Reviewerly/exHarmony huggingface dataset:
Kegiatan penyediaan pelayanan perpustakaan dalam layanan unggah mandiri tugas akhir: studi kualitatif layanan unggah mandiri tugas akhir di Telkom University Open Library

This study was conducted against the background of the emergence of news regarding the disposal of a student's final scientific assignment by one of the university libraries in Indonesia which at that time had become the focus of attention. In addition, the developments that occurred in the handover service for students' final scientific assignment also became the background for this study. This study aims to determine the technical service activities consisting of collecting, processing, disseminating, and preserving that are carried out by the library in supporting the implementation of the final assignment self-upload service at Telkom University Open Library. The research method used in this study is a descriptive qualitative method. Data was collected using observation, interviews, and documentation techniques. The research informants are library staff of Telkom University Open Library. The validity of the data was tested by triangulation techniques and source triangulation. Data analysis was carried out by data reduction, data presentation, and concluding. The results of the study provide information that technical service activities in the final assignment self-upload service at the collecting stage are carried out by library staff by verifying documents and the “Surat Bebas Kewajiban Perpustakaan” (Library Liability Free Letter) administrative requirements which consist of free of library loans and fines, and donating a book following the provisions. Processing activities are carried out by the processing staff by inputting document catalogs, inputting journal articles of the final project to Tel-U e-Proceeding, and making FlipBooks. Disseminating activities are carried out by providing final project collections and Tel-U e-Proceeding. Information dissemination activities are carried out by holding literacy class and distributing infographic content with the aim of attracting use from library users. Preserving activities are carried out by making metadata backups, document backups in docx and PDF format on external hard disk, and maintaining library websites.
Analysis of the Status Quo of Popular Science Animation Education Based on Python

In the context of big data, China attaches great importance to the development of interdisciplinary cooperation. This article combines science animation and education through Python big data analysis, affirms the auxiliary advantages of science animation education, and analyzes the oversimplification of creative methods in science animation education during the development process. Singleness and lack of talents have put forward the concept of multi-dimensional innovation and development, promoted the cultivation of cross-cutting talents in popular science, and all parts of the country and enterprises have paid close attention to and coordinated development.
Progressive Evolution Scheme with Socialization Swarm for Privacy Blockchain

To enhance the dependability of lightweight blockchain, this paper presents a privacy protection mechanism in open networks with a secure ledger for each cell of society. We propose a swarm intelligence scheme called hierarchical RAFT (HRAFT), which accumulates valuable workloads about the credibility of all the nodes to sort for selecting candidates. Thus, the scheme achieves high-performance decentralization by progressive evolution consensus.
Digital Transformation of a Course Design in the Context of Pandemic

Automation of technological design in the implementation of course and diploma projects sets the task of creating and introducing into the higher educational process the necessary systems capable of working with large amounts of information stored in structured databases. This task becomes much more difficult when developing course and diploma projects during a pandemic. The difficulty lies in the fact that one have to work not in the local network of the university but support remote work with students who are at their place of residence via the Internet using client-server technology. At the same time, the mode of authorization of each user and reliable storage of intermediate and final design results should be supported. Experience of using the T-FLEX system:the Technology integrated into the T-FLEX DOC’s electronic document management system is considered on the example of designing a technological process for machining a specific part. The stages of technological design are consistently described.
Features of Internet fraud during the pandemic COVID-19

The scientific article is devoted to the study of the relevance of the issue of the spread of offenses on the Internet, which is an extremely relevant issue in the conditions of the development of modern society, especially during the global pandemic of COVID-19. This problem necessitates the study and research of the essence of cyber fraud as one of the most common criminal offenses committed in the global network and the features of its commission. Moreover, the problem of protecting Internet users both during work and during leisure time is more acute than ever. In particular, it is important to note the constituent elements of fraud on the Internet, as they express the peculiarities of the essence of this type of offense. In addition, the work highlights issues related to the cybercriminal and his criminological features, which, in particular, is important for the detection and prevention of cyberfraud. The prevalence of fraud on the Internet, depending on the age and gender of the offenders, also needs attention. Separately, it is worth noting the study of the problem of victims of cyber fraud, which again allows us to understand the prevalence of cyber fraud among Internet users. Recommendations for preventing and combating this type of crime were also made.
Face-capture with automatic blendshape generation

