Reviewerly/exHarmony huggingface dataset:
Modelling Sentiment Analysis on Indonesian Product Reviews Using Machine Learning

Insight from product reviews from customers is important messages to the business. It provides feedback to the business and enhances the customers’ experiences and leading to sustainable competitive advantage for the business. Sentiment analysis can be performed from the customers’ product reviews regarding to particular product or service. Capturing sentiment analysis can be done automatically using machine learning techniques. This research aims to explore several machine learning algorithms to model automatic sentiment analysis on Indonesian product reviews. The product reviews data was gathered from an e-commerce platform in Indonesia. All of the product reviews are in the local (i.e. Indonesian) language. The research contributes to a state of the art of automatic sentiment analysis model on Indonesian product reviews. Eight algorithms and 4507 settings are explored to find the best setting and algorithm to model sentiment analysis in Indonesian product reviews. The results demonstrate that the best model was achieved by the one trained with five layers of artificial neural network with 99.1% for testing model accuracy, precision, recall, and F1-score. The best AUC of the model was 99.6%. The model requires 34.68 minutes to be trained.
Development of Fabrication Techniques for Magneto-Optical Diffractive Deep Neural Networks

Magneto-optical diffractive deep neural network (MO-D2NN) is a type of diffractive deep neural network that utilizes the magneto-optical (MO) effect of magnetic materials. MO-D2NN is composed of magnetic material, which is non-volatile and allows neurons to be rewritten. In our work, we fabricated and evaluated MO-D2NN with two layers containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100\times100$ </tex-math></inline-formula> magnetic domains with a domain size of 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> . Nd0.5Bi2.5Fe4GaO12 thin films were prepared on both sides of a Gd3Ga5O12 substrate and magnetic domain patterns were recorded using the MO recording technique. The recording accuracies of the first and second layers were 79% and 70%, respectively. We believed that MO recording technology can achieve a recording accuracy of over 90% by optimizing the fabrication and recording conditions. The optical setup was built and handwritten digit classification was carried out using fabricated MO-D2NN, but the number of classifications was lower than in the case of simulation. We considered that recording errors were responsible for the low number of classifications and assessed the effect of recording errors on accuracy by simulation. When the recording error was lower than 10%, the loss in classification accuracy was found to be only ~3%. Therefore, we are convinced that MO-D2NN has high physical implementation potential.
Controllable image generation and manipulation

Recent years have witnessed an unprecedented interest in developing Deep Learning methodologies for the generation of images and image sequences that are hardly distinguishable to the human eye from real ones. A major issue in this field is how the generation can be easily controlled. In this talk we will focus on some of our recent works in generative models that are primarily aimed at controllable generation. We will first present unsupervised methods for learning non-linear paths in the latent spaces of Generative Adversarial Networks such that following different paths lead to different types of changes (e.g., removing the background, changing head poses, or facial expressions) in the resulting images [4]. Subsequently, we will present a method that allows local editing by finding a Parts and Appearances decomposition in the GAN latent space [2]. Then, we will present recent works on reenactment [1], where the goal is to transfer the facial activity (pose, expressions, speech) of a certain person to another one, and recent works in which supervision for generation comes from language models [3]. Finally, we will touch on the technical challenges ahead, as well on the challenges that this creates in spreading misinformation.
Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa

Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the tendency of an opinion, whether the opinion tends to be positive, negative, or neutral. Researchers or institutions can find out how the response and emotions of an issue are happening and make good decisions. With the large user of Twitter social media in Indonesia, sentiment analysis will be carried out using deep learning Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly Optimized BERT Pretraining Approach (RoBERTa), Synthetic Minority Over-sampling Technique (SMOTE), and Global Vector (Glove). In this research, the dataset used is trending topics with hashtags related to government policies on Twitter social media and obtained through crawling. By using 30.811 data, the result shows the highest accuracy of 95.56% using CNN with a split ratio of 90:10, baseline unigram, RoBERTa, SMOTE, and Top10 corpus tweet with an increase 10.1%.
EMOJIFIER

Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication. Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also led to increasing data science research dedicated to emoji-driven storytelling. With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. Here we will classify human facial expressions to filter and map corresponding emojis or avatars. Emojifier is software which deals with the classification of facial expressions, text and speech into Emojis or Avatars.
Evolutionary Algorithm in Game – A Systematic Review

Research in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further.
A Multi-Grained Attention Residual Network for Image Classification

Attention mechanisms in deep learning can focus on critical features and ignore irrelevant details in the target task. This paper proposes a new multi-grained attention model (MGAN) to extract parts from images. The model includes a multi-grain spatial attention (MSA) mechanism and a multi-grain channel attention (MCA) mechanism. We use different convolutional branches and pooling layers to focus on the crucial information in the sample feature space and extract richer multi-grain features from the image. The model uses ResNet and Res2Net as the backbone networks to implement the image classification task. Experiments on the CIFAR10/100 and Mini-Imagenet datasets show that the proposed model MGAN can better focus on the critical information in the sample feature space, extract richer multi-grain features from the images, and significantly improve the image classification accuracy of the network.
A novel adversarial example generation algorithm based on WGAN-Unet

The security of deep neural networks has triggered extensive research on the adversarial example. The gradient or optimization-based adversarial example generation algorithm has poor practicality and cannot combine high success rate and high efficiency; using GAN to generate adversarial examples has the problems of gradient disappearance and unstable generation. In this paper, we propose a novel adversarial example generation algorithm based on WGAN-Unet, which uses the structure of WGAN and Unet to form a generative adversarial network to improve the stability of network training, and uses the cosine loss function to measure the category loss and improve the success rate of adversarial attacks. The adversarial example generation using WGAN-Unet is compared with other algorithms in terms of human eye perception, time consumption, attack success rate, and image quality, proving our scheme’s superiority.
Tech majors could revitalise interest in metaverse

Headline INTERNATIONAL: Metaverse tech
Key points of knowledge graph construction technology for fault analysis

In the context of the new era, with the continuous development of social economy and information technology, the software and hardware coupling relationship of information system becomes more and more complex, and more and more information is stored in the market, which makes the information system fault more likely to occur and difficult to locate. According to this problem, the knowledge graph construction technology is applied to the information system construction, and the knowledge graph construction technology oriented to fault analysis is proposed, which can not only store and manage large quantities of data information, but also can dig the data rules deeply. Therefore, based on the knowledge graph construction technology, this paper explores the key points of knowledge graph construction technology for fault analysis based on the construction and application of power transmission information system. The final experimental results prove that the knowledge graph and construction technology can more intuitively show the association between faults and early warning, and accurately locate system faults according to the artificial intelligence algorithm, providing data basis for relevant departments.
Privacy leaks from seized US phones

Nude photos and bank details were found on devices sold by US police forces via auction site
The Degree of Availability of Digital Transformation Requirements in Qasaba Amman Schools from the Point of View of School Principals and Educational Supervisors

The current study aimed to identify the degree of availability of the requirements of digital transformation in the schools of Qasaba Amman from the point of view of school principals and educational supervisors.The indicators of their validity and reliability were verified, and the study sample consisted of (150) principals and (45) supervisors, who were chosen randomly.Infrastructure requirements are generally high, while for the rest of the requirements, administration, students, and the digital teacher, and digital content are generally medium, and it turns out that there are no statistically significant differences in the degree of availability of digital transformation requirements in Qasaba Amman schools from the point of view of school principals and educational supervisors due to gender, number Years of experience and current job, while the results showed that there are no differences attributable to educational qualification, and the study recommended that there should be efforts and measures that must be taken by the Ministry of Education, directorates, and schools to provide the requirements for digital transformation in public schools, such as providing the necessary training and qualification for principals, teachers, and supervisors to use Technology in education, and providing the necessary resources to improve the digital infrastructure in the directorates and schools.Which requires the integration of the efforts of the administration, teachers, students and parents to improve the level of digital transformation in schools and directorates at the level of the kingdom.
Disaster Risk Reduction Management: Basis in Crafting: A 3-Year Strategic Plan for Pili National High School

