Reviewerly/exHarmony huggingface dataset:
Faktor Penentu Suksesnya Pengajaran Online Pada Masa New Normal

Before the pandemic, teaching and learning activities were carried out offline with the guidance of teachers. However, after the Covid-19 pandemic, all teaching and learning activities and extracurricular activities were carried out online. In implementing online learning activities, Pedungan State Elementary School 11 can carry out online learning activities successfully, so it is necessary to study what factors influence the success of online teaching. This research was conducted to find out what factors influence the success of online teaching during the New Normal period at 11 Pedungan State Elementary School. This study uses a qualitative method where this study contains a description of the factors that influence the success of online teaching in the new normal period at 11 Pedungan State Elementary School. Informants in this study were principals, teachers, and parents of students using incidental techniques. The factors that influence the success of online teaching during the new normal period at the 11 Pedungan State Elementary School are as follows: (1) Teacher readiness. Teachers are very ready and have the ability to use information technology. (2) School Readiness. The school has well facilitated this online teaching and learning activity. Such as the internet, computers, laptops, cellphones, special IT rooms. (3) Student Readiness. The students have followed the online learning activities well. according to the schedule determined by the teachers. (4) Readiness of Parents of Students. Parents of students have provided guidance and supervision to their sons and daughters so that online learning can run well.
Facial Based Age Estimation Among Dark Skinned Hominid Species

By intuition humans can estimate the age of an individual effortlessly. However, this task can be very challenging for face age recognition system. These challenges primarily are attributed to the unavailability and the under-representation of appropriate dataset used in the training of age estimation models. A specific scenario is the underrepresentation of black race which raise the concern of algorithm accountability resulting in inefficient model formulation. In this study, a dataset named Faces was curated from secondary sources containing face images of the black race. A hybrid model consisting of a KNN classifier and an SVR was then proposed to estimate these ages. For a fair comparison, the trained hybrid model was experimented on three other datasets and compared to the LARR model which was the state-of-the-art model at the time of study. The results showed that the proposed model generated the least MAE on the Faces dataset which affirms our hypothesis that a model works better with the kind of data used curated.
Automatic matching of multi-scale road network under the constraint of small-scale road mesh

Aiming at the problem of road network multi-scale matching, a multi-scale road matching method under the constraint of road mesh of small-scale data has been proposed. First, two road meshes with different scale data are constructed; Secondly, under the constraint of the small-scale road mesh, the composite mesh composed of several road meshes in the large-scale road is extracted, and the mesh matching with the small-scale road mesh is completed; Then, many-to-many matching of road meshes with different scales is realized; finally, the matching relationship between composite mesh and small-scale road mesh is transformed into the matching between multi-scale road mesh boundary roads and internal roads, and the matching of the whole road network is completed. The experimental results show that this method can better realize the matching of multi-scale road network.
EFFECTIVE FILTERING PROCEDURES FOR MULTI-CRITERIA SELECTION OF TRANSPORTATION SERVICE PROVIDER

The report presents an approach to formalizing the procedures for filtering alternatives in models of multi-criteria choice of a transport service provider in the case of horizontal cooperation in supply assurance. Filtering procedures are formalized on the basis of binary relations, and only alternatives that correspond to majorants in the corresponding strict order relation are kept. This will significantly reduce the number of considered alternatives without compromising the quality of the chosen solutions. Numerical illustrations are given in the format of the following generalized selection criteria: scalar, geometric mean, ideal point.
Academic Survival Experiences of the Students during the COVID-19 Pandemic

This qualitative research study aimed to determine the effects of the COVID-19 pandemic to the college students? lives in Ilocos Sur Polytechnic State College Cervantes Campus, specifically towards their academic survival. It was conducted to produce an action plan in strategizing modes of instructional delivery that will cater on the needs of students for significant learning experiences in this new normal. Findings reveal that students experienced both Personal and Environmental challenges in meeting their subject requirements. Personal challenges include difficulty in adjustments and in comprehension of subject matters as well as their personal struggles. Meanwhile, environmental challenges include time restrictions; and overload requirements per subjects; lack of resources; inconsiderate teachers and poor internet connections. To cope with the challenges, the students employ Internal and External adaptive coping mechanisms. Internal includes keeping their self-motivations and responsibility. External includes seeking assistance from others, finding a suitable place for learning, earning extra income, and clinging on to God. Positively, the students have developed their intrapersonal and interpersonal relationships. With themselves, they have developed values solidifying their character besides enhancing their digital and entrepreneurial skills. With others, they have realized the importance of teachers, have spent more time for family, have built genuine relationships, and have deepened their faith. Meanwhile, the students have recognized the extended help by the teachers in terms of digital instructions, adjustments, and moral supports. However, they wished to have limited face to face instructions, online classes, mental health aid, financial assistance and strong internet connectivity.
Pengembangan Multimedia Interaktif E-Modul Menggunakan Kvisoft Flipbook Maker dan Kinemaster dengan Model Anchor Instruction pada Materi Genetika

