Reviewerly/exHarmony huggingface dataset:
Three-dimensional measurement for specular reflection surface based on deep learning and phase measuring profilometry

Phase measuring profilometry (PMP) is a non-contact 3D measurement method. However，it is still a very challenging task when it is applied to an object with a specular reflection surface. In this paper, a 3D measurement method for specular reflection surface based on a deep learning framework is proposed to reduce the measurement error caused by highlights. Firstly, a dual-channel U-Net network (DC-UNet) is used to identify the highlight areas in the fringe image. Secondly, the dilated residual networks (DRN) combined with the features of the neighborhood of the highlight pixels are used to correct the highlight pixels, and the predicted fringe image without highlight can be obtained. Thirdly, the predicted fringe image is blended with the original image, regarded as the final result of the highlight removal. Fourthly, phase unwrapping is implemented using the fringe image of highlight removal, and the 3D coordinates values can be obtained by combining the calibrated parameters with the phase values unwrapped. Simulated results show that the absolute maximum deviation, absolute mean error and standard deviation of reconstructed 3D height coordinates are reduced by 98.27 %, 68.70 %, and 91.11 %, respectively. The real experiment results show that the maximum error of the absolute phase is reduced by more than 87.00 %, and the mean error of the absolute phase is reduced by more than 92.72 %. Finally, more than 99.14 % of the pixels with intensity saturated caused by highlights can be corrected by the proposed method.
Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes

Film is one of the most interesting topics to talk about. When someone writes an opinion on a film, all the elements in the film will be written down. Film opinion data in this study were taken from film comments written on twitter. The number of opinions written on Twitter requires classification according to the sentiments they have so that it is easy to get the tendency of the opinion towards the film whether it tends to have a positive, negative or neutral opinion. Recently, the spotlight on twitter media in Indonesia is a film with the title Horror-Ngeri Sedap. Ngeri-Ngeri Sedap is a 2022 Indonesian comedy-drama film directed and written by Bene Dion Rajagukguk. The film is set in the Batak Tribe, starring Arswendy Beningswara Nasution, Tika Panggabean, Boris Bokir Manullang, Gita Bhebhita Butar-butar, Lolox, and Indra Jegel. This causes differences in the views and opinions of twitter users towards the Horror Sedap film. So it is necessary to have a sentiment classification for the opinion. The use of the Naïve Bayes Algorithm was chosen in the analysis because it has the highest probability or opportunity value for data classification. Labeling on Twitter data is done manually by giving positive, negative, neutral sentiments to the raw dataset in Microsoft excel then the data enters the preprocessing transformation, tokenization and filtering stages. The tf-idf weighting is carried out when the data is complete in the transformation process, tf-idf is used to determine the number of occurrences of words, then the data classification is carried out using the Naïve Bayes Algorithm. confusion matrix testing is done after the data classification is complete using the orange tools. Based on the test results with the confusion matrix with orange tools, the average accuracy value is 0.65% and the precision value is 0.67%, and recall is 0.65%, and the percentage is neutral 0.83% in its classification. This proves that the public sentiment on the twitter platform towards the case of the film Horror Sedap is neutral and the Naïve Bayes Algorithm is considered reliable and valid in data processing.
Manipulation of URL Addresses Using Machine Learning to Provide Better Cyber Security

In this new and modern reality, complex systems are no longer only reserved for the theoretical textbooks, research centres, or industrial plants. With computers and the Internet becoming indispensable for both homes and organizations, complex systems are now in the hands of regular civilian users, utilizing a combination of advanced concepts (such as control theory, artificial intelligence and machine learning, BigData, etc.) to deliver comfort, safety, robustness, and fast and easy communication to households, commerce, and industries alike. However, this sudden rise in complexity and opaqueness of the systems in regard to their everyday users prompts a serious rethink of the approach to securing IT systems. As a pinnacle example of complexity, the Internet offers many points of cyber vulnerability where regular users can be targeted. In this work we investigate the implementation of machine learning based approaches for detection of malicious URLs in order to improve cyber security. The proposed approaches show promising results in exposing dangerous links, and could be implemented in several points within a complex network, both locally and in different nodes within a network hierarchy, thus showing how data science and machine learning (two of the very driving forces in the rise and development of complex systems) stimulate the core of the cyber security. Moreover, the same proposed approaches are used for classification of URL addresses based on the content that each of the URLs provides, thus, exploiting the benefits of machine learning approaches even further.
If you sense it coming, it may be too late: the digital screen as a window into warfare. Art Review – I Saw the World End (2020)