This study determined the extent of Disaster Risk Reduction Management (DRRM) of Pili National High School (PNHS), Magallanes, Sorsogon.The study looked into the level of preparedness of PNHS in terms of the localized existing policies related to school safety and the status of implementation of PNHS on the three pillars of Comprehensive DRRM in Basic Education Framework along safe learning facilities, school disaster management, and DRR in education.The findings of this study guided the researcher in crafting a 3-year strategic DRRM plan for PNHS for the calendar year 2023-2025.The purposive sampling method was used in selecting the participants of the study.They are the members of the local DRRM councils of Magallanes and the school and district DRRM teams.They examined and observed the DRRM implementation of PNHS using the adapted comprehensive school safety monitoring checklist.Data gathered were statistically analyzed and a 3-year strategic DRRM plan for PNHS was crafted.
Optimization of random forest algorithm based on mixed sampling additional feature selection

Because of the poor performance of the Random Forest algorithm in processing the classification of high-dimensional unbalanced data, a Hybrid Samping&Feature Selection Random Forest optimization strategy (Hybrid Samping&Feature Selection Random Forest (HF_RF) is proposed in this paper. First, from the data level, the high-dimensional unbalanced data set is preprocessed by SMOTE algorithm combined with random undersampling to achieve balanced unbalanced data. At the same time, the clustering algorithm is combined with SMOTE algorithm to improve the processing ability of the algorithm for negative samples; On the algorithm level, through the Relief F algorithm, different weight values are given to the preprocessed high-dimensional data, irrelevant and redundant features are eliminated, and high-dimensional data is reduced for dimensionality; Finally, the weighted voting principle is used to further elevate the predictive performance of HF_RF. The experimental results show that compared with the traditional algorithm, the proposed algorithm has higher indicators when dealing with high-dimensional unbalanced data, which proves that the HF_RF proposed in this paper is The correctness of the algorithm and its effectiveness in improving the classification performance of high-dimensional unbalanced data.
Design of Aero-engine Start-Up Fuel Control System Based on Particle Swarm Algorithm

Aero-engine is one of the most complex mechanical equipment that can be built by the industry at present. Its complex structure, nonlinear dynamic characteristics and complex and diverse engineering technologies required for normal operation can be called the Mount Everest in the industry. As the power source of aircraft, aero-engine affects the performance, economy and reliability of aircraft, and has important strategic and economic significance. The purpose of this paper is to design the fuel control system for aero-engine starting based on particle swarm optimization. In the experiment, the algorithm of combustion chamber is used to analyze and study the law of steady state fuel control system of aero-engine.
Data Virtualization Layer Key Role in Recent Analytical Data Architectures

The amount of data, its heterogeneity and the speed at which it is generated are increasingly diverse and the current systems are not able to handle on-demand real-time data access. In traditional data integration approaches such as ETL, physically loading the data into data stores that use different technologies is becoming costly, time-consuming, inefficient, and a bottleneck. Recently, data virtualization has been used to accelerate the data integration process and provides a solution to previous challenges by delivering a unified, integrated, and holistic view of trusted data, on-demand and in real-time. This paper provides an overview of traditional data integration, in addition to its limits. We discuss data virtualization, its core capabilities and features, how it can complement other data integration approaches, and how it improves traditional data architecture paradigms.
Comparative Analysis of Machine Translation (MT) and Computer Aided Translation (CAT)

Since the emergence of MT, its fast translation speed and declining cost have been favored by the whole society. In order to make MT more useful, computational linguists have been committed to improving the accuracy of MT. By discussing the advantages and disadvantages of computer-aided translation and the advantages and disadvantages of MT, this paper compares the changes of translation accuracy, recall rate and word reasoning ability under different sentence lengths under different translation modes of MT, computer-aided translation and human translation through the test of machine frame model algorithm. The results show that in the length range of 0–10, the translation accuracy of the three translation models has reached more than 85%, which shows that the additional non top-level information of the encoder has an obvious gain for the model in translating shorter sentences; In the length range of 10–20, the translation accuracy does not decrease significantly; In the range of 30–40, the accuracy of sentence translation begins to decline significantly, especially in MT. With the increase of sentence length, the accuracy decreases to 70% in the range of 40–50; The decline of computer-aided translation is not obvious, and the accuracy is 79% in the length of 40–50; The accuracy of human translation is the most stable, maintained at more than 85%; There is little difference in the recall rate, which has reached more than 85 points, but the score of word reasoning is slightly different. The highest score is MT, which has reached 88 points, which fully proves the effectiveness of MT. Based on the above experimental test results, it can be seen that MT and computer-aided translation have their own advantages, and there is little difference in the overall level. For the translation accuracy, computer-aided translation combines human translation on the basis of MT, and computer-aided translation has more advantages than MT.
Power Grid Sensitive Information Detection Technology Based on Internet Big Data and Machine Learning

