Reviewerly/exHarmony huggingface dataset:
Trajectory Generation of a Quadrotor Transporting a Bulky Payload in the Cluttered Environments

When an aerial robot carries a bulky and heavy payload, it suffers from a wavering effect because of the large inertia of the heavy mass of a payload, which can cause a serious crash or fall to the ground.We propose the path-planning algorithm for an aerial robot to pass a narrow space while transporting a bulky payload to resolve the issue.Unlike the existing conventional path-planning method that treats the payload as a point-mass, this paper generates the trajectory considering the shape of a payload and passage size of an Unmanned Aerial Vehicle (UAV).First, we design a map-generation algorithm with the Voronoi diagram by exploiting the fact that the UAV has an ellipsoidal shape in transport operation with a bulky object.The modified map data can be exploited for the global path generation such as A* path-planner.For the rotational motion of the UAV, the heading angle was determined by considering the lengths of the major and minor axes of the ellipse.The global path was optimized based on the elastic band optimization algorithm to solve the jerky motion on the trajectory.Through the simulations and experiments, we validate the efficiency of our algorithm in the environment with the narrow space.Experimental results showed that the proposed path-planning algorithm could be used for safe aerial transportation in realistic environments.
ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

This research was conducted to find information about the tendency of Indonesian people regarding the Covid-19 vaccination. The method that the author uses is by collecting data from Twitter social media using the API key provided by Twitter. The process of collecting data using a Python application with several libraries such as tweepy, pandas, numpy and nltk. After the data is crawled, then the data is cleaned with several data cleaning processes such as remove username, remove url, lower case, remove stopwords and lemmatize. Then the results are labeled with the textblob and sklearn libraries. then the data is analyzed using the Support Vector Machine (SVM) algorithm with the best comparison being 20 testing data and 80 training data or as many as 942 testing data and 3766 training data, the prediction results for testing data are f1 score 0.93, accuracy score 0.88, precision score 0.88 and a recall score of 0.99. The results showed that from 4,078 tweet data, there were 2,525 positive sentiments (43.0%), 771 negative sentiments (16.4%), and 1,912 neutral sentiments (40.6%). The results of 80% (3766) of training data and 20% (942) of test data obtained an accuracy score of 73.6%. From this study, it can be concluded that the tendency of Indonesian people when sampling data is taken is more accepting (positive responses) to government policies regarding the Covid-19 vaccination program. In the future, it is hoped that there will be a library that supports text data processing such as regional languages, because researchers found that during the data cleaning process there was a lot of word elimination, because many regional languages ​​were used by the Indonesian people in writing on social media.
An efficient line-based approach for resolving merge conflicts in XMI-based models

Abstract Conflicts in software artefacts can appear during collaborative development through version control systems. When these conflicts happen in XMI models, the conflict sections generated by diff programs break the XMI serialisation and compromise the ability to use model editors that assume well-formedness of this serialisation. While these conflict sections already mark the conflicting lines of the model, current tools for conflict resolution in models ignore them and instead load the different versions of a model from the repository, over which they perform a full and costly comparison that re-identifies the conflicts. We present a novel approach that prevents this repetition of work by directly parsing XMI-based models with conflict sections, which allows for a targeted analysis of only the lines of the model that have been detected to be in conflict by the version control system. We have implemented this approach in the Peacemaker tool, which can load XMI models with conflict sections, compute and display conflicts at the model level, and provide appropriate resolution actions. Compared with state-of-the-art model comparison tools with support for conflict resolution, Peacemaker is able to identify the vast majority of conflicts in models while reducing the required time by up to 60%. The small subset of non-identified conflicts does not introduce issues into the models, e.g. there is no loss of model information, and the resulting models after line-merging these conflicts conform to their metamodels.
Mixed-integer quadratic programming reformulations of multi-task learning models

