Reviewerly/exHarmony huggingface dataset:
High-resolution demodulation of interference envelope peak at arbitrary positions by warped discrete Fourier transform

We propose a novel approach to address the high-resolution problem for analyzing the peak position of an interference envelope in vertical-scanning wideband interferometry. Nonlinear envelope demodulation cannot be performed for arbitrary interference fringes because the envelope peak positions fluctuate depending on the acquisition statuses of the interference fringes. In the proposed method, first, the interference fringes are shifted until the desired results are obtained; this helps solve the discrepancy problem. Then, nonlinear envelope demodulation processing is applied to the selected shifted interference fringes. This paper describes the proposed procedure used in this study and its development using a shift selection scheme based on the envelope match characteristics of the shifted interference fringes. The proposed approach is introduced within the framework of super wideband light-based Michelson interferometry, and the experimental results demonstrate the superiority of the proposed algorithm over the conventional Fourier transform method for envelope peak resolution. The present technique is expected to be useful for the high-precision measurement of distances for not only scientific purposes but also industry requirements.
Robust Subgroup Multi-signatures for Consensus

Multi-signatures are used to attest that a given collection of n parties, indexed by their respective public keys, have all signed a given message. A recent popular application of multi-signatures is to be found in consensus protocols to attest that a qualified subset of a global set of n validators have reached agreement. In this paper, we point out that the traditional security model for multi-signatures is insufficient for this new application, as it assumes that every party in the set participates in every multi-signature computation and that is honest. None of these assumptions hold in the typical adversarial scenarios in consensus protocols (aka. byzantine agreement), where malicious players can launch a denial-of-service attack by injecting malformed individual signatures and cause liveness to halt. We address this by introducing a new multi-signature variant called robust subgroup multi-signatures, whereby any eligible subgroup of signers from the global set can produce a multi-signature on behalf of the group, in the presence of a byzantine adversary. We provide syntax and security definitions thereof and argue that existing unforgeability security proofs for multi-signatures do not carry over to the consensus setting; a consequence of this observation is that many multi-signature based consensus protocols are left lacking a rigorous security proof for correctness. To remediate this we propose several constructions which we prove secure under widely held cryptographic assumptions using our newly introduced formal definitions and also improve upon multi-signature computation time. Finally, we report on benchmarks from a proof-of-concept implementation.
ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING

Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members.
A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data

Digital transformation is a long process that changes the managing human profiles in both offline and online approaches. This generates the amount of huge data stored in both relational databases and many others like social networks or graph databases. To exploit effectively big data, several measures and algorithms in Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using a knowledge graph to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. Furthermore, techniques and algorithms on the graph are used to classify the nodes. The experiments in the knowledge graph implemented to illustrate the proposed model. The main contribution in this paper is to identify the right persons among complex-relational groups, locations in real-time based on large datasets on the social networks, relational databases and graph databases.
Network Situation Risk Assessment Based on Vulnerability Correlation Analysis

For the question that situation assessment methods for the analysis of existing vulnerabilities are associated with the lack of analysis of vulnerability assessments, which leads to the poor accuracy assessment, the paper presents a method for network vulnerabilities associated with risk assessment situation analysis. The method improves the existing hierarchical network situation assessment, with the system being divided into three levels, which are loopholes at the bottom, host in the middle, and network system at the top. Based on the security risk indices, we calculate the vulnerability, the host, the entire network system risk index, and evaluate and analyze the security posture of the entire network, to solve the problem of inaccurate assessment. The experiments show that the method improves the accuracy of the assessment of network situation assessment greatly.
APPLICATION OF BIG DATA ANALYSIS IN PATH PLANNING OF INTELLIGENT PICKING ROBOT

In order to solve the problem of autonomous movement of intelligent picking robot, a method for big data analysis is proposed. The data collected by laser ranging sensor, CCD camera and electronic compass are analyzed to determine the current robot position and heading. The walking route for the robot is preset of the orchard. The sign for the digital tube is installed, so the coordinates for the sign are known. Electronic compass is used to determine the heading angle of robot. The CCD camera captures the sign image, and adopts the methods of image graying, image segmentation, image corrosion and image thinning to extract the digital tube image data on the sign. Fuzzy control method is used to identify digital tube numbers on the ground sign, and CCD camera scans whether there are obstacles in front of the road at the same time. The laser ranging sensor completes two tasks: ① detecting the sign distance and direction angle of the distance, and calculating the current position and heading angle of the robot through the coordinates of the sign; (2) detect the distance from obstacles, and start the obstacle avoidance system when it is less than 1m. The path planning system of picking robot based on big data analysis relies on a variety of sensors, and has a strong ability to perceive orchard environment, and it has high ability of path planning.
Illustrations for Encyclopedia Erupting Mountain Theme as Visual Culture

