Reviewerly/exHarmony huggingface dataset:
LAYANAN INFORMASI DIGITAL SEBAGAI STUDI PENELUSURAN ALUMNI S1 PROGRAM STUDI PPKn UNIVERSITAS TERBUKA

The purpose of this study is to analyze the benefits of digital information services as a search for S1 alumni of the Universitas Terbuka PPKn Study Program.The research method used is the Mixed Method (quantitative and qualitative). The survey method was carried out on graduates from the PPKn S1 study program who graduated from 1998-2013 through a questionnaire sent using a google form. While the qualitative method is carried out through interviews conducted by interviewers who have attended qualitative research training at UT. The instrument was developed by the UT Study Tracer Team by involving expert reviewers. Data was collected in two ways, namely filling out a questionnaire and interviewing. The questionnaires were filled out for the selected sample graduates, graduate supervisors, and graduate colleagues.The results showed that the existence of digital information services as a search for S1 alumni of the Open University Civics Study Program received a good response from users. The alumni find it easy to fill in and get the data needed by alumni. Graduates of the UT PPKn Undergraduate Program received a fairly good appreciation in competitiveness with other university graduates, while for further studies they felt satisfied with the PPKn S1 graduates. they are satisfied because the substance of the field of knowledge obtained in the Civics Undergraduate Study Program because it is in accordance with the needs of the work, there are only a few courses that are not very relevant to the teaching material in junior high school.
Research on Fault Diagnosis Method of Analog Circuits of Important Equipment in Nuclear Power Plant

Large-scale steam turbine generator sets are important equipment of nuclear power plants, and theoretical research on fault diagnosis of steam turbine thrust bearings and safe service guarantees is essential to improve the safety of nuclear power plants. In this paper, we present a hybrid whale optimization algorithm (H-WOA), which is applied in solving an analog circuit fault diagnosis problem. First, chaotic variables are adopted to accelerate the convergence speed of the algorithm. Second, in order to enhance the exploration capability of the algorithm and improve the diversity of the whale population to avoid being trapped into local optimum, we propose a random search strategy. The performances of the H-WOA algorithm are tested with eight numerical benchmark functions. This algorithm helps to improve the safety and reliability of Nuclear Power Plants.
Refined Search and Attribute-Based Encryption Over the Cloud Data

In recent years, cloud technology is used everywhere to store data. For data security reasons, the data are encrypted, but this adds complexity in searching the data in cloud which is in encrypted form. Generally, searching a data is done using the single keyword search, but the data retrieved is not precise. Also, this results in high searching cost due to scanning of huge amount of data. The search result will be adulterated search and lack of security. The time token methodology also adds more process time to the computation. Lack of rank mechanism is also a major drawback here, as user takes long time to select the data. We have proposed multi-keyword ranked search, and attribute-based encryption. This technique will help us to give a refined search and also, it encrypts the keyword that is used for searching in such a way that the keywords are not exposed for any privacy concerns.
Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are, in their most basic form, two neural networks that teach each other how to solve a specific task. The idea was invented by Goodfellow and colleagues in 2014. The two networks help each other with the final goal of being able to generate new data that looks like the data used for training. For example, you may want to train a network to generate human faces that are as realistic as possible. In this case, one network will generate human faces as good as it can, and the second network will criticize the results and tell the first network how to improve upon the faces. The two networks learn from each other, so to speak. This chapter looks in detail at how this works and explains how to implement an easy example in Keras.
Identification Schemes

Identification schemes are one of the most important types of scheme used in cryptography. There are two entities, namely, the prover and the verifier, in a scheme. The prover tries to prove its identity to the verifier in such a way that the prover cannot be impersonated by an adversary either in the process of demonstrating its identity or after the demonstration. Once the identity is confirmed, not even the verifier can alter the prover’s identity. The process of proving the identity may end with acceptance or rejection (termination without acceptance).
Keywords-Driven Paper Recommendation Based on Mobile Edge Computing Environment Framework

In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>w</mi> </math> (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users’ query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions.
Developing an Android-Based English Reading Material for the Eighth Grade Students in Mtsn Karangasem

