Reviewerly/exHarmony huggingface dataset:
Utilization of Polypropylene Plastic Waste for Visual and Textural Effects in Wall-Tile Making

This research is an experimental process of polypropylene (PP) plastic material in search for novelty values in the form of physical properties, textures and visual effects for decoration design. This research is based on the availability of plastic material as a choice of recycled material in the design of functional products. The purpose of this research is to produce decorative products that have the character of the visuals. The result of this research is a wall tile from a coaster combination from the plastic recycling process. The design has a novelty in the form of the application of the plastic material recycling process and visual effects in the form of color and texture. Materials for plastic processing can come from plastic bottles, plastic bottle caps, gallon caps, and the rest of production from large-scale industries. Experiments were melting out the plastic ore using a semi-machinal method that requires heat and metal molding and then semi-machinal processed using an injection molding machine to produce a wall-tile product. In this case, the author uses an experimental method through material exploration to find novelty values and see opportunities from exploration results in the form of decoration products.
The Position and Importance of Earthquake Education in the World

Earthquake education is significant for students to raise awareness and improve knowledge related to earthquakes. Literature on earthquakes emphasizes that people with high awareness and increased knowledge can effectively cope with the devastating effects. At this point, traditional teaching methods, such as drills and theoretical lessons, are inadequate, but innovative methods like virtual reality (VR) technology is more promising. However, there is no consensus on the ideal type of earthquake education. In the world, earthquake education significantly differs. Even though some countries are located in regions that are prone to earthquakes, they do not consider earthquake education in schools. On the other hand, several countries include earthquake education in school curricula. Existing literature demonstrates the insufficiency of current earthquake education in countries like Mexico and Israel. Students have deficiencies in knowledge related to the earthquake in spite of the implementation of earthquake education in schools. Reviewing different countries implementing earthquake education allows us to create an ideal type of earthquake education. Therefore, this review aims to investigate earthquake education in different countries.
A Research of Improved Facial Expression Recognition Method Based on CBAM and Joint-normalization Techniques

As a technology of great practical value, human facial expression recognition is developing rapidly in recent years. However, it requires a large, complex and diverse training set to develop an excellent facial expression recognition network or model. During the training procedure, imbalanced sample size may ruin the accuracy and generalization ability of recognition networks. Although the deep learning technology improves generalization ability of traditional methods that rely on manual feature extraction, there are still some problems such as network overfitting. To address the problems above, this paper proposes a facial expression recognition method that composed of joint-normalization strategies and Convolutional Block Attention Module (CBAM). The method can adaptively evaluates the importance of each channel's features to enhance key feature attention and recognition sensitivity of network. The method has achieved an accuracy of 73.73%, 73.92% and 95.6% respectively tested on FER2013, FER2013-Plus and CK+ training set, outperforms traditional mainstream algorithms.
IT Risk Analysis Based on Risk Management Using ISO 31000: Case study Registration Application at University XYZ

XYZ University has an Alpha Application used during the registration process for student courses each semester. There must be a hazard in the installation of this information system that could impede the process or functionality of the system; therefore, risk analysis is necessary to avoid troubles. By implementing ISO 31000 at XYZ University, it is believed that the risks associated with implementing Alpha can be reduced. This study uses Fishbone Analysis to identify the possible causes of an issue and then separates the root causes. It can also identify potential solutions to this problem (could be more than one problem). According to ISO 31000, risk analysis results are presented as documentation of potential hazards around the Alpha application, with risks grouped based on their impact. The results of this research will be included in the risk evaluation matrix per ISO 31000 recommendations. The evaluation matrix is divided into low, medium, and high-risk categories. So that universities can take advantage of the findings of this risk analysis to prevent and reduce risks and treat them based on the potential impact or risk priority in the Alpha application. Strengthening internal security systems, password policies, encryption, two-factor authentication, and educating students are some ways to mitigate these risks.
Adaptive Motion Compensation and Quantized Embedded Watermarking Against Collusion Attacks

Digital watermarking was an important method for video copyright protection, but it may face inter-frame linear attack in two kinds of collusion scenarios. By constructing collusion model, simulating attack principle and analyzing noise source, a hybrid resistance strategy and evaluation criterion based on adaptive motion compensation and adaptive quantization embedding were proposed. The experimental results show that the adaptive motion compensation algorithm has stronger convergence and modulating inter-frame redundancy than pixel and block motion compensation algorithms, the average reduction of PSNR is 28.76%, and the average amplitude of PSNR is 5.67%, which is significantly better than that of the contrast method, which is 46.45%, 37.36%, and 16.33%, 10.81%, respectively. The Peak signal-to-noise ratio of PSNR is higher than that of the contrast method, it is shown that the proposed method balances robustness and concealment while improving the capacity of embedding. In conclusion, the proposed method meets the evaluation criteria and is superior to the comparison method, which ensures that the collusion rules can not be obtained by using data redundancy and the collusion rules can implemented by the collusion method.
Access Control Management of Information System based on Homomorphism and Blockchain Technology