Indonesia's geographical location in the growth zone of three plates namely Eurasia, Indo-Australia and Pacifica or commonly known as ring of fire, causes Indonesia to have many active volcanoes. The study intends to determine the design of illustrations in the encyclopedia of volcanoes as an alternative learning media for for inspiring the students. To collect detailed actual information that describes the symptoms of nature as well as the situation of social symptom conditions then this research is carried out with descriptive qualitative and quantitative research methods. This study uses illustration theory and visual culture as the main reference literature. Visual culture is an interdisciplinary field with visual concepts as the basis for the study of knowledge and understanding. One part of visual culture that has a very important role in this research is illustration. Illustration design is conveyed through drawings, illustrations, or photos about disaster situations and conditions that are equipped with narratives though effective language. It is expected that by the illustrations through visual images and combined with the narrative the interest of students, especially children can be more aroused so that the information presented can be more quickly captured and understood.With the availability of the book Encyclopedia of Mount Erupting for elementary school students, it is expected that knowledge about the erupting mountain can be conveyed interestingly so that children can have better understanding of the causes and how to deal with the disaster of erupting mountains. Keywords:Encyclopedia, Illustration, Book, Mountain Eruption, Visual Culture.
Comparison of backpropagation and ERNN methods in predicting corn production

East Java is one of the producers of food crops in Indonesia. Some food crop commodities in East Java Province are corn, soybeans, peanuts, sweet potatoes, and cassava. These food crops have many benefits to make the demand for production increase. The uncertain amount of food crop production will be a problem for the Department of Agriculture and Food Security of East Java Province in determining a policy. To overcome this problem, a system is needed to predict the production of food crops in East Java. This study compares the Backpropagation algorithm and Elman Recurrent Neural Networks (ERNN). The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 per quarter. The result of this research is that trial scenario 1 produces the best MSE value of 0.00000063 on the Backpropagation algorithm compared to ERNN which only gets an MSE value of 0.00000627. Trial scenario 2 produces the best MSE value, which is 0.000000003 in the Backpropagation algorithm with gradient descent momentum, this is also better when compared to ERNN which gets an MSE value of 0.00000407. It can be concluded that the best algorithm in this study is Backpropagation with gradient descent momentum because it produces MSE values ​​with good prediction results from all algorithms compared.
COVID19, SOCIAL JUSTICE AND EDUCATION

The 2019 health crisis emerged in an environment of injustice and inequality in all parts of the world. It has shown once again the severity of inequalities within most countries, especially those in the South. Most education institutions around the world are under enormous pressure to ensure continuity of educational plans by applying new technologies, which has posed a huge problem of fairness in access to technology and the availability of technological infrastructure in some countries, such as Morocco. This research tries to analyse the complex relationship between social justice and educational system, especially E-learning and blended education. The overarching question posed is how the educational system should change and be aligned with social justice in order to reduce inequality, discrimination and injustice. Whereas this is an ideal and challenging goal, which must be the target of all countries, especially the 21st century has begun with large social inequality and regional disparity. The methodology adopted was a mixed approach. We present a theoretical analysis of the relationship between social justice and education, as well as a quantitative analysis of data collected in a study conducted in the Béni Mellal-Khénifra region of Morocco using a questionnaire administered to 133 families. The main conclusion of this study is that policymakers must put in place the means to reduce digital inequalities before the situation worse in the future, namely (i) provide digital facilities to every student, regardless of their socio-economic status; (ii) ensure a fundamental digital right by offering equal and fair access to digital technology, and (iii) encourage industrialisation and digitalisation of the economic system.
PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.
Improve the design and testing of fuzzy systems with a set of (almost) simple rules

Prof. Dzitac used to say, ‘The mathematics of fuzzy systems is not fuzzy’. We discuss several limits and potential errors in the design of fuzzy logic systems and how they can be corrected or avoided. Examples from the literature are presented.
Navigation and Path Planning of an Autonomous Mobile Robot

Abstract Autonomously navigating robots are used in many applications including assistive robotics, military, space exploration, manufacturing, etc. Unmanned ground vehicles (UGV) are an example of autonomous systems falling under the category of navigation, where navigation is dominantly composed of automatic transport and movement in real world environments. Simultaneous Localization and Mapping (SLAM) provides the best approach to the problems faced in unknown environments. Visual based cameras, light detection and ranging (LiDAR) sensors, global positioning systems, and inertial measuring units (IMU) feed constant streams of data to the SLAM algorithm. These sensors allow the UGV to explore an unknown outdoor environment whilst traversing around obstacles. The objects focused are construction barrels, cones, ramps, flags, and white lanes. Utilizing open source ROS packages, the UGV navigation algorithm uses 2D LiDAR, IMU, GPS, and depth camera data to combine the sensor inputs for a reliable robotic system. This paper demonstrates a robotic system combining RGB depth camera, object detection, and conventional outdoor SLAM navigation in unknown environments. Once a ramp or flag is detected an alternative path is implemented that is different from the global GPS path. The UGV combines all these methods to reliably explore unknown environments without the need for teleoperation.
Recognition Model of Counterfeiting Digital Records of Biometric Photographic Image