In an increasingly digitised reality, our interactions with war are often filtered through the screen. This review, If you sense it coming, it may be too late: the digital screen as window into warfare, focuses on Es Devlin and Machiko Weston’s I Saw the World End (2020) as an enmeshing of distanced spectatorship and an abstract intimacy that provides a window into warfare. The digital artist’s collaborative conceptualisation of the 1945 atomic bombings entangles a virtual and physical co-presence through its publicly networked projection. The artwork I Saw the World End enacts a personal, societal, and cultural dialogue for the past and future on the atrocities of warfare and the fragility of humanity, epitomizing digital’s art’s innate responsibility to bear witness and critique such events.
Analisis Kemampuan Pemahaman Mahasiswa Pada Mata Kuliah Statistika di STIKes Santa Elisabeth Medan Tahun 2022

Statistics is one of the subjects that must be taught in almost every study program. Many students consider the course to be a pretty scary subject because the material is more calculating. The purpose of this study was to describe the level of ability and the location of students' understanding errors in the statistics of the Health Information Management Study Program at the Santa Elisabeth College of Health, Medan. This research uses research with survey method. In this study, students were asked to solve statistical problems related to the statistical material that had been studied previously. The place of this research is the Santa Elisabeth College of Health, Medan in the Health Information Management Study Program, Medan City, North Sumatra Province. The research time to obtain data and information was implemented in June 2022. The population in this study were students of level I and II who programmed statistics courses for the academic year 2021/2022, totaling 30 students. The results of this study indicate that: (1) The level of students' understanding ability in the statistics course of the Health Information Management Study Program, Santa Elisabeth College of Health, Medan is in the high category, namely 62%; (2) Of the entire sample, 62% answered the questions correctly, and 38% answered incorrectly. From these results, it can be seen that the percentage of students who answered correctly was greater than those who answered incorrectly. (3) The results showed that the errors in answering statistical questions occurred because students had problems at the stage of reading the questions, understanding, transforming, processing skills and in choosing the right answer.
Secure File Storage On Cloud Using Hybrid Cryptography

As technology today is moving forward exponentially, data exchange over the Internet has become a daily routine. Furthermore, businesses are growing internationally and offices are being established in a variety of different places throughout the world. This has resulted in the necessity to make data accessible and practical from any place. As a result, information sent via an may lead to critical security problems involving the breach of secrecy, authentication, and data integrity. This paper introduces a cloud storage system by utilising hybrid cryptography approach that leverages both advantages of symmetric key and asymmetric key cryptographic techniques. In our proposed system, the symmetric key algorithm AES is utilised to encrypt data, whereas the asymmetric key algorithm ElGamal is employed to perform key encryption before the data upload into cloud storage. Combining both symmetric key and asymmetric key methods alleviates privacy issues while increasing data confidentiality. In addition, a hash function which is SHA-2 is executed before the encryption process and after the decryption process. Both hash values are derived through a hashing procedure and matched in order to verify the data integrity. However, if the users' accounts were lost or stolen, all encryption would be meaningless. Hence, a Two-Factor Authentication (2FA) is also employed to minimise the abovementioned risk to achieve a greater security over the cloud environment.
Self-Learning for Few-Shot Remote Sensing Image Captioning

Large-scale caption-labeled remote sensing image samples are expensive to acquire, and the training samples available in practical application scenarios are generally limited. Therefore, remote sensing image caption generation tasks will inevitably fall into the dilemma of few-shot, resulting in poor qualities of the generated text descriptions. In this study, we propose a self-learning method named SFRC for few-shot remote sensing image captioning. Without relying on additional labeled samples and external knowledge, SFRC improves the performance in few-shot scenarios by ameliorating the way and efficiency of the method of learning on limited data. We first train an encoder for semantic feature extraction using a supplemental modified BYOL self-supervised learning method on a small number of unlabeled remote sensing samples, where the unlabeled remote sensing samples are derived from caption-labeled samples. When training the model for caption generation in a small number of caption-labeled remote sensing samples, the self-ensemble yields a parameter-averaging teacher model based on the integration of intermediate morphologies of the model over a certain training time horizon. The self-distillation uses the self-ensemble-obtained teacher model to generate pseudo labels to guide the student model in the next generation to achieve better performance. Additionally, when optimizing the model by parameter back-propagation, we design a baseline incorporating self-critical self-ensemble to reduce the variance during gradient computation and weaken the effect of overfitting. In a range of experiments only using limited caption-labeled samples, the performance evaluation metric scores of SFRC exceed those of recent methods. We conduct percentage sampling few-shot experiments to test the performance of the SFRC method in few-shot remote sensing image captioning with fewer samples. We also conduct ablation experiments on key designs in SFRC. The results of the ablation experiments prove that these self-learning designs we generated for captioning in sparse remote sensing sample scenarios are indeed fruitful, and each design contributes to the performance of the SFRC method.
The Effect of Flipped Classroom Model with Information Search Strategy Through Moodle-Based E-Lematika on Students' Self-Efficacy in Mathematics