The development of interactive electronic multimedia modules is the development of electronic book teaching materials in which there are text, image, audio, video, and graphics. Anchor instruction is a learning model that trains students to be able to solve problems presented through videos with certain steps. The kvisoft flipbook maker and kinemaster applications are supporting applications for creating electronic multimedia modules based on the anchor instruction model. The purpose of this research is to develop and know the exact multimedia by using the ADDIE development model (analysis, design, development, implementation, evaluation). Where the development has reached the development stage. The multimedia development stage has been validated for linguists, material experts, media experts, supporting lecturers, and a readability test was held for students. The results are then analyzed to determine the suitability of multimedia in the hope of providing interactive multimedia that is interesting for students.
Cyber Eyes Wide Open: Creative Collaboration between Artists, Academics &amp; Cyber Security Practitioners

This paper discusses a collaboration between creatives, artists, academics, cyber security professionals and companies to create artworks that increased awareness of cyber security.The aim was to offer creative perspectives and practices into how cyber security might be better understood through creating visualisations, experiences and narratives that could enable communication and insight.4 cyber security businesses each provided a theme resulting in vibrant collaborations between the companies, 4 academics and 8 artists.11 projects and 42 pieces of art were developed and exhibited in two exhibitions in the Northeast where over 100 people viewed the works.Evaluation indicated that the artworks had an impact, with people more aware of cyber security.This project has highlighted the opportunities created through collaborations among artists, academics and cyber security practitioners in facilitating the understanding of complex technical issues.
CATEGORIES OF USER IMPAIRMENT

This work presents a novel classification scheme for human impairments and aims at being as small as possible while simultaneously representing the vast diversity of users with impairments with sufficient accuracy.The scheme is based on the International Classification of Functioning, Disability, and Health of the WHO and consists of in total 21 impairment categories which are broadly grouped into the four main areas cognition, senses, voice, and movement.The classification scheme is of generic nature and can thus be applied to the design of human artifacts and environments in general, as it considers the plain interaction of the human body with its surroundings in multiple modalities.Simulation of user impairment is a primary application area.
An efficient scalar multiplication algorithm on Koblitz curves using τ&lt;SUP align="right"&gt;3&lt;/SUP&gt;-NAF

Elliptic curve cryptography (ECC) is an efficient and widely used public-key cryptosystem. It uses relatively shorter keys compared to conventional cryptosystems hence offering faster computation. The efficiency of ECC relies heavily on the efficiency of scalar multiplication which internally depends on the representation of the scalar value. Based on the representation, the number of point additions and point doublings varies. Koblitz curves are binary elliptic curves defined over F2 and also known as anomalous binary curves. Scalar multiplication algorithms on these curves can be designed without any point doublings. In τ-NAF representation, we need 0.333 m point additions whereas in τ2-NAF it is 0.215 m. This paper proposes a method to improve the efficiency of scalar multiplication on Koblitz curves using τ3-NAF representation that further reduces the point additions to 0.143 m.
Channel Attention Mechanism-based Multi-Feature Fusion Network for Specific Emitter Identification

In this paper, a channel attention mechanism-based multi-feature fusion network MF-RESNET is proposed for specific emitter identification (SEI). MF-RESNET takes the raw signal as input. Firstly, several traditional signal features, such as bispectrum, cycle spectrum and power spectrum, are achieved. Simultaneously, a residual neural network is utilized as the feature extraction network to extract the intrinsic characteristics from the raw signal. Then, the traditional signal features and the intrinsic features extracted by the residual neural network are fused based on the channel attention mechanism, where the potential radio frequency fingerprint (RFF) features are automatically exploited. Finally, the RFF features are sent to the simple CNN classifier network to output the probability of the category. Experimental results indicate that the proposed MF-RESNET can achieve 92.25% identification accuracy on automatic identification system (AIS) steady-state signal with 10 classes.
Design of China Crude Oil Import Knowledge Graph

