Reviewerly/exHarmony huggingface dataset:
Pelembutan Citra dengan Metode Filter Gaussian

Image is a multimedia component rich in information and has an essential role as an information provider. However, the images encountered often experience a decrease in quality, such as defects or noise, causing the information conveyed from these images less clear. Noise causes the image to be too contrasted, blurry, or not sharp enough. One type of noise is contrast noise. Image Smoothing here is one of the operations to improve quality which aims to smooth out images with unbalanced contrast noise. Disturbances in the image are generally in the form of variations in the intensity of a pixel that is not correlated with neighboring pixels. Contrasting images are caused by uneven lighting, which can cause the information in the image to be reduced and difficult to interpret. For this reason, quality improvement must be made to get a better image. In this study, the softening of contrasting images uses the Gaussian Filter method. This filter has the effect of equalizing the gray distance to make the image obtained smoother. Based on the results of the tests, it got an accuracy of 83.3%, meaning that the application's performance is suitable for image smoothing.
Creating Password Protector Application for Preventing Unauthorised Hackers Using Java

Password managers utilise the master password or security key to safeguard user passwords. The security of Master Password users gets compromised when they use a weak Master Password. Because users always require a token to log in, the advantages of using security keys are minimal. In this article, we present an entirely novel password administration platform that uses secret sharing and a user's server to offer excellent security and convenience of use. Password protection systems are still vulnerable to various attacks ten years after their inception. SECRYPT is an all-in-one encrypted password manager that takes care of your security and privacy. Protects the user's web service password in the cloud service password file. The fact that the application works as SaaS in the cloud increases the usability and reliability of the application. All passwords stored in our app are encrypted and stored in a cloud database. Your passwords and encrypted keys are stored in multiple databases for added security. To address this issue, we attempted to create a safe password manager that can recover multiple user accounts and passwords locally, relieving users of the stress of remembering many accounts and passwords. The data is encrypted with the AES encryption algorithm for maximum user security and the password manager is integrated into the browser to provide many important functions to the end users
Deep Regression on Quaternion: A Forward Kinematics Neural Network for Study Six-DoF Pose

Solving the forward kinematics problem of robots with high degrees of freedom using neural networks is a really innovative field of study. However, conventional neural network has not been adequate to address this mission requirements of the robot under it’s large-scale data with significant unpredictability and low coupling. Thus, we have analyzed and discussed this methodology, and have proposed three solutions, using empirical theory of visual tasks. The results of the experiments demonstrate that our proposed model was effective.
An efficient multi-party private set intersection protocols based on bloom filter

Secure Multi-Party Computation (SMPC) has attracted the attention of many scholars in recent years because it can calculate an agreed function without a trusted third party. As a specific problem of SMPC, Multi-Party Private Set Intersection (MPSI) is what we focus on. MPSI allows multiple parties to calculate the intersection of their set data securely without leaking any other private data. Although many MPSI protocols have been proposed, they are still not efficient enough. To solve this problem, we propose an efficient MPSI protocol which is based on Bloom filter and ElGamal cryptography. By using encrypted Bloom filter and multiply homomorphism of ElGamal cryptography, our protocol has the following advantages: (1) each party has his own public key and secret key, which increases security; (2) with the same number of parties and set elements, our protocol is highly efficient compared to the related protocols.
A knowledge graph complement method based on improved graph convolution neural network

In order to better measure the correlation between entities and solve the problem of link prediction in the knowledge graph, a knowledge graph completion method based on graph neural network and attention mechanism is proposed HRGAT. The hybrid attention mechanism is used to calculate the correlation of adjacent entities in the knowledge graph and obtain the corresponding feature entity vector. The capsule network is used as the decoder to better represent each attribute dimension of the entity, to improve the expression ability of migrated feature entity vectors, link prediction experiments were conducted in the classic datasets WN18RR and FB15K-237. The experimental results show that compared with other classical algorithms, this method has some improvement in four common evaluation indicators. It is proved that the hybrid attention mechanism can effectively improve the prediction accuracy of RGCN on the knowledge graph triplet, thus expanding the knowledge map and providing data support for downstream tasks.
Design of power grid full-service data monitoring platform based on cloud computing

