Reviewerly/exHarmony huggingface dataset:
Applying the Multiple-Attribute Decision Making - Simple Additive Weighting to determine the Most Popular Internet Provider among Students

The rapid development of information technology makes internet access an important requirement in many aspects. The COVID-19 pandemic has also become the basis for the increasing use of internet access, especially in the education sector. Restrictions activities outside the house, causing the productivity of students to decline. Therefore, in order to remain active and productive, students are required to follow an online learning system. This will make the use of internet quota even greater. The number of cellular providers that provide internet services sometimes makes users confused in determining which provider is better to use. In this study, the Multiple-Attribute Decision Making (MADM) - Simple Additive Weighting (SAW) methods used to identify the decision support system in determining the type of cellular provider that is the most popular among students, especially in the Griya Martubung Area. This decision support system can certainly meet the needs of internet quotas and budgets. The results show that the most popular providers are Three, followed by Telkomsel, XL Axiata, Smartfren and Indosat.
Analisis kekuatan struktur ramp door haluan pada kapal Ferry Ro-Ro 1500 GT dengan variasi beban menggunakan Finite Element Method

Ramp Door is a door to put a vehicle into a Ro-Ro ship or any other type of ship that transports vehicles. The use of Ramp Door is needed to facilitate the process of unloading and loading vehicles from the crossing dock to the ship and vice versa. This study aims to analyze the strength of Ramp Door structure with load variations from various types of vehicles in order to compress the maximum stress results and safety factors. The method used is the FEM method with the help of an element-based application up to. The results of the study obtained the maximum stress value of Ramp Door Bow with MPV vehicle type at an even load is 43.26 MPa. In this type of SUV vehicle, the maximum stress with an even load is 50.37 MPa. In sedan vehicle type the maximum stress with an even load is 37.61 MPa. in commerial vehicle type the maximum stress with an even load is 45.70 MPa. In this type of small truck vehicle, the maximum stress with an even load is 81.53 MPa. In large truck vehicles the maximum stress with an even load is 302.48 MPa. In this type of vehicle, the maximum stress bus with an even load is 178.08 MPa. For the largest safety factor value is a type of Commercial vehicle with a value of 8.91. While the smallest safety factor value is the type of Big Truck vehicle with a safety factor value of 1.01.
Detection of Outdoor Traffic and Kids Playing Scene for Visually Impaired People

In this paper, a novel outdoor scene detection technique for visually impaired people has been proposed. This system focuses on the detection of traffic jam scenes and playing kids scenes. Visually impaired people (VIP) face more difficulties walking alone outdoor. As they cannot prevent themselves from unexpectedly hitting objects, avoiding stray animals, and bumping into people. It inspired us to work on a solution for the mobility of blinds. This paper proposes an effective outdoor scene detection system for improving their lifestyle. Traditional solutions are available for visually impaired people like white cane and guide dogs. Traditional solutions address obstacle detection and path guidance, but scene-cognition is somehow a neglected domain. The proposed system can detect two scenes. SIFT and K-means clustering machine learning algorithms have been used in this system along with the following classifiers with accuracy-decision tree (84%), KNN (84.75%), random forest (88.58%), SVC linear kernel (85.08%), SVC polynomial kernel (85.33%), SVC radial basis function kernel (87.08%), and linear regression (85.08%).
Polarimetric Multi-View Inverse Rendering

A polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) and the degree of polarization (DoP) of reflected light are related to an object's surface normal. In this paper, we propose a novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that effectively exploits geometric, photometric, and polarimetric cues extracted from input multi-view color-polarization images. We first estimate camera poses and an initial 3D model by geometric reconstruction with a standard structure-from-motion and multi-view stereo pipeline. We then refine the initial model by optimizing photometric rendering errors and polarimetric errors using multi-view RGB, AoP, and DoP images, where we propose a novel polarimetric cost function that enables an effective constraint on the estimated surface normal of each vertex, while considering four possible ambiguous azimuth angles revealed from the AoP measurement. The weight for the polarimetric cost is effectively determined based on the DoP measurement, which is regarded as the reliability of polarimetric information. Experimental results using both synthetic and real data demonstrate that our Polarimetric MVIR can reconstruct a detailed 3D shape without assuming a specific surface material and lighting condition.
PROFIL AKTIVITAS PESERTA DIDIK PADA UNJUK KERJA BERBASIS PROYEK DALAM PEMBELAJARAN DARING MATERI SIFAT-SIFAT CAHAYA