&lt;abstract&gt;&lt;p&gt;In this manuscript, we consider well-known multi-task learning (MTL) models from the literature for linear regression problems, such as clustered MTL or weakly constrained MTL. We propose novel reformulations of the training problem for these models, based on mixed-integer quadratic programming (MIQP) techniques. We show that our approach allows to drive the optimization process up to certified global optimality, exploiting popular off-the-shelf software solvers. By computational experiments on both synthetic and real-world datasets, we show that this strategy generally leads to improvements in terms of the predictive performance of the models, if compared to the classical local optimization techniques, based on alternating minimization strategies, that are usually employed. We also suggest a number of possible extensions of our model that should further improve the quality of the obtained regressors, introducing, for example, sparsity and features selection elements.&lt;/p&gt;&lt;/abstract&gt;
Developing Android-Based Mobile through Local Ecosystem Materials to Improve Thinking Skills of High School Students

This was second year's research in developing a biology learning module of ecosystem material that promotes mobile android-based local region potentials, after first year conducted a field examination on android e-module and limited testing in East Lombok Regency, West Nusa Tenggara and Sleman Regency, D.I. Yogyakarta.The second-year research aimed to investigate the results of biology learning on local ecosystem material towards the development of scientific process skill and divergent thinking ability in Senior High School of Yogyakarta City and Sleman Regency.This module development followed a Design and Development Research approach.Data collecting technique used a posttest of scientific process and divergent thinking skills.Analysis technique were quantitative descriptives analysis.The result showed that the comparison of second year android module dissemination result towards the development of scientific process and divergent thinking skills of Senior High School students of Yogyakarta City and Sleman Regency showed a higher results in problem solving design than project based learning design, however, in z value of problem solving design obtained a value of -1,11, while in project based learning design obtained z value of 12,6.It meant there were other factors that influenced the android module application.
Reliable Algorithms for Machine Learning Models: Implementation Research in Data Science

Machine Learning generates programs that make predictions and informed decisions about complex problems in an efficient and reliable way. These ML programs autonomously test solutions against the dataset to find the best fit for the problem. The paper aims to review the ML algorithms that develop prediction models by utilizing training dataset and known output. The paper also focuses on ML principles, algorithms, approaches, and applications for Supervised, Unsupervised, and Reinforcement learning that can perform tasks without being explicitly programmed for it. Completely opposite to rule-based programming, the machine learning paradigm uses examples of real data sets and pre-process it before providing the desired outputs based on these examples. In the case of more involved and complex tasks, it can be challenging for humans to explicitly program the models. On the other hand, it can be more effective to help the machines develop the algorithms for advanced tasks. This paper will also present the trending real-world applications of Machine Learning in Image Recognition and Biomedicine. Additionally, it will provide a background analysis of machine learning and related fields of data science.
A Football Shot Action Recognition Method Based on Deep Learning Algorithm

Football is regarded as the world’s number one sport and is loved by all countries, and large-scale football matches are held basically every year. The key to football matches is to shoot goals, and how to improve the accuracy of football shooting requires the identification and analysis of football shooting actions. Deep learning enables machines to imitate human activities such as seeing, hearing, and thinking. It solves many complex pattern recognition problems. Especially, the deep learning algorithm is unique in the recognition of pictures with high accuracy, and it provides technical support for the recognition and analysis of football shooting actions. What this paper will discuss is the recognition method of football shooting action based on a deep learning algorithm. Experiments show that the football shooting action recognition method developed in this paper has a great effect on promoting the accuracy of football shooting, which can make the accuracy rate reach about 96%. The research in this paper has great reference value and practical significance for the team’s ability to shoot and grasp the opportunity to score.
Online AI-Guided Video Extraction for Distance Education with Applications

