Reviewerly/exHarmony huggingface dataset:
AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

Jana, Prithwish Jana, DebasishWith technological advancement, drone has emerged as unmanned aerial vehicle that can be controlled by humans to fly or reach a destination. This may be autonomous as well, where the drone itself is intelligent enough to find the shortest obstacle-free path to reach the destination from a designated source. Be it a planned smart city or even a wreckage site affected by natural calamity, we may imagine the buildings, any surface-erected structure or other blockage as obstacles for the drone to fly in a straight line-of-sight path. To address such path-planning of drones, the bird’s-eye view of the whole landscape is first transformed into a graph of grid cells, where some are occupied to indicate the obstacles and some are free to indicate the free path. We propose a method to find out the shortest obstacle-free path in the coordinate system guided by GPS. The autonomous drone (AutoDrone) will thus be able to move from one place to another along the shortest path, without colliding into hindrances, while traveling in a two-dimensional space. Heuristics to extend this to long journeys and 3D space are also elaborated. Our approach can be especially beneficial in rescue operations and fast delivery or pickup in an energy-efficient way, where our algorithm will help in finding out the shortest path and angle along which it should fly. Experiments are done on different scenarios of map layouts and obstacle positions to understand the shortest feasible route, computed by the autonomous drone.
Verbs and Proposition Phrases as a Model for Describing the Content of Metasubject Competencies in the Perspectives and Expectations of Labor Market Participants

The article presents results of studying how the labor market participants formulate the perspectives and expectations about the contents of metasubject competencies. The research was carried out in 2019-2020 by the Project Management Laboratory of the Moscow City University and was based on the methodology of the activity approach and qualitative research, using the methods of in-depth interviews and content analysis. Metasubject competencies were considered as a set of purposeful actions expressed in the speech of respondents by verbs (actions) and propositional phrases (goals/subjects). A six-step content analysis of data obtained during an in-depth interview shows that the expectations and perspectives of various categories of labor market participants about the contents of meta-subject competencies can be described by nine enlarged sets of goal-oriented actions denoted by verbs: 1) to adapt, 2) to interact, 3) to speak and write, 4) to achieve, 5) to compete, 6) to present, 7) to design, 8) to manage, 9) to learn. Within each set were defined verbal descriptions of actions, decomposed by adding a goal/object to each action. The results of the study allowed us to identify and present a verb-target model for describing the content of “metasubject competencies” at the conceptual level. At the practical level, the description of the content of metasubject competencies in the form of a verb (action) and propositional phrase (goal/subject of action) allowed us to show them as cross-cutting structural components of educational programs in the education system.
Commentary

This is a commentary piece on the previous chapters based on insights of researchers in Thailand, who have been contributing to worldwide collaborations to tackle traditional/cyberbullying. In Thailand, cyberbullying research and online safety workshops are already emerging, however laws, policies and programmes should be progressed more in the future. The applicability and suitability of some of the solutions in previous chapters in the Thai context are discussed.
APLIKASI ALGORITMA FLOYD-WARSHALL DENGAN PENDEKATAN MADM DALAM MENENTUKAN RUTE TERPENDEK PENGANGKUTAN SAMPAH

Currently, the problem of environmental hygiene caused by the accumulation of garbage becomes a serious problem for every community. In addressing this problem, an efficient waste transport process is required. This study aims to find the shortest route of garbage transportation in Gorontalo city by using Floyd Warshall Algorithm by finding the smallest weight between each point (Vertex). In this study, the weights used in the Floyd Warshall Iteration Algorithm were Alternative weights obtained by the Multi-Attribute Decision-Making approach (MADM). The criteria for determining weights in MADM use three indicators that affect the efficiency of garbage transportation, namely Distance, time, and congestion. The route used in this study is the dump truck route with 17 garbage transportation points. After obtaining the Alternate weight and iteration using Floyd Warshall algorithm obtained the shortest route with the smallest trajectory weight of 110.845.
Lidar-millimeter wave radar information fusion multi-target detection based on unscented Kalman filter and covariance intersection algorithm