In the centralized user rights management scheme, the information system is faced with the threat of overstep access and malicious tampering of sensitive data such as key operation records, and it is difficult to ensure the auditability. At the same time, although encryption can be adopted to ensure the security of sensitive data, it will cause difficulties in data operation and access. This paper presents a method to build a user rights management scheme using blockchain, and combined with the research of identity-based homomorphic encryption, proposes an information system access control management scheme based on homomorphism and blockchain technology and a method to build a sensitive information storage scheme to improve the storage and operation security of sensitive data. To achieve unified and standardized management of information system user operation behaviors, complete the decentralized, untampered, traceable user rights and operation data recording mode.
Elevating Data Quality in University Procurement: Unveiling the Potential of e-Procurement Processes – A Case Study of Open University

Procurement is the process of meeting or providing needs between organizations carried out by two roles: buyers and sellers with the aim of buying goods or services from sellers that are done with an agreement process in the form of documents. Procurement of goods and services of the Indonesian government is regulated in Presidential Regulation Number 54 of 2010 concerning the process of implementing electronic procurement of goods / services which is the forerunner of the eProcurement process in Indonesia. The implementation of Electronic Procurement Services (LPSE) is regulated in LKPP Regulation Number 10 of 2021 and Presidential Regulation Number 16 of 2018 and its amendments (Perpres 12/2021) in article 75. the flow of goods / services procurement using LPSE based on Presidential Regulation of the Republic of Indonesia Number 54 of 2010: Login/Register, Fill in Identity, Search for Auctions, Follow the Auction, Bidding, Verification, Determination of Winners, Refutation/Appeal, Contact. In accordance with the LPSE process flow, this research compares the procurement process flow at the Open University, namely the Promise Open University application. The results of the comparison found a difference in the process flow, namely there is no protest / appeal process in Promise Open University. This is also included in the principle of procurement of goods and services, namely fairness.
Handbag Product Design from Cordura Fabric Waste (upcycling fashion) Through Material Exploration Approach

This research is a continuation of the previous series of Design Projects, namely the design of bag products from cordura fabric waste, but this research is focused on handbag products. the approach used in this study is material exploration so that the focus of the research is in the form of product creations produced from selected explorations, this research is motivated by the opportunity of no utilization of cordura fabric waste produced by CV Akhtar Bag Makers and the continuation of previous projects that have not been optimal. This design goes through a different exploration process than before, namely by giving different treatments such as sewing, heating, and steaming. The method used in this design is design by doing, and at the end of the research an output was produced in the form of a prototype of handbag products from cordura fabric waste or upcycling fashion that has a selling value. The novelty of this study is that there are no handbag products from cordura fabric waste that go through the exploration process carried out.
Traditional Games as A Learning Strategy to Develop Students' Self-Control

Self destructive behavior and that of others can result from low self-esteem self-control ability or self-control in students. On the other hand, good self-control can influence student learning outcomes and achievements.Self control itself includes 3 aspects including behavioral control, cognitive control, and decision control.One way that can be done to develop students' self-control is with game strategies, one of which is traditional games, where traditional games are now rarely found.The purpose of this study was to conduct a literature review related to game strategies in developing students' self-control. This is a qualitative-library research. The researcher conducted research on the results of journal research related to traditional game strategies to develop students' self-control. Data and research data sources are in the form of ten studies with the theme of game strategies to develop students' self-control. Data sources were obtained from e-journals accessed from Google Scholar, ScienceDirect, and Elsevier. The sources sought were research results from 2020 to 2023. Data collection techniques used data collection techniques with the keywords game strategies and student self-control. Researchers collect data on research data obtained from the Google Scholar, Elsevier, and ScienceDirect with data analysis techniques using content analysis methods. Based on several research results it was found thatgame strategy can be used asmedium to developself control onstudentself-controlthis includesbehavioral control, cognitive control, and decision control.Based on the results of the research, it is hoped that teachers can continue to preserve traditional games which are now starting to be abandoned, as media used in learning.
Organizational and Technological Aspects of the Activities of the Convergent Editorial Office

