Reviewerly/exHarmony huggingface dataset:
TIPOLOGI FONOLOGIS PADA BAHASA WEBINAR BIDANG PENDIDIKAN (KAJIAN FONOLOGI GENERATIF)

ABSTRAK: Dilaksanakannya penelitian melalui pendekatan fonologi generatif ini bertujuan mendeskripsikan proses dan kaidah fonologis bahasa webinar khususnya bidang pendidikan. Metode penelitian yang digunakan deskriptif kualitatif, sedangkan analisis datanya menggunakan model analisis Mile Huberman. Sumber data penelitian ini adalah empat rekaman video webinar. Data penelitian berupa kata yang secara fonetis merepresentasikan tipologi fonologis bahasa webinar. Hasil penelitian ini menunjukkan bahwa proses fonologis meliputi 1) asimilasi bertipe regresif, total, parsial, dan langsung, 2) pelemahan bunyi bertipe aferesis, sinkop, dan apokop, sedangkan penguatan bunyi bertipe protesis, epentesis, paragog, dan peluncuran, 3) netralisasi terjadi pada perubahan fonetis bunyi [b] menjadi bunyi [p], dan bunyi [d] menjadi [t] terjadi pada suku kata akhir (koda), 4) disimilasi terjadi secara langsung yang ditandai berubahnya deret konsonan [jj] menjadi [rj], deret konsonan [tt] menjadi [pt], deret konsonan [rr] menjadi [l], dan deret konsonan [ll] menjadi [j], 5) metatesis terjadi karena perubahan letak bunyi KKV dalam silabel menjadi KVK. Pengaidahan fonologis didasarkan fitur distingtif fonem dan penggunaan notasi fitur distingtif. KATA KUNCI: bahasa webinar; fonologi generatif; tipologi &gt; PHONOLOGICAL TYPOLOGY IN THE WEBINAR LANGUAGE OF EDUCATION (STUDY OF GENERATIVE PHONOLOGY) ABSTRACT: This current generative phonology study is aimed at describing the processes as well as the phonological patterns of webinar language, specifically in the education field. Descriptive qualitative design was employed; the data analysis was conducted by means of Miles Huberman model. The data were collected from the webinar recordings. Data in the form of words that phonetically represent phonological typologies of webinar language. It has been revealed that the phonological processes include 1) assimilation with the types of regressive, total, partial, and direct; 2) sound attenuation with the types of apheresis, syncope, and apocope; while sound reinforcement is evident by means of prosthesis, epenthesis, paragoge, and gliding; 3) neutralization from the sound of [b] into [p], and the sound [d] into [t] in the last syllable (coda); 4) dissimilation that occurs directly demonstrated by the alteration of consonant cluster [jj] into [rj], consonant cluster [tt] into [pt], consonant cluster [rr] into [l], and consonant cluster [ll] into [j]; 5) metathesis occurs due to the alteration of sound position of CCV into CVC in a syllable. Phonological patterns are based on phoneme distinctive features and the use of distinctive feature notation. KEYWORDS: webinar language ; generative phonology ; typologies
Multi-Scale Fusion Networks with RGB Image Features for Depth Map Completion

Currently, researchers cannot directly train end-to-end model for depth image completion because of lacking paired "incomplete-complete" RGB-D datasets.To address this problem, a random mask-based method which is combined with "real-synthetic" data for joint training strategy is proposed to construct paired incomplete-complete RGB-D data.This method generates depth maps with different missing ratios based on random masks, and uses synthetic scene datasets to construct missing regions of depth map with high fidelity truth values.Based on this strategy, a multi-scale depth map completion network is constructed, which fuses the corresponding RGB image features.The proposed network extracts RGB image features and depth map features with different scales from RGB image branches and depth map branches.Then, in the feature fusion branch, the RGB image features and depth map features are fused at different scales, which makes that the rich semantic features of RGB images and the information of depth maps can be effectively integrated for depth map completion.Experiments on the NYU-Depth V2 dataset show that in depth completion tasks with different missing ratios, the average threshold accuracy of this method is 0.98, and the mean relative error is about 0.061.Compared with the existing methods based on neural networks and optimizing sparse equations, this method improves the threshold accuracy
Building the Directed Semantic Graph for Coherent Long Text Generation