This study intended to find out the needs of the eighth-grade students in learning English and to develop an android-based application for learning English reading material in MTsN Karangasem. This study used the design and development research design model proposed by Richey &amp; Klein (2014) which consisted of three major steps namely; 1) Design, 2) Development, 3) Evaluation. The subjects of this study were the eighth grade students of MTsN Karangasem, the English teacher in MTsN Karangasem, and integrated technology experts. The instruments used in this study were questionnaire, interview guide, and product evaluation sheets. The data were collected and analyzed qualitatively and quantitatively. The results showed that the students’ motivation to learn English was lack and need more learning resources or materials to learn reading comprehension. The eighth-grade students need a smartphone application that provides reading material learned in the class. The material developed in this study was limited to Recount Text material. The quality of the application got a high score from experts, which categorized in the excellent category. The use of the effective application with meaningful learning activities for learning reading comprehension for the eighth-grade students is strongly suggested
Use of Set Shaping Theory in the Development of Locally Testable Codes

In developing locally testable codes, information is added to the coded message by creating redundancy in the codewords. In this article, we propose an alternative method in which redundancy is introduced on the message that must be transmitted before its encoding and not in the codewords. This approach exploits the Set Shaping Theory whose goal is the study the bijection functions f(X)=Y that transform a set of strings into a set of equal size made up of strings of greater length. In this way, this type of function transforms the independent variable x into the dependent variable y whose emission probability is conditioned by the previously emitted variables. Thus, if the decoder decodes a symbol associated with a conditional probability equal to zero, we detect an error in the message. If the function f used is the one that minimizes the average information content, we develop a code that can be tested efficiently. In fact, it is observed, in terms of compression, that the greater length of the strings is compensated by the fact of having chosen the strings with less entropy.
Multi robot navigation formation simulation based on ROS

Abstract Multi-robot collaboration is an important development direction of current robotics technology. This paper proposes a navigator and follower model formation control framework that integrates path planning and trajectory tracking, and conducts experiments in the gazebo simulation environment to realize the simulation of navigation and formation led by the navigator in the simulation environment indoors.
Types and Modes of Objects

R objects exist within an object system. R currently uses three object systems: S3, S4, and Reference Class. S3, S4, and Reference Class are all object-oriented systems. S3 is the simplest with informal classes. S4 is a newer system and uses formal classes that are associated with methods. Reference Class uses formal classes that are associated with both methods and environments. The three versions run concurrently in R. The writers at CRAN say that Reference Class is easier to code than S4, but both require a solid knowledge of S3 to work with. This book includes some Reference Class and S4 syntax but focuses on S3 syntax.
Withdrawal

Abstract Withdrawal: Akos, P., Joshua Leonard, A., &amp; Hutson, B. (2022). Virtual federal work study and student career development. The Career Development Quarterly , 00, 1–11. https://doi.org/10.1002/cdq.12288 The above article, published online on 28 April 2022 in Wiley Online Library ( https://onlinelibrary.wiley.com ), has been withdrawn by agreement between the authors, Editor Mei Tang, and Wiley Periodicals, LLC. The article was published in error. The withdrawn article is a duplicate of: Akos, P., Leonard, A. J., &amp; Hutson, B. (2022). Virtual federal work‐study and student career development. The Career Development Quarterly , 70, 16–26. https://doi.org/10.1002/cdq.12281 . The original article, published online on 19 November 2021 in Wiley Online Library, can be found here: https://onlinelibrary.wiley.com/doi/10.1002/cdq.12281 .
Conflict Misunderstanding in the Net Information Society

The increased technological capabilities of communication digital means, the speed of information dissemination, and the disappearance of many barriers to its exchange lead to an increase in a mutual misunderstanding between conflicting groups. The indisputable authority of scientific rationality in the information society does not contribute to the improvement of mutual understanding between people. Socio-political conflicts are interpreted by people not only using scientific but also everyday, practical thinking. Practical thinking acts as a function of a living system, the main goal of which is self-preservation. However, it is self-preservation not of an individual organism, but a population or a group. Practical thinking interprets the world as, first of all, a scene of actions of subjects who can independently make decisions and bear moral responsibility. Much less importance is attached to objective processes in this situation. As a result, the everyday thinking of people is permeated with anthropomorphism which was quite functional for previous historical eras. But in combination with telecommunications in the information society, it creates a kind of explosive mixture.
COVID-19 Contact Tracing Query Processing in IoT Using Spark