In the article, the authors consider various aspects of the functioning of convergent media editorial offices. For this purpose, organizational and technological innovations were studied. They have become actively introduced into the practice of editorial activity in recent decades. The merger of several media channels into large media holdings led to the fact that on the basis of such editorial conglomerations, specialized Internet departments and services first began to emerge, and then independent multimedia editorial offices. In the course of the study, employees of convergent media were interviewed, who told not only about the principles of organizing the production process for the release of multimedia products, but also about the features of introducing new forms of management of the editorial team, about various planning methods, and finally, about the functional tasks of new media employees.
A Comprehensive Survey on Replay Strategies for Object Detection

Object detection is a task to expect the actual features and type of object in a scene. Development of Convolution Neural Network (CNN) gave rise to great advances in object detection. The most popular object detectors are Yolo and Faster RCNN (Region-based CNN). The primary limitation of these object detectors is lack of capability to continually gain knowledge of new objects in the dynamic world. Humans are born to learn continued knowledge while grasping the ability to keep the old knowledge. However, every deep network has a limited capacity to learn and cannot exactly replicate the way humans perform continual learning. This is primarily due to a phenomenon addressed as catastrophic forgetting which cannot retain the previously learnt data while learning a new task. The issue of continual learning extensively measured in image classification applications as these are essential in resolving object detection problems. Incorporating the continual learning strategies within the existing deep learning-based object detectors will be very useful in applications like retail, autonomous driving, and surveillance-related issues. Various recent research findings relate awareness refinement to limit the representation to hold older information. This rigid limitation is disadvantageous for learning an innovative familiarity. Among the various techniques that exist in the literature, replay-based approach is very close to the way humans perform continual learning to retain previous knowledge. This article surveys and analyzes the state-of-the-art replay techniques and compares them to identify the most suitable technique for object detection on edge devices.
Deep Spectral Feature Representations Via Attention-Based Neural Network Architectures for Accented Malayalam Speech—A Low-Resourced Language

This study presents a novel methodology for Accented Automatic Speech Recognition (AASR) in Malayalam speech, utilizing Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architectures, both integrated with attention blocks. The authors constructed a comprehensive accented speech corpus comprising speech samples from five distinct accents of the Malayalam language. The study was conducted in four phases, with each phase exploring different combinations of features and model architectures. In the first phase of the study, the authors utilized Mel frequency cepstral coefficients (MFCC) as a feature vectorization technique and combined it with Recurrent Neural Network (RNN) to model the accented speech data. This configuration yielded a Word Error Rate (WER) of 11.98% and a Match Error Rate (MER) of 76.03%. In the second phase, the experiment utilized MFCC and tempogram methods for feature vectorization, combined with RNN incorporating an attention mechanism. This approach yielded a Word Error Rate (WER) of 7.98% and a Match Error Rate (MER) of 82.31% for the unified construction of the accented data model. In the third phase, MFCC and tempogram feature vectors along with the LSTM mechanism were employed to model the accented data. This configuration resulted in a Word Error Rate (WER) of 8.95% and a Match Error Rate (MER) of 83.64%. In the fourth phase, the researchers utilized the same feature set as in phases two and three and introduced LSTM with attention mechanisms to construct the accented model. This configuration led to a Word Error Rate (WER) of 3.8% and a Match Error Rate (MER) of 87.11%. The experiment yielded impressive results, with a Word Error Rate (WER) of 3.8% and a Match Error Rate (MER) of 87.11%. Remarkably, the study demonstrated the effectiveness of the LSTM with attention mechanism architecture, showcasing its ability to perform well even for unknown accents when combined with the appropriate accent attributes. The evaluation of performance using Word Error Rate (WER) and Match Error Rate (MER) showed a significant reduction of 10–15% when incorporating attention mechanisms in both the RNN and LSTM approaches. This reduction indicates the effectiveness of attention mechanisms in improving the accuracy of accented speech recognition systems.
Synergizing Voice Cloning and ChatGPT for Multimodal Conversational Interfaces

Conversational AI systems have gained a lot of attention in recent years because they are capable of interacting with users in a natural and an emotional way. Designing a personalized and human-like chat experience remains a difficulty. We introduce a paper that delves into the possibilities of bringing together two technologies, voice cloning and ChatGPT, to create more seamless, natural, and intriguing multimodal conversational interactions. Voice cloning is the process of replicating the voice of a user. ChatGPT, on the other hand, provides contextual and human-like text-based responses. With the help of these two technologies, we are creating an intuitive and natural conversational experience that better reflects the user’s communication style. From the analysis of our proposed system, we find that this model highly improves the conversational AI. This research provides valuable insights into the potential of multimodal dialogue networks and opens the door for further innovations in the field.
A CNN-Based Self-attentive Approach to Knowledge Tracing