Generating long text conditionally depending on the short input text has recently attracted more and more research efforts.Most existing approaches focus more on introducing extra knowledge to supplement the short input text, but ignore the coherence issue of the generated texts.To address aforementioned research issue, this paper proposes a novel twostage approach to generate coherent long text.Particularly, we first build a document-level path for each output text with each sentence embedding as its node, and a revised selforganising map (SOM) is proposed to cluster similar nodes of a family of document-level paths to construct the directed semantic graph.Then, three subgraph alignment methods are proposed to extract the maximum matching paths or subgraphs.These directed subgraphs are considered to well preserve extra but relevant content to the short input text, and then they are decoded by the employed pre-trained model to generate coherent long text.Extensive experiments have been performed on three real-world datasets, and the promising results demonstrate that the proposed approach is superior to the state-of-the-art approaches w.r.t. a number of evaluation criteria.
Analysis of the Effectiveness and Efficiency of Software Development Tools

Software tools, frameworks, and libraries are relatively crucial in software development tools.However, these tools cannot guarantee that a developer will produce good quality software applications.This study aims to investigate the efficiency and effectiveness of software tools, specifically frameworks, that support developers in producing good quality applications.Using an experimental approach in software development, the developer participants were given a case in which software needed to be developed with and without a framework.The experiment results were then evaluated to obtain facts from the participants' experiences during development, through some data sources.The results of the study show that the use of a framework does not significantly help developers effectively and efficiently develop software or applications.The success of the application that has been measured based on the level of completion, quality, and usability of the application does not show significant differences in value whether developed with or without tools.Furthermore, the study revealed that the software development skill that cannot be obtained through the use of framework was the general or detailed system conceptualization.
Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service

As the number of users increased over the years, pioneering technologies and solutions in given areas ceased to be sufficient even in terms of performance. Therefore, there was a need for their development or even redesign and redefinition. One of the issues that undoubtedly has a huge impact on the current shape of the global network and the way information is processed in it is the issue of traffic balancing, especially the one in the server infrastructure related to the WWW service, providing users with the possibility of efficient and reliable web browsing. The paper presents a comparative analysis of selected open-source solutions used for traffic balancing in server infrastructures providing WWW service based on selected criteria. The designed architecture of the test environment and the test results of selected tools implementing the traffic-balancing functionality are presented. Methodologies, test plans, and comparison criteria are proposed. A comparative analysis of results based on specific criteria was performed. The balance between network traffic optimization and load balancing distribution among servers is crucial for the development of energy-efficient data processing centers.
Investigating Annotator Bias in Abusive Language Datasets

Nowadays, social media platforms use classification models to cope with hate speech and abusive language.The problem of these models is their vulnerability to bias.A prevalent form of bias in hate speech and abusive language datasets is annotator bias caused by the annotators subjective perception and the complexity of the annotation task.In our paper, we develop a set of methods to measure annotator bias in abusive language datasets and to identify different perspectives on abusive language.We apply these methods to four different abusive language datasets.Our proposed approach supports annotation processes of such datasets and future research addressing different perspectives on the perception of abusive language.
Efficient identity based ring signature scheme in prime order group

Aiming at the problem of long signature generation and verification time caused by low operation efficiency in ring signature algorithm based on composite order group, an asymmetric identity based ring signature scheme based on prime order group is proposed. The model definition and specific identity based ring signature scheme design of the proposed scheme are described, and the correctness and security of the proposed scheme are analyzed. Finally, the efficiency of the core operation part of the algorithm is explained. Compared with the correlation signature algorithm based on composite order group, the optimization has a great improvement in operation overhead and performance, and the designed scheme is unforgeable. The designed signature scheme meets the unconditional anonymity and unforgeability of ring signature.
The effect of colloids practicum e-module to improve students’ interest on virtual lab during the covid-19 pandemic

Practical learning which is generally carried out in the laboratory is one of the important lessons in the science learning process, especially chemistry. The Covid-19 pandemic has caused practical learning activities to not be accommodated in real laboratories. This situation encourages the creation of practical learning innovations, namely the use of virtual labs. In this study, students' learning interest in the use of virtual labs is described which is integrated with the use of e-modules on colloidal material. Based on the results of data analysis, 74.55% of students were interested in using the virtual lab, 10.90% very interested, 12.73% quite interested, and 1.82% lack of interested. These data indicate that the majority of students can make good use of the virtual lab and are able to optimally elaborate on the learning process. However, virtual labs are not intended to replace real laboratories but can be used as supplements and media to support learning in real laboratories.
Development of a Set of Requirements for the Hardware and Software of LMS Services of the University