Knowledge graph is a powerful technique to describe entity relationship. To better express the relation between supply and receiving ports of China import oil trade; this paper proposes a good solution to create a China crude oil import knowledge graph. This method explores extracting corresponding knowledge values from multi-source heterogeneity raw materials, and building the knowledge dataset after data preprocessing, dataset creation and graph entity creation steps. CQL's batch commands are employed to construct automatically oil graph database with correct parameters based on export ports, import ports and relation CSV files. Finally, an application is developed to retrieve knowledge data to make flexible visualization and analysis on the demand. The result will contribute much to extreme event that influence China oil supply inference future.
Rule based model for pneumonia (COVID-19) nursing care

This research was conducted during the COVID-19 pandemic, when during the pandemic, many patients died.The mortality rate was caused by complications in the form of pneumonia in patients with deteriorating health conditions.This study aims to develop an inference model to become a decision support system in the enforcement of the clinical pathway of pneumonia COVID-19 nursing care.This research model is based on the application of NANDA International nursing diagnoses to determine the objectives of the Nursing Outcome Classification (NOC) and the interventions that must be carried out by the Nursing Intervention Classification (NIC).The data in this study were obtained from the results of expert interviews regarding the handling of pneumonia and medical literature on handling COVID-19 cases.The results of this study can guide the diagnosis and treatment of pneumonia caused by the COVID-19 virus, as well as a similar process that occurs with acute respiratory distress syndrome.
An Integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph

Due to the increasingly serious problem of information overload, it is difficult for medical staff to accurately screen out data conducive to patient consultation when dealing with massive amounts of diagnostic information. In this paper, we propose a hybrid recommendation mechanism integrating the improved Singular Value Decomposition and Knowledge Graph for medical information. It can effectively analyze the characteristic relationship between patients and medical item information and give appropriate medical recommendation scheme information based on patients’ interests and preferences. We design the Promote Product Singular Value Decomposition (PPSVD) algorithm and integrate knowledge graph technology to construct the medical system’s recommendation model. Finally, the experimental results show that the integrated medical recommendation model proposed has higher accuracy and usability than any other algorithms.
Study of Cyber Security Effects on Wireless Sensors Networks

In recent two years, information systems have been adopted in most fields of life due to the health state around the world. At the same time, the risk factor of security attacks is increased sharply as well. These attacks consider different actions toward damaging the data of systems or even making down their work. In this paper, a study of cyber-threats (attacks) on Wireless Sensor Network (WSN) is presented. This study illustrates the effects of the cyber-threats on the WSN according to the network layers, as well as their privacy concerns. The outcome of this study is the classification of these attacks that can lead to produce cyber-security systems which can prevent them from damaging the involved information systems. Index Terms— Cyber-Security, Wireless Sensor Network, Internet of Things.
Incorporating Natural Language Processing models in Mexico City's 311 Locatel

Natural Language Processing based technologies are transforming various sectors by facilitating new ways of providing services through Artificial Intelligence (AI). In this paper, we describe the methodology and present the challenges encountered during the creation of a Deep Learning-based model for classifying citizen service requests. Our system is able to effectively recognize among 48 categories of public services with an accuracy of 97% and was integrated into Mexico City’s 311, significantly increasing the government’s ability to provide better services.
Research on Application of Deep Learning Algorithm in Earthquake Noise Reduction

For the traditional denoising method, it is necessary to accurately model the signal and noise and optimize and adjust the manual input parameters, which causes the problem that it is difficult to remove the noise of the seismic data.Especially random noise in remote areas has the characteristics of non-stationary, high energy, and serious aliasing of effective signals and random noise in the frequency domain, which brings great difficulty to the recovery of seismic data by conventional denoising methods, but the existence of random noise seriously affects subsequent earthquakes.The processing and interpretation of data makes it difficult to determine the exact location of oil and gas.It is necessary to develop an efficient denoising algorithm, which can retain the complex edge information and rich texture information of the signal as much as possible while removing random noise, so as to restore the seismic data and improve the utilization rate of the seismic data.In this paper, the convolutional neural network is applied to seismic data processing.The algorithm has a good denoising effect in seismic data.While eliminating a large amount of random noise, it retains the texture features in the data, realizes denoising of seismic data, and enhances vision quality.
Understanding the Characteristics of Public Blocklist Providers

Websites can spread malware and phishing scams, and this represents a significant risk to users. To block such malicious websites, various organizations and individuals, e.g., security vendors, analyze URLs and create blocklists. Some blocklists are paid and some are free public blocklists; however, the effectiveness of public blocklists is generally not guaranteed. Thus, many studies have attempted to verify their effectiveness. To the best of our knowledge, public blocklist providers (PBP) have not been studied as rigorously as blocklists, and it is still unclear how blocklists should be operated and how PBP websites should be designed to maintain the effectiveness. Therefore, to unveil more characteristics of the PBP, we designed a measurement study to analyze PBPs in terms of lifespan, update frequency, entry bias, and user interface metrics. In this paper, we describe the results of measuring seven PBPs according to these four metrics.
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks

Recent isotropic networks, such as ConvMixer and Vision Transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic Convolutional Neural Networks. Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN). We present a framework to formalize major weight sharing design decisions and perform a comprehensive empirical evaluation of this design space. Guided by our experimental results, we propose a weight sharing strategy to generate a family of models with better overall efficiency, in terms of FLOPs and parameters versus accuracy, compared to traditional scaling methods alone, for example compressing ConvMixer by $$1.9\times $$ while improving accuracy on ImageNet. Finally, we perform a qualitative study to further understand the behavior of weight sharing in isotropic architectures. The code is available at https://github.com/apple/ml-spin .
Augmenting the detection of online image theft using diagonal transformation and iris biometric watermarking

The digitisation of data has brought in various social media platforms where a lot of data are compromised every day. Likewise, there is a possibility of theft for every photo published online. Hence, the research work proposes an image theft detection technique where biometric traits of the owner of the photo, are embedded into the image. This helps to prove rightful ownership in case of any ownership disputes on the image. The proposed architecture identifies the rightful owner by comparing the embedded traits from the claimed image with that of the person asserting ownership. The watermark imperceptibility is analysed and robustness against various attacks is proven. Also, the accuracy of the work is analysed in terms of the false acceptance rate and false rejection rate. By considering iris images taken from CASIA, and IIT-Delhi databases, the proposed approach achieved a better accuracy rate with a very low FAR and FRR.
Detection of Cyberbullying in Social Media to Control Users' Mental Health Issues Using Recurrent Neural Network Architectures

Cyberbullying is a kind of bullying that takes place via social media with digital gadgets.It may cause significant, long-lasting trauma and difficulties with mental and physical issues.This condition may also result in difficulties in personal lives.Because of this, detecting and reporting such offensive posts may help avoid the harmful repercussions of cyberbullying.Some studies are available in this context, and it was found that deep learning algorithms are more efficient in detecting abusive texts on social media.Some studies were carried out on deep learning algorithms.The issue was not solved, and more studies are needed to find the most efficient model to control this problem.It was observed from the literature survey that the performance of Recurrent Neural Network (RNN) architectures is good.Therefore, the objective of this study was to find and develop an efficient model to detect cyberbullying texts on social media.For this research, the Twitter dataset was used, which was uploaded by Munki Albright in Kaggle.The dataset consists suspicious, cyberbullying, hate and suicidal classifications.This research was focused on cyberbullying texts.Sentiment analysis was implemented to classify the cyberbullying posts.For that, sexism (Class 2), racism (Class 1) and either (Class 0) classification were analyzed.Researchers applied the LSTM, GRU and Bidirectional LSTM architectures of RNN to perform the sentiment analysis.All models gave somewhat similar results.When we consider f1, GRU shows a better F1 score for all the classes when compared to others (Class 0 was 95%, Class 1 -70%, and Class 0 was 56%).GRU shows the best results for this data set compared to other models.Based on the Accuracy, all three architectures were obtained at around 90%.However, GRU has outperformed the other two models in accuracy as well.After that, researchers developed an ensemble model using all three models.In the ensemble, each model has been given weight.Since the GRU is the best performing model, it was given 0.4, and the other two models were assigned 0.3 each.The ensemble model performed well in F1 measurement (57%) and accuracy (91.17%).When we compared the previous works against the proposed model, our ensemble model obtained the highest values and performed well in detecting cyberbullying in sentiment analysis.
Sentiment analysis of IPOT application reviews using naïve Bayes method

Investment conditions in Indonesia today are very crowded among the public, especially investors as investment actors. One of the investment applications used by the people of Indonesia is IPOT. IPOT is a trading application for stock investments, mutual funds, and Exchanged Traded Funds (ETF) with more complete features. The availability of IPOT application user reviews on Google Play is very helpful for companies to know the needs and wants of users about the application. These reviews can be analyzed using sentiment analysis to find out how users think about the application they're using. Therefore, the purpose of this study is to conduct sentiment analysis on IPOT application reviews using the Naive Bayes Classifier method. The results of this study were obtained that positive sentiment has a greater value compared to negative sentiment with the accuracy of the classification of test data has an AUC value of 67.80% while the training data has an AUC value of 70.13%.