The outbreak of infectious diseases like COVID-19, Spanish Flu, and Ebola represents a threat to governments and medical sectors worldwide due to millions of deaths caused by these diseases. Besides, infectious diseases reduce economic growth due to fear for workers to go to their workplaces. As a result, interventions to reduce the onward transmission of infectious diseases are a must. In this regard, the contact tracing process can be considered as a mitigation tool that is used in fighting the spread of infectious diseases. Meanwhile, the usersâ€™ traces data-sets grow exponentially with time; processing contact tracing queries over these data sets is a big challenge. This paper proposes a full-fledged technique that implements a contact tracing process and retrieves all suspected contacts who are exposed to the infected patient either low-risk or high-risk contacts or both of them. Moreover, the proposed technique utilizes the trajectories of contacts to reveal if they are suspected cases or not, the technique analyzes these trajectories based on two factors named; exposure time and proximate social distance, and based on the results of these values, the technique can identify the case of the contact. Besides, the designed technique takes full advantage of the spark framework to handle scalability challenges for answering contact tracing queries over a large scale of traces efficiently. Experimental evaluation of the proposed solution with big real data sets illustrates the effectiveness and scalability of this technique.
An improved adaptive genetic algorithm for job shop scheduling problem

Job shop scheduling problem is to find the optimal jobs sequence, which minimize the expected makespan. In solving NP-H problems such as job shop scheduling problems by genetic algorithm, trapping in local extremum, low search efficiency and instability are often encountered. In order to restrain this condition, an improved adaptive genetic algorithm based on sigmoid function was put forward. The crossover probability and mutation probability can be adjusted in nonlinear and adaptive based on the dispersion of the fitness of population in the evolution, which is better able to generate new individuals to get rid of the local extremum search to the global optimal solution. To research the performance of the improved adaptive genetic algorithm in solving job shop scheduling problems, a detailed application scheme was given out for the process of it. In the solving scheme, the chromosome decoding algorithm with the objective function of makespan was proposed. Ten JSP benchmark instances were solved with the evaluation index of solution accuracy, convergence efficiency and solution time by the simulation of MATLAB. Through the experimental results of comparing with the other three adaptive methods, the improved adaptive genetic algorithm has been significant improvement in solution accuracy and convergence efficiency.
Neural Style Transfer Using ResNet50V2

The test of taking a content picture and a style picture as info and taking a picture with the content of the content picture and the style of the style picture is known as Neural Style Transfer (NST). The most often used approach for Neural Style Transfer is an Artificial Neural Networks (ANN) that utilizes ResNet50V2 and TensorFlow. Our investigation sheds intelligence on how Artificial Neural Networks (ANNs) figure out how to address filmland and how they may be utilized to make and alter elevated place pictures.
A Novel Sine Cosine Algorithm for Global Optimization

Sine cosine algorithm (SCA) has a fast convergence speed and is easy to implement. In order to overcome the evolutionary stagnation of swarm, this paper presents a novel SCA (NSCA) in which three learning strategies are used to update individuals and a selection mechanism is developed to guide each individual to choose a proper updating strategy. The selection mechanism is designed by the credit assignment method and Upper Confidence Bound (UCB). The proposed algorithm has been experimentally validated on 18 benchmark functions. Compared with SCA variants and other swarm intelligence algorithms, experimental results show NSCA is competitive in solving most functions.
TxChain: Scaling Sharded Decentralized Ledger via Chained Transaction Sequences

The blockchain has become the most prevalent distributed ledger (DL). Sharding has emerged as a major solution to the scalability bottleneck of DLs. From the underlying data structure of existing sharding schemes, although the Directed Acyclic Graph (DAG)-based topology improves the scalability of DLs compared to chained blocks, the security and reliability of consensus mechanisms in DAG-based DLs have not been verified. Moreover, these schemes suffer from high communication overhead when scaling out. To address these issues, we propose a sharded DL named TxChain, which adopts a novel data structure manipulated by the unit of transaction and constituted by chained transaction sequences of each account. TxChain optimistically processes concurrent transactions and ensures the consistency of all shards via transaction sequence conversion (TSC)-based consensus mechanism. Shards maintain the full replica of TxChain and execute transactions by trustworthy validators, which reduce the frequency of communication with other shards. We theoretically prove the consistency of shards maintained by TSC and demonstrate TxChain’s throughput scales with low latency through extensive experiments.
Bringing It All Together

The past chapters in this book have introduced data analysis methods, feature extraction techniques, and traditional machine learning and deep learning techniques. We have conducted multiple experiments on numeric, textual, and visual data and found how to analyze and tweak the performance.
Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare.