This article considers the issues of expert research to determine a set of require-ments for the hardware and software of the key LMS services of the university. Expert questionnaires have been developed to determine categories, including in-dicators of the functioning of the University's LMS services. Based on the devel-oped questionnaires, a survey of various user groups was conducted: developers, administrators, teachers and students. Based on the processing results, the final set of requirements for the LMS reference model was determined
Towards Region Queries with Strong Location Privacy in Mobile Network

With the interaction of geographic data and social data, the inference attack has been mounting up, calling for new technologies for privacy protection. Although there are many tangible contributions of spatial-temporal cloaking technologies, traditional technologies are not enough to resist privacy intrusion. Malicious attackers still steal user-sensitive information by analyzing the relationship between location and query semantics. Reacting to many interesting issues, oblivious transfer (OT) protocols are introduced to guarantee location privacy. To our knowledge, OT is a cryptographic primitive between two parties and can be used as a building block for any arbitrary multiparty computation protocol. Armed with previous privacy-preserving technologies, for example, OT, in this work, we first develop a novel region queries framework that can provide robust privacy for location-dependent queries. We then design an OT-assist privacy-aware protocol (or OTPA) for location-based service with rigorous security analysis. In short, the common query of the client in our solution can be divided into two parts, the region query <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>q</mi> </mrow> </msub> </math> and the content query <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>q</mi> </mrow> </msub> </math> , to achieve location <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> -anonymity, location <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>m</mi> </math> -diversity, and query <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>r</mi> </math> -diversity, which ensure the privacy of two parties (i.e., client and server). Lastly, we instantiate our OTPA protocol, and experiments show that the proposed OTPA protocol is reasonable and effective.
A New Method of Matrix Decomposition to Get the Determinants and Inverses of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>r</mi> </math>-Circulant Matrices with Fibonacci and Lucas Numbers

We use a new method of matrix decomposition for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>r</mi> </math> -circulant matrix to get the determinants of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mtext>Circ</mtext> </mrow> <mrow> <mi>r</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </mfenced> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msub> <mrow> <mi>B</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mtext>Circ</mtext> </mrow> <mrow> <mi>r</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </mfenced> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </math> is the Fibonacci numbers and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </math> is the Lucas numbers. Based on these determinants and the nonsingular conditions, inverse matrices are derived. The expressions of the determinants and inverse matrices are represented by Fibonacci and Lucas Numbers. In this study, the formulas of determinants and inverse matrices are much simpler and concise for programming and reduce the computational time.
Rethinking Covid-19 Lockdown-policy Interim Influence on African Economies

The Coronavirus disease (COVID-19) outbreak brought the world to a standstill having triggered a global lockdown of movements and economic activities.Governments were compelled to enforce social distancing policies comprising practices meant to minimize physical contacts to a reasonable extent.These helped in containing the spread of the pandemic in Africa, as elsewhere.The lockdowns jeopardized the flow of trade across the globe, which particularly spelt panic in Africa, where short and long-term impact on the economies could be more devastating.Against this backdrop, the paper attempts to determine the extent of damage COVID-19 lockdown saga has inflicted on the hitherto impoverished African economies and the way forward.The analytical framework for the paper is anchored on the dependency theory while the secondary source of data collection and analysis was employed.It found that after the COVID-19 lockdowns, most African economies have declined further than they were before the outbreak of the disease.As a remedy, African states should strive to adopt stringent and viable strategies to reconstruct their economies by cutting down on costs and frivolous spending.Furthermore, the adoption of prudent revenue drive is advocated coupled with encouraging local and foreign direct investments, in order to stay afloat.
Analyzing Architectures for Neural Machine Translation using Low Computational Resources

With the recent developments in the field of Natural Language Processing, there has been a rise in the use of different architectures for Neural Machine Translation. Transformer architectures are used to achieve state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such setups consisting of high-end GPUs and other resources. We train our models on low computational resources and investigate the results. As expected, transformers outperformed other architectures, but there were some surprising results. Transformers consisting of more encoders and decoders took more time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively less time to train than transformers, making it suitable to use in situations having time constraints.
Enhancing the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques

To know the information from the internet searching is one of the most important part for any user. In case of ‘Syntactic Search’ keyword based matching technique is used. Search accuracy is improved applying the filter like location, preference, user-history etc. However, it can happen that the user query or question and the best available answer or result in the internet domain has no terms in common or ignorable number of terms is common. In such case syntactic search cannot give the desired output. The role of ‘Semantic Search’ becomes prevalent in this scenario. The execution of semantic search faces challenge due to unavailability of resources like WordNet, Ontology, Annotation etc. An end to end algorithm is described to improve the accuracy of the semantic search in this work. Four classification techniques are used. They are ANN, Decision Tree, SVM and Naïve Bayes. Dataset is provided from the TDIL project of the Ministry of Electronics and IT, Govt. of India. The repository contains 86 categories of text having more than a million sentences. After getting the impressive result for the Bengali language test run was done for other Indian languages and a very good result is achieved. This research is extremely useful for the automatic question answering system, semantic similarity analysis, e-governance and m- governance.
Online Education and Engineering Students' Perception of Pedagogical Quality, in Learning the Process Control Course with MATLAB

The objective of this article is describe the results obtained from the evaluation of perception in engineering students, regarding pedagogical quality, in the context of online education; during the learning process of the process control course with Matlab. When developing the research, it was determined with respect to the answers capacity factor, that the indicators that present a better perception are "When presenting an observation about the development of the subject, the teacher responds to it appropriately" and "When you have any question or concern, the teacher answers your query quickly”, which have a total agreement of 82.4%. Regarding the Empathy factor, the indicator that presents a better perception is “The time in which the subject is taught is convenient for all students”, which presents a total compliance of 72.7%. Regarding the indicators that show the quality of the pedagogical service in general, it was determined that 75.7% perceive that teachers are always willing to help them and 81.8% perceives that teachers understand the specific academic needs of their students.
Cryptography: Security and Integrity of Data Management

The internet always deals with data. Basically, data is any type of digital information that has been stored in the servers. With the advancement of technology, billions of tons of data have been accumulated over the internet. Protection of these data is very crucial as; theft of these data will lead to damages beyond imagination. Data Security is the protection of these data and privacy, which prevent hackers from unauthorized access to computers, applications, and data servers. Data security is achieved by implementing Cryptography. Cryptography is a technique in which data is encrypted and stored in the databases so that if by chance anyone gets access to these data, it will of no use to them. There are many cryptography algorithms that are widely used to encrypt and decrypt data, some of them are the AES algorithm, stream ciphers, DES algorithms, etc. The concept of cryptography is not only limited to text encryption but also has a wide application in visual cryptography, watermarking, steganography, etc. Now a day’s security of communication has become very important, especially after people have started using internet banking. Everything on the world wide web is all about confidentiality, integrity, and authentication.
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

Recently, a new trend of exploring sparsity for accelerating neural networktraining has emerged, embracing the paradigm of training on the edge. Thispaper proposes a novel Memory-Economic Sparse Training (MEST) frameworktargeting for accurate and fast execution on edge devices. The proposed MESTframework consists of enhancements by Elastic Mutation (EM) and Soft MemoryBound (&S) that ensure superior accuracy at high sparsity ratios. Differentfrom the existing works for sparse training, this current work reveals theimportance of sparsity schemes on the performance of sparse training in termsof accuracy as well as training speed on real edge devices. On top of that, thepaper proposes to employ data efficiency for further acceleration of sparsetraining. Our results suggest that unforgettable examples can be identifiedin-situ even during the dynamic exploration of sparsity masks in the sparsetraining process, and therefore can be removed for further training speedup onedge devices. Comparing with state-of-the-art (SOTA) works on accuracy, ourMEST increases Top-1 accuracy significantly on ImageNet when using the sameunstructured sparsity scheme. Systematical evaluation on accuracy, trainingspeed, and memory footprint are conducted, where the proposed MEST frameworkconsistently outperforms representative SOTA works. A reviewer strongly againstour work based on his false assumptions and misunderstandings. On top of theprevious submission, we employ data efficiency for further acceleration ofsparse training. And we explore the impact of model sparsity, sparsity schemes,and sparse training algorithms on the number of removable training examples.Our codes are publicly available at: https://github.com/boone891214/MEST.