The pervasive applications of artificial intelligence (AI) have made an impact on the human knowledge sharing and education. In practice, a lifelong learning process is becoming an inevitable trend in the future. Although distance education has been developed for a long period, there are still problems such as low learning enthusiasm, low autonomy, and unsatisfactory learning support services. The application of AI to distance education will fundamentally affect the instruction and guidance methods, as well as the instructing content of distance education. Evolutions in distance education have brought significant changes in the instructing mode, learning strategy and support service, evaluation mode, and instructing platform of distance education. Distance education can construct an intelligent education platform to provide learners with personalized learning support. It can also offer flexible, dynamic, and convenient evaluation and on-demand learning resources with guaranteed quality. By leveraging the state-of-the-art data mining technique, we in this work design a set of simple yet effective distance education system, in the context of video conferencing. Our technique depends on the existing video surveillance network and equipment. Our technique can be applied to achieve an effective face-to-face online interaction. Meanwhile, the video surveillance network switch is optimized to enhance the network transmission and ensure the reliability of network operation. Comprehensive experimental results on the two instructing platforms based on embedded system learning and hospital nurse learning have shown the advantage of our method.
Blockchain based Decentralized Trust Management in IoT: Systems, Requirements and Challenges

Internet of Things (IoT) vision has astoundingly transcended the environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real world IoT scenarios relaying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM) being a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Centralized TM systems for IoT have a potential risk of failure and attacks and decentralized TM systems are not completely decentralized, requiring the assistance of central servers for the completion of final calculation and maintenance of trust credits. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in Trust management field for different classes of IoT applications. It can provide tamper proof data by enabling a more reliable trust information and integrity verification, ultimately enhancing its availability and privacy during storage and sharing. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain based Decentralized Trust Management Systems (BCDTMS) for IoT. In this paper, our contributions are twofold; first we provide a comprehensive comparative analysis of state-of-the-art BCDTMS devised for different IoT classes including IoMT, IoV, IIoT and SIoT. To make it an extensive study, we perform a detailed Assessment of existing BCDTMS in the literature (for last three years 2018-2020) on the basis of Blockchain and trust based aspects. Secondly, we present requirements and challenges in the context of using blockchain for TM in IoT.
Grey Wolf Optimization–Based Deep Echo State Network for Time Series Prediction

The Echo State Network (ESN) is a unique type of recurrent neural network. It is built atop a reservoir, which is a sparse, random, and enormous hidden infrastructure. ESN has been successful in dealing with a variety of non-linear issues, including prediction and classification. ESN is utilized in a variety of architectures, including the recently proposed Multi-Layer (ML) architecture. Furthermore, Deep Echo State Network (DeepESN) models, which are multi-layer ESN models, have recently been proved to be successful at predicting high-dimensional complicated non-linear processes. The proper configuration of DeepESN architectures and training parameters is a time-consuming and difficult undertaking. To achieve the lowest learning error, a variety of parameters (hidden neurons, input scaling, the number of layers, and spectral radius) are carefully adjusted. However, the optimum training results may not be guaranteed by this haphazardly created work. The grey wolf optimization (GWO) algorithm is introduced in this study to address these concerns. The DeepESN based on GWO (GWODESN) is utilized in trials to forecast time series, and therefore the results are compared with the regular ESN, LSTM, and ELM models. The findings indicate that the planned model performs the best in terms of prediction.
How to prove any NP statement jointly? Efficient Distributed-prover Zero-Knowledge Protocols

Abstract Traditional zero-knowledge protocols have been studied and optimized for the setting where a single prover holds the complete witness and tries to convince a verifier about a predicate on the witness, without revealing any additional information to the verifier. In this work, we study the notion of distributed-prover zero knowledge (DPZK) for arbitrary predicates where the witness is shared among multiple mutually distrusting provers and they want to convince a verifier that their shares together satisfy the predicate. We make the following contributions to the notion of distributed proof generation: (i) we propose a new MPC-style security definition to capture the adversarial settings possible for different collusion models between the provers and the verifier, (ii) we discuss new efficiency parameters for distributed proof generation such as the number of rounds of interaction and the amount of communication among the provers, and (iii) we propose a compiler that realizes distributed proof generation from the zero-knowledge protocols in the Interactive Oracle Proofs (IOP) paradigm. Our compiler can be used to obtain DPZK from arbitrary IOP protocols, but the concrete efficiency overheads are substantial in general. To this end, we contribute (iv) a new zero-knowledge IOP Graphene which can be compiled into an efficient DPZK protocol. The (D + 1)-DPZK protocol D-Graphene, with D provers and one verifier, admits O ( N 1 /c ) proof size with a communication complexity of O (D 2 ·( N 1−2 /c + N s )), where N is the number of gates in the arithmetic circuit representing the predicate and N s is the number of wires that depends on inputs from two or more parties. Significantly, only the distributed proof generation in D-Graphene requires interaction among the provers. D-Graphene compares favourably with the DPZK protocols obtained from the state-of-art zero-knowledge protocols, even those not modelled as IOPs.
Culture of the School Literacy Movement (GLS) for Students in Elementary Schools to Realize the 2045 Golden Generation