The goal of this paper is the development of a system for the production of performance-driven facial animation that automatically generates a blendshape model from input video frames. This simplifies the production of this kind of model to be used in animation projects. The proposed method is designed to be used without the need for expensive hardware, such that any computer with a webcam can run the system.
Optimizing Data Augmentation Policy Through Random Unidimensional Search

It is no secret among deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result. To that end, the community has seen many efforts to automate the process of finding the perfect augmentation procedure for any task at hand. Unfortunately, even recent cutting-edge methods bring massive computational overhead, requiring as many as 100 full model trainings to settle on an ideal configuration. We show how to achieve equivalent performance using just 6 trainings with Random Unidimensional Augmentation. Source code is available at https://github.com/fastestimator/RUA .
Autonomous Learning Rate Optimization for Deep Learning

A significant question in deep learning is: what should that learning rate be? The answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover, the long hours spent carefully crafting the perfect learning rate can be more demanding than optimizing network architecture itself. Advancing automated machine learning, we propose a new answer to the great learning rate question: the Autonomous Learning Rate Controller. Source code is available at https://github.com/fastestimator/ARC/tree/v1.0 .
Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta

In this study, the authors conducted an analysis of the quality of fiber optic network maintenance based on attenuation value and maintenance time using fuzzy Mamdani logic and simulated using Matlab software, to improve accuracy in drawing conclusions on maintaining quality. This study uses a quantitative method, in which the author obtains a summary of customer data from PT. Telkom Indonesia in a period of 4 months of observation from August to November 2021. In August there were 776 customers, in September there were 362 customers, in October there were 359 customers, and in November 445 customers who underwent Indihome fiber optic cable maintenance. The test results with the centroid method with an input Handling Time of 1.5 hours and an Attenuation of 15 dB, then the output Repair Quality is 5.5 or categorized as Good. The greater the attenuation value generated, the more time it takes to maintain the IndiHome internet network disturbance. This is due to the many technical maintenance of fiber optic cables carried out by technicians to adjust for damage/trouble in the field. It is expected that maintenance can be carried out routinely in order to avoid fatal internet disturbances on the customer's side, and maximize maintenance time according to the dosage determined by the company, which is less than 3 hours, taking into account the work performance of technicians and also the quality of maintenance.
Adapted Generalized Unsharp Masking Algorithm for Sharpness Improvement of Scanning Electron Microscopy Images

Scanning electron microscope (SEM) images are highly valuable in different scientific applications because they can depict extremely small entities. SEM images are sometimes obtained blurry, in that such an issue reduces the clarity and hampers the detection of important features in the image. One way of processing the unwanted blurring effect is to use image sharpening, which aims at emphasizing the edges so that the output appears more significant to the observer with better-highlighted details. Many image sharpening methods exist, but not all are efficient as they may introduce artifacts, unnatural appearance, contrast/brightness modifications, or can be complicated and require a high computational cost. One algorithm of interest is the generalized unsharp mask (GUSM), which avoids the generation of artifacts that many sharpening methods own and have a somewhat simple structure. Still, when the GUSM algorithm is applied to different SEM images, it provides an unnatural sharpness and modifies the contrast and brightness as well. This is undesirable because proper sharpening is required for SEM images as they depict important information. Hence, an adapted GUSM algorithm is introduced in this article in that it provides a more natural sharpening without modifying the brightness or contrast of the filtered images. The main contribution of this study is to remove the contrast enhancement procedure and replace the smoothing process to deliver more natural sharpness. The developed AGUSM algorithm is verified with different real-unclear SEM images, its performance is appraised against different image sharpening methods, and the outcomes of comparisons are evaluated by utilizing advanced metrics. For the performed experiments, the AGUSM provided satisfying performances as the outcomes appear to have more acutance and look more natural when compared to the original counterparts and the outcomes of the comparison methods.
Attribute Based Encryption in Healthcare Application