Signals, file size constraints when sending proof of student progress, and processing time, which may be delayed owing to other commitments, are all obstacles to integrating performance during online learning. This is due to differences in the implementation of online and offline performance. As a result, more study is needed to see how online learning activities improve project-based performance. The purpose of this study is to see how online learning habits influence project-based performance. The study included elementary school students. Data was acquired through observation and interviews because this is a qualitative research. In this investigation, observation and interviews were used as techniques. The data analysis strategies employed in this study included data reduction, data display, and data withdrawal/verification. Students' actions are good and in line with performance indicators, according to this study's findings. At SDN Menanggal 601, a project-based demonstration was held as part of this online learning using Google Meet and Microsoft Teams.&#x0D; &#x0D;
Key generation and distribution schemes classification for quantum key distribution networks of arbitrary topology

Quantum keys created during the quantum key distribution protocol have absolute secrecy due to physical laws and are not susceptible to breaking even with the unlimited computing power of the attacker. However, quantum key distribution systems have a range limit. Quantum key distribution networks based on trusted intermediate nodes are built to overcome the problem of the maximum range. This paper examines the connection of backbone networks with networks of arbitrary topology, introduces criteria for the classification of key generation and distribution schemes, and classifies some schemes according to the criteria introduced.
A2UA: An Auditable Anonymous User Authentication Protocol Based on Blockchain for Cloud Services

Regulating illegal activities in cyberspace to balance user privacy and cyberspace governance has been a non-trivial challenge when designing anonymous authentication solutions. For example, while several existing anonymous authentication protocols support accountability, they either risk leaking users' private keys or incur significant overhead for accountability in each ongoing authentication, including in cloud service-based authentication schemes. Seeking to address these limitations, this paper proposes an auditable anonymous user authentication (A2UA) protocol based on blockchain for cloud services. The A2UA protocol mainly employs bilinear pairing, partial authentication factors, dynamic credits and fake-public keys (FPKs) to achieve anonymous mutual authentication between users and cloud service providers, and applies ring signature and blockchain to accomplish two-level accountability while maintaining user privacy. Our analysis results show that the A2UA protocol outperforms several other existing schemes in terms of security, computation and communication costs as well as security and privacy features. Additionally, it has good feasibility in terms of the Ethereum Gas cost as demonstrated in our evaluation.
Popular science magazines in the Hindi language: an assessment

This review paper is based on the journey of science magazines in Hindi language. Apart from being the official language of India, Hindi is the third most spoken language in the world. Education in mother tongue is also being promoted by the government. In such a situation, it is necessary to study the Hindi science magazines which play an important contribution in science communication. In this direction, this review paper presents the discussion of various Hindi magazines to be published in the Hindi language. Through this, the challenges faced by Hindi science magazines have also been outlined.
A Linguistic Error Analysis of Google Translate Translation Result From Bahasa Indonesia To English

This study discusses the analysis of linguistic errors resulting from the translation of Indonesian text into English using Google Translate. The linguistic errors in question are errors at the morphological, syntactic, and semantic levels. The purpose of this research is to present the results of the analysis of linguistic errors in the translation of Indonesian texts into English and to describe examples of linguistic errors, with the hope that the use of Google Translate can provide complete and acceptable translation results in accordance with linguistic rules. The method used in this study is a descriptive qualitative method in which the researcher analyses linguistic errors in the Indonesian text which is translated into English. The subject of this research involved 13 students from the English Education Study Program at the Faculty of Teacher Training and Education, Pohuwato University. Research findings show that there are two linguistic errors found, which are classified into syntactic and morphological errors.
Image denoising using discrete wavelet transform and adaptive thresholding optimised with improved arithmetic optimisation algorithm and guided filter

The identification and characterisation of blood cells are needed for the diagnosis of blood-related diseases. To identify and classify different types of blood cells, image processing techniques are necessary. However, the medical images are generally corrupted with different types of noises. These noises affect the quality of images and causes impact in image processing. Denoising is a pre-processing method in image processing to acquire clear vision, eliminate noise, and enhance the quality of images. In this paper, a novel approach for medical image denoising based on discrete wavelet transform (DWT) and adaptive thresholding optimised with improved arithmetic optimisation algorithm and guided filter is proposed. Initially, the input image is denoised using DWT. Then, the denoised image is again processed with a guided filter for enhancing and improving the denoising performance. Moreover, from the experimental analysis, it is proven that the proposed approach provides better performance than the existing denoising methods.
On Speeding Up Methods for Identifying Redundant Actions in Plans