Self-efficacy has a great influence on learning outcomes and academic achievement. However, many students have low self-efficacy in mathematics. For this reason, a learning process is needed that can help improve students' self-efficacy in mathematics. This study aimed to determine the effect of the inverted classroom model with information search strategies through moodle-based e -lematika media on students' self-efficacy in mathematics. This research is a type of quantitative research with a quasi-experimental design method. The population of this research is class XI students of SMK N 1 Jambi City and two sample classes, namely 31 students of class XI TKJ 1 and 31 students of class XI TKJ 5, respectively, the experimental class and control class. The self-efficacy questionnaire data analysis for the two classes was carried out using the independent sample t-test, which showed the value of sig. (2-tailed) is 0.000 &lt; 0.05. So, there is a significant effect on the application of the flipped classroom model with information search strategies through moodle-based e-lematika media on students' self-efficacy.
Human-Centric Ontology Evaluation: Process and Tool Support

Abstract As ontologies enable advanced intelligent applications, ensuring their correctness is crucial. While many quality aspects can be automatically verified, some evaluation tasks can only be solved with human intervention. Nevertheless, there is currently no generic methodology or tool support available for human-centric evaluation of ontologies. This leads to high efforts for organizing such evaluation campaigns as ontology engineers are neither guided in terms of the activities to follow nor do they benefit from tool support. To address this gap, we propose HERO - a H uman-Centric Ontology E valuation P RO cess, capturing all preparation, execution and follow-up activities involved in such verifications. We further propose a reference architecture of a support platform, based on HERO. We perform a case-study-centric evaluation of HERO and its reference architecture and observe a decrease in the manual effort up to 88% when ontology engineers are supported by the proposed artifacts versus a manual preparation of the evaluation.
DESAIN MODEL KLASIFIKASI SAMPAH ORGANIK MENJADI BAHAN BAKU BRIKET BIOMASSA MENGGUNAKAN METODE DEEP LEARNING

The development of information technology in Makassar has implemented the smart city concept. The mission of urban development is to build the economy of urban communities that have superior capacity, competitiveness, productivity, creativity, innovation, efficiency and use information technology to utilize the waste generated by the community and companies. Cities have problems stemming from development by producing waste in the form of organic and inorganic waste. To empower the community, city governance is needed with a design concept that continues towards green technology. The aim of the research is to design a model that can sort and identify waste based on various types of organic matter that can be used as raw material for making biomass briquettes as a renewable alternative energy source. Research uses deep learning methods as part of machine learning in solving object detection problems in digital image classification in computer vision. The research resulted in a classification model design based on object detection by classifying 5 types of organic waste.
The Effectiveness of Kahoot in Improving Vocabulary: The Case of a Private University English Study Program

The purpose of this research was to find out the effectiveness of Kahoot application in improving the students’ English vocabulary. A study was conducted at the English Study Program students of Widya Dharma University Pontianak. The study adopted a mixed method by online using Google form and interviews directly by using zoom. The data were collected via questionnaire and interviews. The findings showed that the majority of the respondents who took part in the study mostly gave the positive responses which showed that Kahoot could help them greatly in improving their English learning vocabulary and could increase their motivation in gaining the knowledge and getting the new vocabularies.
Analysis of the Stage Performance Effect of Environmental Protection Music and Dance Drama Based on Artificial Intelligence Technology