Lidar-based object detection is an important method of environment perception for autonomous driving. Due to the limitation of the inherent properties of lidar, the detection accuracy of obscured vehicles and distant objects is inferior, which causes the problem of missed detection. To address this problem, a lidar-millimeter wave radar information fusion multi-target detection method based on the unscented Kalman filter (UKF) and the covariance intersection (CI) algorithm was proposed in this article. Firstly, the UKF algorithm was applied to generate state estimations on the data collected by the sensor. Subsequently, the CI algorithm was introduced to form state fusion estimates. Finally, a simulation experiment platform was built based on MATLAB, and a comparison experiment with Joint Probabilistic Data Association (JPDA) and Gaussian mixture probability hypothesis density (GMPHD) algorithms were designed. The Generalized optimal sub-pattern assignment (GOSPA) indi-cators were adopted to evaluate the detection accuracy of each algorithm, and the effectiveness of the method was verified. The experimental results showed that UKF-CI had higher detection accuracy and provided accurate infor-mation for the decision-making part of the autonomous driving system, which guaranteed the stable operation of the autonomous driving system.
QUE LUGAR É ESSE? OS DESAFIOS E AS POTENCIALIDADES DE INSERÇÃO DA EDUCAÇÃO FÍSICA NA RESIDÊNCIA MULTIPROFISSIONAL EM SAÚDE DA FAMÍLIA

Introduction: The text, built by a physical education teacher narrates her trajectory as a multiprofessional resident in family health, starting from a central question: What place is this? Method: This is an experience report covering the period from March 2019 to March 2021. Results: Through the initial questioning it was possible to characterize the performance as a physical education professional in this context of formation, to present the challenges encountered and potentialities in the primary health care scenario, and analyze the developments in the area of physical education, including in the context of the COVID-19 pandemic. Conclusion: The reflections proposed a humanized perspective of care, inviting us to rethink our practices and promote health care based on Unified Health System in Brazil (SUS) principles and guidelines.
Development Of Self Navigator For Open Water Swimmer

Nowadays, swimming is one of the sports activities that often adopted by individuals in their leisure time. This activity also is a popular exercise because it contributes all around body developer. Besides that, it also commonly used by people with physical disabilities in their therapy session. However, this activity has little risks whenever a swimmer does not have the proper equipment of swimming. One of the challenges is swimmer are difficult to keep swim on track in order to reach the destinations. This is because a slight imbalance body positioning while swimming can change the heading of the swimmer from destination point. Because of that, its cause them a little time to stop swim as theswimmer need to start lift their head in order to correct the heading direction again. This project was developed to assist a beginner swimmer to reach the destination. In thisproject, Global Positioning System (GPS) sensor, Ublox Neo 6-M had been used to deteimine the exact heading to the destination. Additionally, it also consists of magnetometer sensor (HMC5883L) which provide the current heading of the swimmer. This device will navigate and assist the swimmer by provides the correct heading angle to reach the destination. All of these systems are performed by Arduino Mega 2560microcontroller to calculate the distance between swimmer and destination point. It also detennines the correct heading angle to assist the swimmer reach the destination point. Finally, this project was developed to help a less-skilled swimmer in order to trained the right swimming technique.
From the Curse of Cash to the Burden of Digitization

Are cryptocurrencies still an answer in search of a problem? For reasons discussed at length in Rogoff (2016), decentralized “permission-less” blockchain tokens are extremely unlikely to replace centralized government-issued “fiat” money. As the long history of currency illustrates, while it is usually the private sector that is originally responsible for the most important innovations, governments eventually regulate and appropriate. However fervently cryptocurrency evangelists believe that superior private technology must eventually win out in the long run, they do not seem to understand that, when it comes to the core of the monetary system, governments cannot afford to lose, and they will not. Functions such as the unit of account, the medium for final clearing of transactions among financial institutions, and especially the payment of taxes and transfers are simply too vital for modern-day countries to leave in the hands of a decentralized private sector system. History shows that governments always have the capacity to keep changing the rules of the game until fiat money wins out…
Digital Literacy of Accounting Teachers in The Era of Industry 4.0

This study aims to address the digital literacy competence of accounting teachers as the subjects in this study. The approach used is qualitative with the type of case study research. Data were collected from interviews and documentation using sources triangulation and techniques triangulation to test the validity of the data. The results showed that the digital literacy competence of accounting teachers in public schools was quite good compared to accounting teachers in private schools. This is supported by adequate IT facilities and regular training. Inhibiting factors experienced by accounting teachers in public schools can be minimized compared to accounting teachers in private schools.
Prediction Utilizing Photonic Reservoir Computing Based on Complex Chaotic Mask

We investigate the photonic reservoir based on semiconductor-lasers with optical injection and feedback. Enhanced performance of the time-series prediction task can be obtained by using the wideband complex chaotic mask compared to the normal one.
Case-based Reasoning for Natural Language Queries over Knowledge Bases.