Satisficing planning aims at generating plans that are not necessarily optimal. Often, minimising plan generation time negatively affects quality of generated plans. Acquiring plans quickly might be of critical importance in decision-making systems that operate nearly in realtime. However, (very) suboptimal plans might be expensive to execute and more prone to failures. Optimising plans after they are generated, in a spare time, can improve their quality. This paper focuses on speeding up the (Greedy) Action Elimination methods, which are used for identifying and removing redundant actions from plans in polynomial time. We present two enhancements of these methods: Plan Action Landmarks, actions that are not redundant in a given plan, and Action Cycles which are subsequences of actions which if removed do not affect the state trajectory after the last action of the cycle. We evaluate the introduced methods on benchmark problems from the Agile tracks of the International Planning Competition and on plans generated by several state-of-the-art planners, successful in the recent editions of the competition.
Security Policy

As a cloud native artifact registry, Harbor manages and distributes artifacts of applications. The security of the content directly affects the operation of the platform and environment where the content is distributed or deployed. Therefore, in addition to the security of Harbor itself, the security of the content managed by Harbor is particularly important. This chapter describes the capabilities provided by Harbor from the perspective of content security, including the content trust function for preventing content tampering and the static scanning mechanism for discovering security vulnerabilities. In addition, this chapter describes how to configure the security policy and the whitelist system of vulnerability based on content trust and vulnerability scanning. Finally, it shares some FAQs about the content security in Harbor.
Design of Data Mining Hybrid Teaching System based on Artificial Intelligence Era Algorithm

With the rapid development of computer technology, artificial intelligence has been paid more and more attention, and artificial intelligence technology has become one of the most popular technologies. In this paper, artificial intelligence is used as the technical means to combine artificial intelligence with hybrid courses, and the plant landscape course is taken as an example. In the plant landscape course, Superstar is used as the online course platform, and students can learn at anytime and anywhere through mobile phones, ipads, computers and other electronic products. It also analyzes the online learning of students in all aspects. In short, artificial intelligence plays an important role in the reform of modern education and teaching.
Robust Human Identity Anonymization using Pose Estimation

Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms. The human identity anonymization should be robust so that less manual intervention is needed, which remains a challenge for current face detection and anonymization systems. In this paper, we propose to use the skeleton generated from the state-of-the-art human pose estimation model to help localize human heads. We develop criteria to evaluate the performance and compare it with the face detection approach. We demonstrate that the proposed algorithm can reduce missed faces and thus better protect the identity information for the pedestrians. We also develop a confidence-based fusion method to further improve the performance.
Database Forensics for Analyzing Data Loss in Delayed Extraction Cases

This chapter focusses on the examination of seized iPhones for the data loss when the extraction is delayed. For law enforcement today, very few crimes are committed without having some nexus to a mobile device, and as such, mobile devices play a critical evidentiary role for investigations. More recently, law enforcement digital forensic labs have had the ability to purchase advanced digital forensic solutions that support accessing and extracting data from locked devices. When an iPhone has been left idle for a period of time, and the user is not interacting with the device, the phone will enter a state of clean up or vacuuming. This chapter presents multiple extractions from different locked After-First-Unlock (AFU) iPhones. A comparative analysis of the data extractions from each device was conducted to establish any reductions in the reported number of artifacts. When decreases were identified, the source database or file system storage location was examined in order to confirm the data loss. Additional timeline and log analysis also formed part of this analysis, in an effort to determine if a cause of the data loss could be identified.
Medical Record Laboratory Electronic Information System Design Analysis

This study aims to create a system design information record medical electronic where is the recording system medical electronic is system that provides information completeness of patient data and record medical During maintenance and storage all patient data. System management Archives in the Amanah Stikes Laboratory Record Medical not yet computerized. Patient data entry still uses paper, while recording and storage record medical done manually. Study this aim for making related apps with system information record medical electronic on Amanah Stikes laboratory. In research descriptive this we use approach qualitative, what d do on guarantor answer laboratory staff record medical. Based on from result study with doing questionnaire and observation, therefore then Relevant data is required with recording record medical take care road for example number record medical information, patient data info, and disease data info, in designing a system information record medical electronics.
2D Pose-guided Complete Silhouette Estimation of Human Body in Occlusion

Estimating complete human and hand silhouettes from a monocular RGB image has important roles in many visual applications such as image editing and action recognition. However, complicated scenes, non-rigid articulated deformations and occlusions make it a difficult task. To overcome the difficulties of inferring the invisible part of the human body due to objects’ occlusion, we propose a pose guided two stage deep neural network model. It leverages the key points semantic prior knowledge to help the model envision occluded areas, which is more in line with human’s visual recognition characteristics. The proposed model is validated on both hand and human datasets with varied occlusions. The experimental results are satisfying and better than the state-of-the-art methods.
Eccentric regularization: minimizing hyperspherical energy without explicit projection