There are a lot of environmental protection musicals and dances in public entertainment life to support the concept of environmental protection and to entice people to support the cause of environmental protection. As we all know, in a musical performance, the distance between the audience and the actors, the design of the stage environment, the training of the actors, the costumes of the actors, the makeup of the actors, and the musical accompaniment will all have more or less influence on the stage performance effects. In order to make environmental protection music and dance dramas profoundly meaningful to social production and human activities, it is especially important to analyze the stage performance of such music and dance dramas. The analysis of stage performance effects is not only beneficial to making the ideas spread by the music and dance drama penetrate into people's hearts but can also provide guiding suggestions for the production of music and dance drama. Therefore, this paper proposes a linear regression algorithm based on artificial intelligence to deeply explore and analyze the influence of the above six factors on the stage performance effects of environmental protection music and dance drama. In our method, first we preprocess the data collected by classification to remove the odd values from the data so that the various types of data conform to a normal distribution. Secondly, we obtained linear fit plots of the six factors with the stage performance effect scores by using a linear regression algorithm to deeply analyze the correlation between various types of data and the stage performance effect scores. Finally, through numerical calculations, we found that the distance between the audience and the actors, the training of the actors, and the musical accompaniment have a greater influence on the performance effect of the musical cabaret in the environmental protection category. Meanwhile, the costumes of the actors, the makeup of the actors, and the design of the stage environment have less influence on the performance effect of the musical cabaret. Therefore, in the production and performance of environmental protection music and dance dramas, producers and performers should pay more attention to the distance between the audience and the actors, the design of musical accompaniment, and the training of actors. To sum up, this paper has made a scientific, detailed, and reasonable analysis of the performance effect of environmental protection music and dance dramas on stage, contributing to the dissemination of environmental protection ideas.
You've got a Friend in ME (Mobile Edge): Blockchain Processing with Cloud Node Backup

The growing ubiquity of Blockchain has led to the experimental transplantation of classical web applications into the Blockchain space. Notoriously, running computationally-expensive processes on-chain is less than ideal. Therefore, it is crucial to find ways to utilize the security and decentralization provided by Blockchain technology while reducing on-chain computational load. To that end, recent work has shown the benefit of Multi-stage Transaction architectures. The benefit of such systems is that they provide requested data quickly to the client while still ensuring that accurate results are eventually returned. In this paper, we describe a prototype of a multi-stage transaction system, which we call Friend in Mobile Edge (FiME), that intelligently coordinates client requests between a Blockchain and a central cloud node. We select the use case of stock price prediction and evaluate based on response time to the client as well as computational load on the various components. Our results involve an analysis of the performance of such a system in delivering timely results to the end user, as well as a discussion of the drawbacks of handling large data requests on-chain. We also include recommendations for how future systems can build on these findings.
Fast Inline Microscopic Computational Imaging

Inline inspection is becoming an essential tool for industrial high-quality production. Unfortunately, the desired acquisition speeds and needs for high-precision imaging are often at the limit of what is physically possible, such as a large field of view at a high spatial resolution. In this paper, a novel light-field and photometry system is presented that addresses this trade off by combining microscopic imaging with special projection optics to generate a parallax effect. This inline microscopic system, together with an image processing pipeline, delivers high-resolution 3D images at high speeds, by using a lateral transport stage changing the optical perspective. Scanning speeds of up to 12 mm/s can be achieved at a depth resolution of 2.8 μm and a lateral sampling of 700 nm/pixel, suitable for inspection in high-quality manufacturing industry.
What Competences Does the Regional Market of IT Specialists Want? Development and Approbation of Technology for Analysis of Current Vacancies in the Labor Market

One of the most dynamically developing industries in Russia is IT industry. The main problem of IT development is the lack of highly qualified specialists. Determining the relevant and in-demand knowledge and skills of specialists is possible on the basis of an analysis of vacancies posted on Internet job search portals. The study is aimed at development and approbation of the technology for analysis of current vacancies for IT specialists for further designing the content of additional vocational educational programs. Modern machine learning techniques such as Natural Language Processing can be used for automatic position selection based on skills in vacancies. K-means method was used in this paper for cluster analysis of hard and soft skills lists from vacancies posted on the HeadHunter website. The most popular skills have been determined such as teamwork skills, knowledge of working with databases and version control systems, as well as proficiency in English. It has been established that in 2021 the most popular programming language is JavaScript. As a result, two variants of programmer specialization were formed, hard and soft skills for each specialization were determined.
Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels

Modulation recognition (MR) has become an essential topic in today's wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature's susceptibility to its surroundings and reduce the need for human feature extraction and evaluation. In particular, these investigations rely on the unrealistic assumption that the channel coefficient is typically one. This motivates us to overcome the previous constraint by providing a novel MR suited to fading wireless channels. This paper proposes a novel MR algorithm that is capable of recognizing a broad variety of modulation types, including M-ary QAM and M-ary PSK, without enforcing any restrictions on the modulation size, M. The analysis has shown that each modulation choice has a distinct two-dimensional in-phase quadrature histogram. This property is beneficially utilized to design a convolutional neural-network-based MR algorithm. When compared to the existing techniques, Monte Carlo simulations demonstrated the success of the proposed design.