The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artificial intelligence, and in particular deep learning, has enabled researchers to gain new insights from large scale and multimodal data. At the 2022 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare", we showcase the latest research, influenced and inspired by the idea of using technology to build a more fair, tailored, and cost-effective healthcare system after the COVID-19 pandemic.
Multi-index Federated Aggregation Algorithm Based on Trusted Verification

Movited by the modern phenomenon of distributed data collected by edge devices at scale, federated learning can use the large amounts of training data from diverse users for better representation and generalization. To improve flexibility and scalability, we propose a new federated optimization algorithm, named as Multi-index federated aggregation algorithm based on trusted verfication(TVFedmul). TVFedmul is optimized based on Fedavg algorithm, which overcomes a series of problems caused by the original aggregation algorithm, which only takes the single index of data quantity as a reference factor to measure the aggregation weight of each client. The improved aggregation algorithm is based on multi-index measurement, which can reflect the comprehensive ability of clients more comprehensively, so as to make overall judgment. Further, we introduces hyperparameter α, which can be changed to determine the importance of the indexes. Finally, via extensive experimentation, the efficiency and effectiveness of the proposed algorithm is verified.
Comparing Qubit Platforms in the Race to Feasible Quantum Computing

Quantum computing is an emerging field that has been attracting a substantial amount of interest in the scientific community lately due to the advantages of quantum information processing. The fundamental unit of quantum information that makes up a quantum computer is a qubit. Several platforms have been proposed as physical realisations of these qubits for the purpose of making quantum computing a feasible technology, yet no one platform has significantly outperformed the others. This literature review discusses the current state of the field of quantum computing, by comparing and contrasting some of the most promising qubit platforms of today. By doing so, this paper analyses each of their feasibilities for full-scale quantum computing in the future and maps out a possible trajectory for how quantum computing may progress in the next few years
A Comparative Study of Word Embedding Techniques in Natural Language Processing

Natural language processing plays a crucial role in understanding and processing of large amount of unstructured data. NLP helps to take better decisions related to business, clinical decisions, product quality, etc. Word representation is an important area of research in NLP as these models are used in sentiment analysis, machine translation, text generation language modelling, etc. In this paper several word representation techniques of natural language processing are discussed and their features are mentioned. Two advanced word representation techniques like word2vec and Glove are implemented and compared for performance analysis in terms of loss and nearest words.
Pemanfaatan koleksi Perpustakaan Pusat Pendayagunaan Informatika dan Kawasan Strategis Nuklir BATAN menggunakan ISO 11620:2014

The library collection is an essential element of a library. The increasing turnover and collection usage by users in the library will affect the library's performance. The availability of collections in the library is an adequate number of collections and the availability of several collections owned by a library. This study aimed to determine utilization of the Central Library for Utilization of Informatics collection and the BATAN Nuclear Strategic Area using ISO 11620:2014. This study used a descriptive method with a quantitative approach, derived from numerical data of circulation services from 2017 to 2019. Results showed that the overall level of borrowing library materials decreased due to reduced direct user visits to libraries and digital libraries. The utilization level of library collections by the population to be served has increased significantly. Documents downloaded were six titles, and the highest download frequency was four times in 2018. Besides, documents downloaded were four titles, and the highest download frequency was 23 times in 2019. Users found the information they were interested in from electronic collections that were downloaded every year, namely radiography training exam questions. The presence of electronic documents is considered quite relevant and useful according to the user's information needs. Many collections of the Central Library for the Utilization of Informatics and the Nuclear Strategic Area of BATAN are not lent due to the availability of collections that are not yet relevant to users' needs. It significantly affects the turnover of the collection.