The use of power monitoring and management system in power enterprises has significantly improved the convenience, fault tolerance, and related economic income of power grid in power supply. At present, there are still many problems in the management system, such as the lack of unified planning. Many functions related to power monitoring and management often need a lot of software to be repeatedly installed in the system to complete the functions required by customers. Meanwhile, this software often has duplicate functions. Power data cannot be shared, and the upgrade of the system will encounter many obstacles. To meet the data communication requirements of component-based power monitoring and management system, this paper applies the characteristics of multi-point distribution and real-time and efficient transmission of data based on model-based information transmission. It also applies component-based data monitoring technology to complete the design. For the power data content of monitoring and calculation, a reusable framework is applied to facilitate user expansion. Users can complete the interface layout by dragging controls on the control interface of the power monitoring and management system, and then realize data customization and saving by binding the database. Finally, the monitoring of the power grid data platforms under cloud service is realized, which is conducive to the effective and timely processing of blowout power grid data. It is of great significance to the safe and stable operation of the power system.
Development and deployment of a responsive library platform based on Docker and SSM

In recent years, with the rapid development of computers, the Internet and other modern technologies, the information age has quietly arrived. Libraries, as an important way to learn knowledge, have thus ushered in great development. In response to the problem of traditional libraries' static web page compatibility, this paper designs how a responsive library platform based on SSM framework and Bootstrap can be deployed under Centos Linux platform, tested and packaged through Docker technology. Docker covers all stages of platform development and operation and maintenance, and with the help of Docker technology, it can be built once and Docker covers all the phases of platform development and operation and maintenance, and it can be built once and run everywhere with Docker technology. When using LVS to deploy Web server clusters, Docker images can be used to deploy Web servers directly. The test results show that the platform has strong portability, saves the development and deployment costs, and has great advantages in the rapid deployment of large-scale applications.
Research on matrix entropy value between industrial ethernet data and internet data based on information entropy

The adoption of network technology for control and measurement makes Industrial Control Systems (ICSs) and Internet of Things (IoTs) more and more powerful, whose communication data plays an important role to realize data intercommunication and sharing. However, communication data have different characteristics, even if the same communication protocol is used in ICS or IT, the communication characteristics and modes are quite different. In order to understand the difference of communication data between internet and industrial ethernet, in this paper, we conduct a series of experiments that use information entropy algorithm to data changes based on different industrial protocol and http protocol, which always is used to measure the uncertainty and describe the uncertainty of data packets. The experimental result analysis shows that there exists a big difference in industrial ethernet data and internet data, the industrial ethernet data are more relatively balanced and more regular than those of internet data.
The Current Situation and Development of Chinese Language Education in Indonesian Universities in the New Era: A Case Study of the Department of Chinese Language, Universitas Sebelas Maret

Looking at the development of Chinese language education in Indonesia from the perspective of teaching Chinese as a foreign language, over the past few decades, considerable achievements have been achieved in the promotion of the Chinese language in Indonesia, with an increasing number of people learning Chinese language, even evolving the trend of the global Chinese language. This article explores the current situation and prospects of Chinese language education in Indonesian universities and the challenges and problems of Chinese language education in Indonesian universities. In promoting the development of Chinese language education, the academic community has gradually brewed some new ideas, hoping to encourage the globalization of Chinese language education through a brand-new structure, so that there will be further room for further development.
Analysis of the rise of online news and changes in press dependence on press releases : Focusing on the consistency of sentences between Samsung Electronics" press releases and major newspaper articles between 2004 and 2019