With the continuous development of China’s electric industry, the power grid system and other infrastructure have also been well integrated. In the era of big data, the analysis and processing of public opinion information based on the Internet has become the mainstream. In order to make better use of the existing BD technology to effectively identify public opinion problems and hot topics in the power grid and put forward corresponding solutions, in this paper, the machine learning-based sensitive information detection technology for power grid is deeply studied. Firstly, this paper introduces the define and characteristics of power grid sensitive information, and then studies the Internet BD technology and machine learning technology. Based on this, a power grid sensitive information detection framework is designed and tested. Finally, the experimental results show that the grid sensitive information based on the BD and machine learning basic under 3% error rate of detection system, which shows that this technology can make the user satisfied to detect grid sensitive information.
Early warning and detection of geological disasters based on intelligent genetic algorithm

Abstract In recent years, the frequent occurrence of earthquakes, landslides, debris flow and other geological disasters worldwide is endangering people's production and life, which not only causes serious damage to infrastructure, but also creates a certain degree of fear for people. Geological disaster is an open nonlinear complex system, which has extraordinary complex geological process, formation conditions, and causes. Therefore, it makes difficulty in capturing the dynamic information and searching for the global optimal solution. Meanwhile, traditional geological disaster warning system has the deficiencies of single disaster warning and low accuracy. In order to improve the level of early warning and detection of geological disasters, this paper combined the genetic algorithm with superior performance and Support Vector Regression (SVR) algorithm to establish a feasible and credible early warning and monitoring model for geological disasters. The experimental results show that the early warning and monitoring model proposed in this paper can greatly improve the ability of geological disaster prevention and early warning, and greatly improve the level of disaster prevention and early warning, with good engineering application value.
Reducing Product Counterfeiting Using Blockchain Technology in E-Commerce Business

Due to the lack of traceability and proper authentication process, product counterfeiting is one of the main challenges in supply-chain management. In e-commerce business, merchants act as the central and only single controlling authority which leads to less trust and transparency. This creates a chance of product counterfeiting which can be reduced using blockchain with other supporting technologies. As products are delivered in multiple channels through many hands, there needs to be a way to identify original products and a proper product handover process. Many technologies are introduced to combat product counterfeiting such as barcode, QR code, NFC tag, hologram, etc. But these technologies are either expensive or lack full proof of product counterfeiting. Our research shows that a combination of destructible dynamic QR code, product location tracking along with blockchain technology can greatly reduce product counterfeiting in the e-commerce business. This paper proposes a novel approach of a product authentication system where a blockchain-based web application and a mobile application are introduced instead of a traditional product authentication system. Our evaluation shows that using this system, a consumer can easily identify whether a product is counterfeit or not.
Explainable Decision Making Model by Interpreting Classification Algorithms

This work uses the patterns learnt by machine learning models to convert the hard labels into probabilistic labels. The probabilistic labels by different learning models are combined using multi-criteria decision-making approaches. Preference order of alternatives is generated based on the decision probabilities assigned by these approaches. Both the preference order of alternatives and the patterns of learning models are provided as explainable knowledge, which gives a better interpretation about the decision space to a decision-maker. The choice of the final decision alternative depends on the decision-maker. The efficiency and the robustness of proposed decision-making model is verified over publicly available datasets. It can be observed from results that the performance of TOPSIS and PROMETHEE are equal for 9 out of 12 datasets.