Data volumes are increasing due to the growth of technologies such as IoT, Cloud Computing, and mobile internet. Data encryption is the solution, which protects data privacy by giving specific access to encrypted data can be better understood using Attribute-based (ABE) encryption. When patients use web-based commercial systems to store their personal health data, the privacy of personal health records (PHRs) can be a major problem. When it comes to establishing policies to control access and protect data confidentiality, common access control systems, such as Work-Based Access Control, have major limitations. In this research work a novel healthcare application has been developed based on the ABE scheme approach. Advanced Encryption Standard (AES) algorithm and Secure Hash Algorithm (SHA) are used for the encryption and decryption processes in the ABE scheme. This allows only designated authorized personnel, such as a patient or their respective doctors, to access personal data recorded in medical records, with a vision to improve the privacy and security of user details.
Echo Answers in Chinese

Abstract This paper studies echo answers to yes-no questions in Chinese with respect to their distributions, derivations, and typological patterns. We reconsider Simpson’s (2015) verb-raising analyses of verb echo answers (VEAs), finding that his analysis is inspiring in describing the significant properties of VEAs but that he has not discussed issues pertinent to the role of le in VEAs, adverbial echo answers, and multiple verb echo answers. This paper proposes that the derivation of short echo answers in Chinese is two-fold: the verb echo answer involves V-to- v movement, pro -drop, and VP-deletion; and the adverbial short answer involves focus movement and TP deletion. The aspectual markers, the post-verbal le1 denoting perfective and the sentence-final le2 encoding a change of state in Chinese, play a crucial role in determining the grammaticality of certain verb-echo answers via aspectual anchoring. This analysis avoids analytical problems posed by V-to-C (domain) (Holmberg 2016; Simpson 2015, etc.). Evidence from information focus and negative scope reversal supports this two-track analysis of echo answers in Chinese. Finally, we conclude that typologically, Chinese, a language using verb-echo answers, is close to Vietnamese and Finnish in allowing adverbial short answers and akin to Thai and Finnish in allowing multiple verb echo answers.
Peluang dan Tantangan Pembelajaran di Masa Pandemi Melalui Pemanfaatan Aplikasi Google Meet di Desa Sibrama Kecamatan Kemranjen

Education is a process of activities to transfer knowledge from educators to students. Until now, education is carried out online, so there is a need for application programs to support learning programs, one of which is the Google Meet application. The google meet application is one of the learning support application programs that can be done audio-visually. This study aims to provide information related to learning opportunities and challenges during the pandemic through the google meet application. This research is a field research with qualitative research methods and based on a phenomenological approach. The phenomenological approach is an approach that is based on the experience of human life. The research subjects are high school students, especially in Sibrama Village. The data were obtained through interviews conducted through google forms which contained questions related to the phenomena that the authors studied. Apart from this, the author also obtains data from documents or previous research related to what the author is researching. The author analyzes, describes, and summarizes some of the data obtained. The data analysis method used is qualitative data analysis with descriptive-analytical techniques. The results of the study show that special students in Sibrama Village still rarely use the google meet application by educators for learning, while the application that is often used is the WhatsApp Group application.
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization

Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generating domain invariant features from various source domains. However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored. Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise. The proposed domain expert features lie in a learned latent space where the images in each domain can be classified independently, enabling the implicit use of classification-aware domain variations. Based on the analysis, we proposed a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images and aggregate the source domain expert features for representing the target test domain. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space. Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet, and TerraIncognita demonstrate the competitive performance of our method compared to the recently proposed alternatives.
Research and Engineering Realization of Communication System Construction Based on CRC Algorithm

Crc: as the most common error-checking cyclic code in data communication and data compression, belongs to one of the most critical contents of linear block codes. The practical structure is completely built on the basis of finite fields, and can be accurately described by abstract algebra. Therefore, based on the understanding of relevant theories and the development trend of communication system construction, this paper clarified the engineering algorithm of CRC for the relationship between cyclic redundancy code and cyclic code, and then carried out an in-depth discussion on the implementation of general system construction engineering with CRC algorithm as the core, and finally studied the application performance of the algorithm. Practice results show that CRC algorithm is superior to other methods in performance and cost, so it can be widely used in the field of data storage and data communication.
Research of Task Complexity Decision System for Manual Assembly Tasks Using Fuzzy Cognitive Maps to Construct Bayesian Networks