온라인 뉴스가 오프라인 뉴스를 압도하기 전 시점(2004 · 2009년)과 후 시점(2014 · 2019년) 간의 보도자료 의존도 차이 분석을 통해 온라인 뉴스의 확산으로 인한 언론의 보도자료 의존도 변화를 검정했다. 삼성전자의 보도자료 1,010건과 8개 신문사의 기사 2,973건을 분석 대상으로 설정했다. 표절검색 프로그램으로 보도자료와 관련 기사간의 문장 일치율을 도출했다. 검정결과 대규모 언론보다는 소규모 언론의 보도자료 의존도가 상대적으로 높았다. 문장일치율에서 대규모 언론이 전 시점과 후 시점 간에 유의미한 차이가 없었던 데 반해 소규모 언론은 전 시점 대비 후 시점에서 유의미하게 증가했다. 2010년 이후 신문기사 소비에서 온라인 뉴스가 종이신문을 압도하는 환경이 조성되는 바람에 온라인 기사 생산량을 늘려야 했던 언론들이 보도자료에 더 많이 의존한 탓으로 해석할 수 있다. 특히 인력 기반이 취약한 소규모 언론이 온라인 뉴스 생산을 위해 보도자료에 상대적으로 더 많이 의존한 것으로 분석된다. Through the analysis of the difference in press release dependence between the time before (2004 · 2009) and the time after (2014 · 2019) when online news overwhelms offline news, the change in press release dependence due to the spread of online news was tested. Samsung Electronics" 1,010 press releases and 2,973 articles from eight newspapers were set for analysis. The sentence matching rate between the press release and related articles was derived by a plagiarism search program. As a result of the test, small media was relatively more dependent on press releases than large media. In the sentence consistency rate, there was no significant difference between the previous and the posterior points in the large-scale media, while the small-scale media increased significantly from the previous point in time. It can be interpreted that the media, which had to increase the production of online articles, relied more on press releases as online news overwhelmed paper newspapers in the consumption of newspaper articles since 2010. In particular, it is analyzed that small media with a weak manpower base relied relatively more on press releases for online news production.
Development of an Algorithm that Allows Improving Disparity Maps in Environments Contaminated with Suspended Particles

The estimation of the depth of a scene has become a research niche due to the large number of applications that exist today.In a stereo vision system, disparity maps make it possible to obtain the depth of a scene from two rectified images.Stereo vision systems are sensitive to illumination, reflections, lens distortion, noise, camera alignment, etc.; therefore, in this work, we present an algorithm that allows us to improve the disparity map when the environment presents particles in suspension using stereo vision through infrared cameras.For this purpose, filters, matching, and dehazing algorithms are implemented.
Comprehensive Analysis of Video Surveillance System and Applications

In this growing age of technology, various sensors are used to capture data from their nearby environments. The captured data is multimedia in nature. For example, CCTV cameras are used in those places where security matters or where continuous monitoring is required. Hence object detection, object recognition, and face recognition became key elements of city surveillance applications. Manual surveillance seems time-consuming and requires huge space to store the data; hence video surveillance has a significant contribution to unstructured big data. All surveillance techniques and approaches are based on Object Tracking, Target Tracking, Object Recognition, and Object Mobile Tracking Systems (OMTS). The main difficulty, however, lies in effectively processing them in real time. Therefore, finding a solution still needs careful consideration. This paper mainly targeting to the smart city surveillance system and inspects all existing surveillance systems based on various tremendous technologies like a wireless sensor network, machine learning, and Deep Learning. The author discovered the problems in the existing methods and summarized them in the paper. The motive is to point out the various challenges and offer new research prospects for the multimedia-oriented surveillance system over the traditional surveillance system for the smart city network architecture. The thorough survey in this paper starts with object recognition and goes toward action recognition, image annotation, and scene understanding. This comprehensive survey summarizes the comparative analysis of algorithms, models, and datasets in addition to targeting the methodologies.&amp;nbsp;&lt;br&gt;
A SENSE OF SELF-RESPONSIBILITY IN THE BEHAVIOR OF A TEENAGER

The issue of studying and developing responsibility is currently receiving more attention. Responsibility is the term used to describe a person's subjective traits, the study and analysis of which is one of the pressing issues facing contemporary psychology. This is mainly because it is crucial for a person to possess certain personal qualities that guarantee not only adaptability to constantly changing economic, political, and social life but also the advancement of an individual and, ultimately, society as a whole. Because of this, the intended goal of this article is to examine teenagers' feeling of personal responsibility. According to the result of the research, it is important to educate teenagers in the sense of responsibility for their behaviour.
Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

SIM card (Subscriber Identification Module) card is a physical electronic device that is the integrated circuit of the internet. Sim cards are used by the public as a place to store quotas for internet, phone calls and SMS. There are many types of SIM cards that are used by the public, such as Telkomsel cards, XL cards, Exis cards and Smartfren cards. There are some people who are interested and use Telkomsel cards, because the network is good. But there are some people who don't use Telkomsel cards, because the quota price is quite expensive. Therefore, the Penlus will make research about people's interest in Telkomsel cards. This study aims to determine the amount of public interest in the Telkomsel card. To conduct this research, the authors used 42 community data which would be classified using the decision tree method. The data used by the author was obtained by distributing a questionnaire to the public. After classifying using the decision tree method, the result is that the people who are interested in the Telkomsel card are 33 people who are interested in the Telkomsel card (for the representation results it is 78.5%) and the results obtained are that the people who are not interested in the Telkomsel card are 9 people (for its representation results of 21.4%). From the results of the study, many people are interested in Telkomsel cards, even though the internet, call and SMS quota prices are quite expensive.
Conditional Super Resolution Generative Adversarial Network for Cervical Cell Image Enhancement