It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions — a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.
John Cage, Henry David Thoreau, Wild Nature, Humility, and Music

John Cage and Henry David Thoreau draw attention to the indeterminacy of wild nature and imply humans cannot entirely control the natural world. This paper argues Cage and Thoreau each encourages his audience to recognize their own human limitations in relation to wildness, and thus each helps his audience to develop greater humility before nature. By reflecting on how Thoreau’s theory relates to Cage’s music, we can recognize how Cage’s music contributes to audiences’ environmental moral education. We can appreciate the role of music in helping audiences to develop values conducive to environmentally sustainable practices.
FPGA Implementation For Image Enhancement using Zynq-7020

Digital image upgrade strategies can be used to expand the photographic nature of images. The main target of any upgrade technique is to acquire more reasonable result. Image upgrade procedures can be arranged into two techniques: spatial domain and frequency domain techniques. This paper presents and focused on image upgrade in the spatial domain, with specific reference to point prepare strategies include: contrast manipulation, brightness manipulation, inverting images, threshold operation. This undertaking presents an idea of equipment programming for hardware and software co-simulation to image handling and utilizing Xilinx Vivado which is implemented on Zynq -7020 .This strategy, gives a bunch of Simulink blocks (models) for a few equipment activities that could be carried out on different Xilinx FPGAs.
Unbalanced Data Oversampling Method for Traffic Multi-classification in Convergence Network

At present, classification models are widely used in network traffic anomaly detection. Researchers have found that the imbalance of network traffic data sets affects the result of anomaly detection in convergence network. For imbalanced data, researchers have proposed many solutions for dichotomous datasets, and there are often multi-class datasets in network traffic datasets, that is, there are often many different types of anomalous data in network traffic data, and the frequency of occurrence between different anomalous data varies greatly. To address the problems of complex data information and different important features of each class that often exist in such multi-class unbalanced network traffic data, this paper proposes a new oversampling technique, which firstly performs sample selection based on the information entropy of minority class samples, followed by the decomposition of eigenvalues of the data using principal component analysis, making the features uncorrelated with each other, and then synthesizes the data. Finally, we validate the effectiveness of our method on handling multi-class network traffic datasets.
On the Provable Generalization of Recurrent Neural Networks

Recurrent Neural Network (RNN) is a fundamental structure in deep learning. Recently, some works study the training process of over-parameterized neural networks, and show that over-parameterized networks can learn functions in some notable concept classes with a provable generalization error bound. In this paper, we analyze the training and generalization for RNNs with random initialization, and provide the following improvements over recent works: 1) For a RNN with input sequence $x=(X_1,X_2,...,X_L)$, previous works study to learn functions that are summation of $f(\beta^T_lX_l)$ and require normalized conditions that $||X_l||\leq\epsilon$ with some very small $\epsilon$ depending on the complexity of $f$. In this paper, using detailed analysis about the neural tangent kernel matrix, we prove a generalization error bound to learn such functions without normalized conditions and show that some notable concept classes are learnable with the numbers of iterations and samples scaling almost-polynomially in the input length $L$. 2) Moreover, we prove a novel result to learn N-variables functions of input sequence with the form $f(\beta^T[X_{l_1},...,X_{l_N}])$, which do not belong to the ``additive'' concept class, i,e., the summation of function $f(X_l)$. And we show that when either $N$ or $l_0=\max(l_1,..,l_N)-\min(l_1,..,l_N)$ is small, $f(\beta^T[X_{l_1},...,X_{l_N}])$ will be learnable with the number iterations and samples scaling almost-polynomially in the input length $L$.
Implementation Of A Star Algorithm In Android Based Alien Shooter Games: Implementation Of A Star Algorithm In Android Based Alien Shooter Games

The development of games at this time is so rapid and a part of a lifestyle. The Alien War game is a First Person Shooter (FPS) game using the first person's point of view to aim or kill an enemy in the form of an alien. The player only sees his hand and does not see the body of the character being played. This game uses the A Star method which is applied to Non-Player Character (NPC) to find the location of players. This alien war game uses the Android operating system.
Democratic Government, Corruption Control, and Economic Development: Evidence from Nigeria with Cointegration Approach