This study aims to analyze the culture of the School Literacy Movement (GLS) in elementary school students to realize the 2045 golden generation. This study uses a descriptive method with a qualitative approach. The subjects of this study consisted of principals, teachers, school committees, and students. Data collection techniques using interviews, observation, and document review. Data analysis techniques were carried out by collecting data, reducing data, presenting, and drawing conclusions. The results of the study indicate that the educational environment at the Lempuyangwangi State Elementary School views literacy culture as an activity that is more than just reading books, but also includes a wider scope of literacy such as mathematical literacy and technological literacy. The GLS cultural program consists of writing guidance, activity competitions to visit the library, literacy celebrations, reading halls, reading in the library, and literacy corners. The success of GLS is supported by good cooperation. Cooperation within internal schools, schools with committees and schools with parents of students. Communication forums are established through regular meetings and two-way parent associations. Availability of literate human resources and physical infrastructure that supports GLS culture. Monitoring and evaluation are regularly carried out to see the progress of the GLS culture.
Peningkatan Hasil Belajar Matematika Menggunakan Strategi Teka Teki Silang di SMP

Background of The research by learning outcomes student which still many below Complete Criterion Minimize (CCM). Other than that study process only predominated by group owning high ability. Purpose of this research are to know do there are influence active learning strategy of crossword puzzle type to learning outcomes student of VIII.2 class SMP Negeri Bernas. Design of this research is classroom action research with research design repeated actions/cycles, each cycle consists of four steps (planning, implementation, observation and evaluation, reflection). The population in research was all student of VIII.2 class SMP Negeri Bernas. The data analysis technique used in this study is mastery learning after obtaining learning outcomes data through academic tests, then the data is data that describes student learning mastery. Result of this result was learning outcomes student after using active learning strategy of crossword puzzle type better of mean learning outcomes of student before using active learning strategy of crossword puzzle type.
FORMING THE STRUCTURE OF A MODERN CAMPUS

The program for the strategic development of the education system provides for the creation of comfortable conditions for students, researchers and faculty. In accordance with this, there is a need to form approaches to building a new innovative educational environment or campuses, including not only residential areas and public spaces, but also areas for conducting scientific activities: educational spaces and laboratories. This article analyzes the areas characteristic of a modern campus, proposes planning solutions and structure.
Amplitude SAR Imagery Splicing Localization

Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the same problem has never been investigated on SAR images. Forensics methods developed for natural photographs are not guaranteed to succeed on SAR images, as their generation pipeline is completely different from that of digital pictures. In this paper, we investigate the problem of localizing splicing attacks in amplitude SAR imagery. Our goal is to identify the pixels of an amplitude SAR image that have been copied and pasted from another image for malicious purposes, considering also that the attacker might have applied some editing to conceal this manipulation. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.
Temporal Action Detection Methods Based on Deep Learning

Temporal action detection is one of the most important and challenging tasks in video analysis. Due to its wide application prospects, it has received extensive attention in recent years. With the development of deep learning, great progress has been made in temporal behavior detection, but there are still many difficulties to be solved, such as accurate proposal generation and high computational cost. In this paper, deep learning-based temporal action detection methods are classified according to full supervision and weak supervision, and then the representative models of the two methods are summarized in detail, and the ideas, advantages and disadvantages of different models and the evolution between different models are analyzed. At the same time, the performance of different models on mainstream datasets is compared. The mainstream dataset and evaluation index used in temporal action detection are introduced in detail, and the calculation method of evaluation index is also elaborated. Finally, through in-depth analysis, the possible future research directions of temporal action detection and the whole review are summarized.