Biometric portrait as one of the most important means of identifying requirements through strict definition of dimensional relationships, preservation of realistic information about all technical characteristics of the photographic image, so that all biometric values can be digitized and used in recognition. The great variety and accessibility of applications for digital processing of digital record of a photographic image has enabled a visually convincing display of a forged photograph that leaves a different impression on the viewer and transmits a different, that is, a forged message. Due to the need to prove the authenticity of the digital record of the photographic image, methods have been developed for the analysis of the record that can detect deviations from the real record even when there are no visual signs of processing the photographic image. Not all analysis techniques can detect certain methods of photo manipulation, so multiple digital photography detection and analysis techniques need to be applied. In order to prove its authenticity, the scientific paper deals with methods for analysis and detection of forgery of digital photography with respect to the digital record and the structure of JPEG format.
Decentralized Collision Avoidance via Fuzzy Potential Fields

An intelligent and cooperative collision avoidance method is proposed based on a combination of enhanced potential fields and fuzzy inference systems (FIS), where a genetic algorithm is used to optimize the FISs. The proposed approach provides a near-optimal and collision-free path in an environment with static and dynamic obstacles taking into consideration potential uncertainties. Furthermore, it is able to resolve local minima and goal non-reachable with obstacles nearby issues that exist in the traditional artificial potential field approach with minimum computational burden. A simple scenario modeled based on the issues faced by UAVs in such environments is used for training the system, and a complex scenario containing a number of dynamic UAVs in the presence of static obstacles is considered to validate the performance of the proposed approach.
Factors Affecting Cyber Security Awareness in Combating Cyber Crime in Kenyan Public Universities

Over the years, cyber-based attacks have become widespread and are growing rapidly with the increasing use of technology.As a result, cyber security is best understood as a process designed to keep cyberspace safe from potentially known and unknown threats.Therefore, cyber security awareness is vital as it plays an important role in preventing these cyber attacks due to the increasing use of information and communication technologies (ICT) as well as e-learning platforms in universities.Therefore, the paper assesses the key factors influencing cyber security awareness in the fight against cybercrime in the context of Kenyan public universities.A survey tested academic staff from 31 public universities for the variable cyber security awareness, the variable cyber security self-perception, and the factors influencing cyber security awareness.The results show that a significant number of respondents do not have adequate cyber security training.The paper also highlights that most public universities do not have a mandated cyber security policy, as well as adequate infrastructure for ICT cyber security practices.
Link Prediction in Social Network using Artificial Neural Network

The prediction process from prior information of the event helps to know the evolution of social network and assists the companies in effective decisions making during a typical recommendation system.Social network connection prediction is an efficient technique for the analysis of the evolution of social organizations and formation of the social network relations.Link prediction is a crucial research direction within the field of complex networks and data processing.In this work, we proposed a technique which is based on node similarity measure and Artificial Neural Network concept.We consider the features or information which are available in data set then assign the score to all pairs of nodes and apply Artificial Neural Network concept to the system.The proposed technique will improve the accuracy of Link Prediction.
CA-BPEL: A New Approach to Facilitate the Development and Execution of Context-Aware Service Orchestrations

The proliferation of smartphones and sensor-based networks has led to a greater need for contextaware applications and pervasive business processes.One of the key approaches that seek to satisfy this need is context-aware service composition.Service composition can be achieved in two ways, i.e., service choreography and service orchestration.Embedding the context into an orchestrated composite service enhances its flexibility, but makes its development and execution more complicated.This study aims to reduce this complexity by introducing the CA-BPEL approach.Our proposed approach enables developers to turn a standard orchestrated service into a context-aware orchestrated service, consistent with the standard WS-BPEL language.This study applies the Design Science Research Methodology, in which we evaluate CA-BPEL by using a tourism demonstration along with the conduction of a usability survey that shows the convenience of the proposed approach.We also compare our proposed approach with 14 related studies.Our investigations suggest that CA-BPEL has much potential to facilitate the development and execution of context-aware service compositions.
What Is Unseen On-Screen: A Descriptive Study on Students’ Speaking Anxiety During Online Learning

The idea of a good speaker should endeavor fluent yet accurate speaking might arise students’ anxiety during the speaking performance, especially for EFL students. It would drive them either to prepare and perform their best or make them even worse. This state is known as Foreign Learning Speaking Anxiety (FLSA) and could hit anyone when speaking or communicating in the target language with several factors. During the existence of online learning, anxiety in speaking class might be one that is unseen on-screen. This study is aimed at describing the speaking anxiety faced by undergraduate students in speaking English during online learning and the strategies used to cope with speaking anxiety before and during the speaking performance. The participants in this study were 27 students of IKIP Budi Utomo Malang, consisting of 17 students of the Public Speaking class and 10 students of the Advanced Speaking class. To obtain the data, the questionnaire was conducted to know students’ kinds of anxiety and their strategy to deal with it before and during the speaking performance. The results showed that the students feel anxious because of four factors, namely fear of negative evaluation, test anxiety, and anxiety in online speaking classes. The anxiety could be seen from the unclear sentences produced, sweating, butterflies in the stomach, dry mouth, loss of words, and squeaky voice. However, students take notes, do self and peer-practice before the performance; and focus on the speaking materials and breathing management during speaking performance to release the anxiety.