Abstract With the evolution towards intelligent manufacturing, Industry 4.0 has revolutionized and gradually improved product quality and efficiency. When applied to the assembly sector, Assembly 4.0 has also attracted widespread attention. Assembly 4.0 requires lean production, automated learning, and the ability to solve real-time design decisions through technology and convert the direct labor of operators into management information and decisions. Therefore, to better assess the complexity of manual assembly tasks, we believe it is necessary to build a multi-view and multi-dimensional decision-making system from the perspective of manual assembly. For this purpose, we propose in this study, a Bayesian network model based on fuzzy cognitive maps. Simultaneously, we use Genie3.0 to build the Bayesian network model. For optimum quantization parameter values as well as conditional probability directions’ optimization between the parent node and the child node of the Bayesian network, we use the max-min hill-climbing (MMHC) algorithm and the Expectation Maximum (EM) algorithm. The influencing factors can be managed through the Bayesian network using this model, and the users can fully understand the changing rules among task complexity factors. This model can also provide theoretical and technical support for users, starting from the initial stage of product conception down to the completion of production.
Knowledge-enhanced Neural Machine Reasoning: A Review

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications. Over the past few years, plenty of studies have leveraged various forms of external knowledge to augment the reasoning capabilities of deep models, tackling challenges such as effective knowledge integration, implicit knowledge mining, and problems of tractability and optimization. However, there is a dearth of a comprehensive technical review of the existing knowledge-enhanced reasoning techniques across the diverse range of application domains. This survey provides an in-depth examination of recent advancements in the field, introducing a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We systematically discuss these methods and highlight their correlations, strengths, and limitations. Finally, we elucidate the current application domains and provide insight into promising prospects for future research.
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity

We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves $N$ agents interacting with environments that share the same state and action space but differ in their reward functions and state transition kernels. Assuming agents can communicate via a central server, we ask: Does exchanging information expedite the process of evaluating a common policy? To answer this question, we provide the first comprehensive finite-time analysis of a federated temporal difference (TD) learning algorithm with linear function approximation, while accounting for Markovian sampling, heterogeneity in the agents' environments, and multiple local updates to save communication. Our analysis crucially relies on several novel ingredients: (i) deriving perturbation bounds on TD fixed points as a function of the heterogeneity in the agents' underlying Markov decision processes (MDPs); (ii) introducing a virtual MDP to closely approximate the dynamics of the federated TD algorithm; and (iii) using the virtual MDP to make explicit connections to federated optimization. Putting these pieces together, we rigorously prove that in a low-heterogeneity regime, exchanging model estimates leads to linear convergence speedups in the number of agents.
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI

Cybersickness is a common ailment associated with virtual reality (VR) user experiences. Several automated methods exist based on machine learning (ML) and deep learning (DL) to detect cybersickness. However, most of these cybersickness detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone energy-constrained VR head-mounted devices (HMDs). In this work, we present an explainable artificial intelligence (XAI)-based framework, LiteVR, for cybersickness detection, explaining the model's outcome and reducing the feature dimensions and overall computational costs. First, we develop three cybersickness DL models based on long-term short-term memory (LSTM), gated recurrent unit (GRU), and multilayer perceptron (MLP). Then, we employed a post-hoc explanation, such as SHapley Additive Explanations (SHAP), to explain the results and extract the most dominant features of cybersickness. Finally, we retrain the DL models with the reduced number of features. Our results show that eye-tracking features are the most dominant for cybersickness detection. Furthermore, based on the XAI-based feature ranking and dimensionality reduction, we significantly reduce the model's size by up to 4.3x, training time by up to 5.6x, and its inference time by up to 3.8x, with higher cybersickness detection accuracy and low regression error (i.e., on Fast Motion Scale (FMS)). Our proposed lite LSTM model obtained an accuracy of 94% in classifying cybersickness and regressing (i.e., FMS 1-10) with a Root Mean Square Error (RMSE) of 0.30, which outperforms the state-of-the-art. Our proposed LiteVR framework can help researchers and practitioners analyze, detect, and deploy their DL-based cybersickness detection models in standalone VR HMDs.