Nigeria returned to a democratic government in 1999. Evidence in the literature shows that democracy promotes economic development. In the recent past, the democratic trajectory of Nigeria has led to empirical debates about the democratization process of the developing states in particular, which is prone to multifaceted challenges. This study examines the impact of democratic government on Nigerian economic development using the VAR autoregressive approach. Our empirical work suggests that democracy has a long-run relationship with the economic development of Nigeria. Against our a priori expectations and established positions in some segments of the literature, this study identifies a negative impact of democracy on the economic development of Nigeria within our study period. On the other hand, the quality of the rule of law positively impacts economic development in Nigeria. Furthermore, the study finds a positive impact of corruption control in Nigeria with economic development, although observable evidence shows that the Nigerian government’s anti-corruption institution is weak. This research paper recommends institutional reforms for the democratization process of Nigeria to fully harness one of the most significant dividends of democracy-economic development.
Improved Face Tracking Algorithm Based on Block PCA and SVM

The recognition of portraits in surveillance video is different from static portrait recognition. Generally speaking, facial features in surveillance video cannot represent the original information. Therefore, face recognition is facing a great challenge, and there are certain requirements for the environment where information is collected. In order to continuously obtain the face in the video and find an effective face image, an improved face tracking algorithm based on the MEANSHIFT and block PCA+SVM is proposed. the Local Binary Pattern texture feature combined with color histogram is employed to improve the MeanShift algorithm. The experiments show that the accuracy rate of recognition has been greatly improved, from about 70% to about 90% compared with the traditional method. In addition, the improved method is more convenient than traditional method.
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks

For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and time-consuming process. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naively learning from related data fails to improve performance as the related data can be inconsistent with the target task. We describe a meta-learning based method that selectively learns from the related dialog task data. Our approach leads to significant accuracy improvements in an example dialog task.
The High Quality Global Data Management Framework for Climate – Improving the Quality of Data Management for Better Climate Monitoring

Presentation at the pre-ESIP FAIR dataset quality information (DQI) workshop held virtually on Monday July 13, 2020, prior to the virtual ESIP 2020 summer meeting (July 14 – 24, 2020). This one-day workshop with two live sessions is co-organized by the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) and the Evaluation and Quality Control (EQC) Team of Barcelona Supercomputing Center (BSC) and sponsored by ESIP.
Implementation of Gamification With Kahoot Platform! in Improving Student Learning Achievement UPBJJ UT Semarang

The purpose of this research is to find out whether the learning achievements of UPBJJ UT Semarang PGSD students can be improved through the use of gamification with the Kahoot platform! integrated with Ms. Teams in Tuweb. In this study, researchers implemented Gamification with the Kahoot platform! which is used as an effort to improve learning achievement in the English learning process for elementary teachers. The subject in this study is a student of S1 PGSD Semester 1 (one) UPBJJ UT Semarang Registration Period 2021.1 Pokjar Kaliwungu Kendal number of 24 students. This study is a 2-cycle Classroom Action Research with 1 pre-test and 2 post-tests through the stages of planning, action, observation, and reflection. From the results of the study showed an increase in the learning achievement of S1 PGSD UPBJJ UT Semarang students through the use of gamification with the Kahoot platform! integrated with Ms. Teams in Tuweb. This is characterized by an average increase in the score from pre-test to post-test 1 by 27.81 points (from 47.40 to 75.21). Furthermore, there was also an average increase in the score from post-test 1 to post-test 2 by 11.35 points (from 75.21 to 86.56). Based on the findings and conclusions can be proposed that gamification with kahoot platform! Integrated with Ms. Teams in Tuweb can be used as an alternative in the implementation of the teaching and learning process in the situation of the Covid-19 pandemic that requires learning to be done online or remotely.
Two Serial Multi-Layer Perceptrons for Signal Detection and Modulation Code Decoding for Bit-Patterned Media Recording

Since conventional data storage systems are faced with difficult problems such as superparamagnetic limit and thermal effect, bit-patterned media recording (BPMR) which resolves these problems receives attention as a promising next generation magnetic data storage system. The advantages of BPMR are reduced track edge noise and nonlinear bit shift and easily simplified tracking. However, the reduced spacing between islands to increase areal density causes severe intersymbol interference (ISI) and intertrack interference (ITI) which degrade BPMR system performance. To prevent error patterns that bring more ITI, modulation coding schemes was proposed [1] . Also, to improve system performance, neural networks have been applied to detection and decoding in data storage system [2] . In this paper, we propose two serial multi-layer perceptrons (MLPs) which are used for signal detection and modulation decoding, respectively, for BPMR.