Several regularization methods have recently been introduced which force the latent activations of an autoencoder or deep neural network to conform to either a Gaussian or hyperspherical distribution, or to minimize the implicit rank of the distribution in latent space. In the present work, we introduce a simple and novel regularizing loss function which simulates a pairwise repulsive force between items and an attractive force of each item toward the origin. We show that minimizing this loss function in isolation achieves a hyperspherical distribution, and demonstrate its effectiveness as a regularizer for an image auto-encoder. Moreover, a reduction in the regularization parameter leads to a modest increase in the eccentricity of the distribution in latent space. This enhances image generation, and allows the eigenvectors of the covariance matrix to be extracted as deep principal components, which can be used for data analysis, image generation, visualization and downstream classification.
PERENCANAAN INSTALASI PENERANGAN PADA KAPAL ISAP PRODUKSI PT. TIMAH (PERSERO) TBK

On the suction ship produced by PT. Timah (Persero) Tbk applies a lighting system using standards from the 2012 Minister of Health Decree regarding the level of lighting in the work environment. With this standard, it is expected that each manganese lighting will function properly according to the active needs of the task. The lamp used for lighting in each room is Philips TL-D 36 Watt with Pacific TCW097 type, so the lamp can last a long time because it is protected from dust which can reduce the quality of the lamp. The results of research on the use of energy, in this case electricity in shipping plays a very important role for the needs of manganese lighting. Therefore the lighting system of a room on the ship must be planned well taking into account the energy conservation efforts that can be done
Deep Transfer Learning for Model-Driven Signal Detection in Downlink MIMO-NOMA Systems

In this paper, a model-driven signal detection method with deep transfer learning (DTL) is proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Specifically, we first introduce some learnable parameters to an unfolded iterative algorithm for MIMO detection and improve it through a preconditioned process to speed up its convergence. Then we combine this modified algorithm with the successive interference cancellation (SIC) structure in NOMA detection to propose our learned preconditioned conjugate gradient descent network with SIC (LPCG-SIC). Furthermore, to improve the reusability of the trained network, a DTL-based detection algorithm and three model-driven transfer strategies are proposed for our LPCG-SIC detector. Simulation results show that the proposed detection network outperforms conventional detectors, and the transfer strategies can obtain significant performance gain compared to no-transfer methods.
ANALISIS PENGETAHUAN TEKNOLOGI PADA PENDEKATAN TPACK DI SEKOLAH DASAR

The problem faced in this study is how the activity of the technological capabilities of teachers and students in the TPACK approach of elementary school. The purpose of this research is to analyze the technological capabilities of teachers and students in learning with the approach of TPACK. This type of research is qualitative approach. The subjects of this study are teachers and Students of elementary school. Data collection techniques use observations, interviews and documentation. The results of teacher technology capability activities such as learning using software in the form of: Blog, Microsoft Teams, Microsoft 365, Google Meet, Zoom, etc.), the ability to operate technology, learn technology, integrate technology, and use software conference is categorized enough. Teachers still don't meet all categories. While the results of student activities such as learning using software in the form of: Blog, Microsoft Teams, Microsoft 365, Google Meet, Zoom, etc.), the ability to operate technology, learn technology, integrate technology, and use conference software is categorized very well. Because students almost all meet the category.
Bayesian Estimation of Scale Parameter of Power Gompertz Distribution

Zellner [2], Basu and Ebrahimi [3] have recognized that the inappropriateness of using symmetric loss function.Norstrom [4] introduced precautionary loss function which is given as
Analisis Keterampilan Berbicara Siswa Kelas IV pada Mata Pelajaran Bahasa Indonesia di SDN Rawa Buaya 07

This study aims to answer the results of the analysis and describe the speaking skills of fourth grade students in Indonesian subjects at SDN Rawa Buaya 07. The approach method used in this research is descriptive qualitative by taking data at SDN Rawa Buaya 07 with the research subjects being teachers and class students. IV. Data was collected by observation, interviews, oral tests and documentation. Data analysis was carried out through three stages of implementation, namely data reduction, data presentation and drawing conclusions. Checking the validity of the data used includes triangulation of sources, triangulation of data collection techniques, triangulation of time and guarantor of validity. The results obtained indicate that the speaking skills of fourth grade students in Indonesian subjects at SDN Rawa Buaya 07 are in the "still low" category because the overall average is still below the Minimum Completeness Criteria (KKM) in Indonesian subjects, which is 70. It would be nice, teachers should provide active and fun learning through the use of media or varied learning methods to support the effectiveness of students' speaking skills in the classroom and outside the classroom.