth regarding hate speech on social media. Specifically, our research focus is on the complexity and intersectionality involved in cyber violence, especially in relation to marginalized identities. Twenty-five participants aged 18 to 25 studying at a central Canadian University (from an initial sample of 90 participants) who self-identified as victims of hate speech were invited to share their experiences and narrate their stories. Research results demonstrate that online hate speech is growing in Canada to an extent where it is has become normalized. This has serious implications for the well-being of Canadian youth - both perpetrators and victims of hate speech. The main targets of hate speech on social media in Canada are immigrants and minorities, particularly Muslims. Results show that online hate speech has significant consequences for the lives of Canadian youth. The repercussions for the victim's mental and physical well-being manifest in problems ranging from alienation, identity issues, deterioration of psychological and physical health to cyber and in-person bullying, and much more. The study concludes that while there are definite links between the rise of online hate speech, deterioration of mental and physical health, and increased attacks on immigrants and minorities, not much action has gone into policymaking and education to correct the situation.&#x0D;
Finite-rate sparse quantum codes aplenty

We introduce a methodology for generating random multi-qubit stabilizer codes based on solving a constraint satisfaction problem (CSP) on random bipartite graphs. This framework allows us to enforce stabilizer commutation, $X/Z$ balancing, finite rate, sparsity, and maximum-degree constraints simultaneously in a CSP that we can then solve numerically. Using a state-of-the-art CSP solver, we obtain convincing evidence for the existence of a satisfiability threshold. Furthermore, the extent of the satisfiable phase increases with the number of qubits. In that phase, finding sparse codes becomes an easy problem. Moreover, we observe that the sparse codes found in the satisfiable phase practically achieve the channel capacity for erasure noise. Our results show that intermediate-size finite-rate sparse quantum codes are easy to find, while also demonstrating a flexible methodology for generating good codes with custom properties. We therefore establish a complete and customizable pipeline for random quantum code discovery.
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox

Inspired by a recent breakthrough of Mishchenko et al (2022), who for the first time showed that local gradient steps can lead to provable communication acceleration, we propose an alternative algorithm which obtains the same communication acceleration as their method (ProxSkip). Our approach is very different, however: it is based on the celebrated method of Chambolle and Pock (2011), with several nontrivial modifications: i) we allow for an inexact computation of the prox operator of a certain smooth strongly convex function via a suitable gradient-based method (e.g., GD, Fast GD or FSFOM), ii) we perform a careful modification of the dual update step in order to retain linear convergence. Our general results offer the new state-of-the-art rates for the class of strongly convex-concave saddle-point problems with bilinear coupling characterized by the absence of smoothness in the dual function. When applied to federated learning, we obtain a theoretically better alternative to ProxSkip: our method requires fewer local steps ($O(\kappa^{1/3})$ or $O(\kappa^{1/4})$, compared to $O(\kappa^{1/2})$ of ProxSkip), and performs a deterministic number of local steps instead. Like ProxSkip, our method can be applied to optimization over a connected network, and we obtain theoretical improvements here as well.
fastball: A fast algorithm to sample bipartite graphs with fixed degree sequences

Many applications require randomly sampling bipartite graphs with fixed degrees, or randomly sampling incidence matrices with fixed row and column sums. Although several sampling algorithms exist, the ``curveball'' algorithm is the most efficient with an asymptotic time complexity of O(n log n), and has been proven to sample uniformly at random. In this paper, we introduce the ``fastball'' algorithm, which adopts a similar approach but has an asymptotic time complexity of O(n). We show that a C++ implementation of fastball randomly samples large bipartite graphs with fixed degrees faster than curveball, and illustrate the value of this faster algorithm in the context of the fixed degree sequence model for backbone extraction.
Any Time Medicine Vending Machine

Abstract: This paper describes the design of any time medicine vending machine as a health device for the purpose of person who in need of medicines and its assistance. This device mainly consists of a card reader and the dispenser which comprises the automatic medicine vending machine through the dispenser as per the doctor’s e-prescription updated into smart card. This vending machine is controlled by the Arduino board which is a single board computer and it can be aspect from online portal for the need person to check his e-prescription and the doctor to generate or update the prescription. When the patient swipes his smart card into the card reader, this device dispenses out the prescribed medicine for the need person only when the user credentials of the patient is validated from the e-prescription. The online portal is made on a telegram and connected to the same user credentials, and even though the patient can view his details and the prescription by logging in with are credentials into the same web page