Abnormal cellular development in the cervix causes cervical cancer.Undergoing Pap smear screening & HPV tests at regular intervals possibly helps identify the disease at an early stage, aiding diagnosis and treatment.Pathologists are involved in the screening to detect abnormal cells in the slide.Removing a portion of the slide with poor quality could improve nuclei detection by enabling better visualization of some important information.In this study, a Conditional Generative Adversarial Network (CGAN) with a Super Resolution technique is applied for cervical cell images to generate photo-realistic images.Poor-quality images are not removed; rather, they are converted into good-quality images.It ensures equal distribution of image generation in terms of classes in a given dataset.The performance of the recommended technique is measured using PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity)and Inception Score(IS).The proposed methodology outperforms other augmentation techniques and Variational Autoencoders (VAE) based algorithms.
Solving a Multi-criteria Decision Problem to Efficiently Handle UML Model Inconsistencies

Nowadays, every business is heavily depending on software. However, designing and developing software is a very serious engineering challenge since software systems are growing in size and complexity. This gives rise to many inconsistencies in software design. To deal with this issue, researchers have been working for many years on different model inconsistency management activities, namely the detection, the diagnosis and the handling of those inconsistencies. This work focuses on handling UML model inconsistencies by proposing an AHP-based method aiming to help modelers choosing the right repair action that fits well their modeling objectives and strategies. The proposed method helps structuring the selection of the most appropriate repair action among a set of alternatives. It also represents and quantifies the criteria elements of this decision problem, relates these elements to overall goals and evaluates and ranks alternative solutions. The efficiency of the method is evaluated using different inconsistency examples through different situations. The obtained results show that modelers can have credible propositions to make decisions in accordance with their initial objectives.
Deep fake Detection Through Deep Learning

Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
The NOW of time and the Pioneer Anomaly

This paper takes another look at the Pioneer anomaly with the NOW theory of time: Two recent papers by Longo, "The NOW of time and how it impacts physics" and "Theories affected by Time Flow."This new concept of time-flow changes the foundation of the physics of time defined by Newton and has impacted the main evidence for dark matter, which shows that galaxies rotate at a speed consistent with gravitational theory.This work demonstrates that the confused state of the pioneer's anomalous acceleration is also likely due to Newton's definition of time.With the NOW theory of time, the theoretically calculated value of the pioneer anomaly is 7.2 × 10 -10 m/sec 2 within the reported measurement error.When adding to this a few onboard contributions that seem to be the most reasonable, the final value is 8.12 × 10 -10 m/sec 2 compared to the reported observed value of (8 ± 3) × 10 -10 m/sec 2 .
POINTACL: Adversarial Contrastive Learning for Robust Point Clouds Representation Under Adversarial Attack

Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models. In contrastive learning, a projector which consists of multilayer perceptron (MLP) will project high dimension 3D point cloud feature into low dimension for calculating contrastive loss during contrastive pretraining.We propose a novel method for generating high-quality 3D adversarial examples for adversarial training, which leverages the virtual adversarial loss with the feature representations prior to projection in a contrastive learning framework. To train the self-supervised contrastive learning framework adversarially, we introduce our robust aware loss function. Additionally, we show that incorporating high difference points using the Difference of Normal (DoN) operator as an additional input for adversarial self-supervised contrastive learning can significantly enhance the adversarial robustness of the pre-trained model. Our proposed method, POINTACL, is evaluated on several downstream tasks, including 3D classification and 3D segmentation using multiple datasets. Our experimental results demonstrate that POINTACL achieves state-of-the-art performance in terms of robust accuracy when compared to other contrastive adversarial learning methods.
Towards Dialogue Modeling Beyond Text

In this paper, we model aspects of communication beyond the words that are said. Specifically, we aim to detect interruptions and active listening events, which are important elements in any dialogue. We build a dataset with fine-grained annotations for each category and train multimodal models that take into account all channels in a digital conversation, that is, the video, the audio, and the text. Our experiments show that multimodality is a necessary component in modeling the complexity of the non-textual components of the conversation as different artifacts require different modalities to capture effectively.