A key component in modern education is personalized learning, which involves the difficult challenge of precisely measuring knowledge acquisition. Deep knowledge tracing (DKT) is a recurrent neural network-based method used for this problem. However, the predictions from DKT can be inaccurate. To address this, a mathematical computation model called convolutional neural network (CNN) is leveraged to understand the DKT problem better. Our model identifies applicable knowledge concepts (KC) and predicts the student’s conceptual mastery based on them. SAKT, a method of self-attention that successfully manages data sparsity (sparse data representation), is introduced here. Our model outperforms modern knowledge tracing models, improving the average AUC by 5.56% through experiments on real-world datasets. This performance breakthrough has positive ramifications for personalized learning, improving the accuracy and efficacy of knowledge acquisition and ultimately resulting in more specialized and productive educational settings.
Detecto: The Phishing Website Detection

Phishing attacks are among the most prevalent types of cybercrime that target people and businesses globally. Phishing websites mimic real websites to obtain sensitive data of users like usernames, passwords, and credit card numbers. To identify phishing websites, many people employ machine learning algorithms. These algorithms use supervised learning techniques to classify websites into the phishing or legitimate categories. Machine learning algorithms use features such as URL length, domain age, SSL certificate, and content similarity to determine whether a URL is real or fake. In recent years, authors have published papers working on the classification of websites with features by using a support vector machine and achieving 95% accuracy, and also they classify phishing websites by using a URL identification strategy or utilizing the random forest algorithm. The dataset contains a collection of URLs of 11,000+ websites. Each has 30 parameters and a class label identifying as a phishing website or not. To achieve the highest level of accuracy, we suggested a model using 32 features extracted from phishing websites and various machine learning classifiers. Every website has distinct characteristics that are categorized by trained models. We achieved 97.4% accuracy using 7 classifiers, including Naïve Bayes, logistic regression, random forest, decision tree, and gradient boosting algorithm.
Ensemble Approach for Suggestion Mining Using Deep Recurrent Convolutional Networks

The ability to extract valuable information from customer reviews and feedback is crucial for businesses in today’s social media landscape. Many companies and businesses use social media networks to offer and deliver a range of services to their customers as well as gather data on individual and customer opinions and thoughts. An efficient method for automatically obtaining creative concepts and suggestions from web sources is suggestion mining. For suggestion mining, we present in this paper an ensemble model called DRC_Net that integrates deep neural networks, recurrent neural networks, and convolutional neural networks. We evaluated our model using the SemEval-2019 dataset containing reviews from multiple domains. Our proposed model achieved better accuracy and F1-score than state-of-the-art models and performed well on Subtask A and Subtask B, representing in-domain and cross-domain validation. For Subtask A and Subtask B, the model receives F1-scores of 0.80 and 0.87, respectively. The model’s ability to perform well on cross-domain validation suggests that it can be applied to various domains and datasets.
Improving Tree-Based Convolutional Neural Network Model for Image Classification

In recent years, convolutional neural networks (CNNs) have shown remarkable success in image classification tasks. However, the computational complexity of these networks increases significantly as the number of layers and neurons grows, making them computationally expensive and challenging to deploy on resource-limited devices. In this paper, we propose a novel CNN architecture based on a tree data structure to address the computational complexity of standard CNNs. The proposed model has each node in the tree representing a convolution operation that extracts spatial information from the input data. The primary objective of our work is to design a computationally efficient model that delivers competitive performance on the CIFAR-10 dataset. Our proposed model achieved a test accuracy of 81% on the CIFAR-10 dataset, which is comparable to previous work. The model’s training time is also significantly lower than the standard CNNs, and it uses fewer parameters, making it easier to deploy on resource-limited devices. Our work offers a promising direction for designing efficient and effective deep neural networks for image classification tasks. The proposed CNN architecture based on a tree data structure provides a novel approach to address the computational complexity of standard CNNs while maintaining competitive performance levels. Additionally, our work improves upon previous tiny models by addressing their shortcomings and achieving comparable performance levels while being more efficient. Our proposed model is suitable for deployment on resource-limited devices, such as mobile devices and edge computing devices.