Reviewerly/exHarmony huggingface dataset:
Validation of a CNN classifier for RADAR in real and simulation domain, exclusively trained in simulated RSI model

The current developments in driver assistance systems enabled OEMs to oﬀer vehicles, ready for highly automatic driving (Level 3). A key factor for this development is the environmental perception, based on sensor-systems. Currently, LIght Detection And Ranging (LIDAR), Cameras and RAnge Detection And Ranging (RADAR) are the mainly used sensor-systems, to generate a perception of the vehicular environment. Due to diﬀerent used spectrums, the performance and reliability of a sensor depends on environmental conditions, like darkness, rain or fog. RADAR is known as a robust sensor, working well also in rain or light conditions. A key feature for an object, surrounding the own car, is a classiﬁcation, independently of a certain sensor-system. It enables the adjustment of the own trajectory to speciﬁc situations, deﬁned by the surrounded objects. Nevertheless, it is challenging to classify an object with RADAR. Common sophisticated automotive classiﬁcation algorithms rely on camera. But a precise classiﬁcation of objects, detected by RADAR, gained on importance due to the reliable characteristics of RADAR mentioned before.In addition to real world domain, simulation software provides tools for RADAR measurements and deployment. Even physical sensor models, near to reality, are implemented in software, enabling the improvement of signal processing techniques in a reproducible, convenient and fast manner. RADAR-speciﬁc eﬀects, like multipath or clutter, are also implemented. The eﬀects cause so-called Ghost-Targets, pretending fake objects.This thesis will make a contribution to the question how the simulation of RADAR can be used to classify objects in real world. The classiﬁcation task is taken over by a CNN classiﬁer. Due to the fact that it is a supervised learning method, the classiﬁer gets trained. Current published papers used real but quit static data for the training. This thesis uses data out of IPG Carmaker 8 simulation model.The proposed methods cluster the data with a state of the art cluster algorithm for RADAR and label, based on ground truth, with a novel approach. A multi-class CNN, implemented in Tensorflow, is enabled to diﬀer between a car, bicycle or pedestrian. The evaluation is concentrated on a confusion matrix. Beside simulation data, also real world data is used to evaluate the classiﬁer. Therefore, a scenario in real world is created. The measurement is performed with INRAS RadarLog, specialized for research and raw data analyze. Both domains, simulation and real world, are scaled on a common frame size and amplitude. The comparison should indicate the potential of a classiﬁer to classify objects with a certain accuracy. The appropriate classiﬁer is exclusively trained in simulation domain and gets applied in the real world and simulation domain. Weather phenomenons like rain, fog or extreme temperatures are excluded in the simulation.
Review on the Implementation of Mental Health Education Curriculum in Primary and Secondary Schools

Primary and secondary school; Mental health education; Curriculum implementation
IoT-based Activity Recognition with Machine Learning from Smartwatch

Human activity recognition(HAR) with wearable Internet of Things (IoT) sensors can be beneficial for the elderly and patients monitoring. Smartwatches are the most accessible IoT devices that play an important role in human activity monitoring. The structure of an activity recognition system involves a platform that holds wearable sensors. Under the background, many platforms such as distributed sensors and smartphones and the combination of them have been investigated but platforms are still one of the main research challenges. Smartwatches can be more comfortable for the elderly and patients, therefore our research is focused on a smartwatch as an emerging IoT platform and machine learning method. The smartwatch attached to arm as the main position then was compared to other positions. We considered machine learning methods to present the smartwatch as a reliable platform in order to recognize activities, also we considered k-nearest neighbor and decision tree as two popular machine learning methods for activity recognition. We evaluated the performance with the confusion matrix and then we used accuracy and f1-score metrics for the result of our experiment. The metrics show accuracy and f1-score almost 99% as the performance of smartwatch on arm position.
Skeleton-Based Action Recognition with Joint Coordinates as Feature Using Neural Oblivious Decision Ensembles

Recognition of human behavior is critical in video monitoring, human-computer interaction, video comprehension, and virtual reality. The key problem with behaviour recognition in video surveillance is the high degree of variation between and within subjects. Numerous studies have suggested background-insensitive skeleton-based as the proven detection technique. The present state-of-the-art approaches to skeleton-based action recognition rely primarily on Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN). Both methods take dynamic human skeleton as the input to the network. We chose to handle skeleton data differently, relying solely on its skeleton joint coordinates as the input. The skeleton joints’ positions are defined in (x, y) coordinates. In this paper, we investigated the incorporation of the Neural Oblivious Decision Ensemble (NODE) into our proposed action classifier network. The skeleton is extracted using a pose estimation technique based on the Residual Network (ResNet). It extracts the 2D skeleton of 18 joints for each detected body. The joint coordinates of the skeleton are stored in a table in the form of rows and columns. Each row represents the position of the joints. The structured data are fed into NODE for label prediction. With the proposed network, we obtain 97.5% accuracy on RealWorld (HAR) dataset. Experimental results show that the proposed network outperforms one the state-of-the-art approaches by 1.3%. In conclusion, NODE is a promising deep learning technique for structured data analysis as compared to its machine learning counterparts such as the GBDT packages; Catboost, and XGBoost.
Amazon says Prime Air will begin delivering packages via drones 'within months'

At the re:MARS Conference in Las Vegas on Wednesday, Amazon announced that Prime Air will be delivering packages to its customers with drones “within months.”
An Introduction to the Special Issue on Numerical Techniques Meet with OR - Part II

Abstract The special issue: “Numerical Techniques Meet with OR” of the Foundations of Computing and Decision Sciences consists of two parts which are of the main theme of numerical techniques and their applications in multi-disciplinary areas. The first part of this special issue was already collected in the FCDS Vol. 46, issue 1. In this second part of our special issue editorial, a description of the special issue presents numerical methods which can be used as alternative techniques for Scientific Computing and led Operational Research applications in many fields for further investigation.
透過社群媒體評論及選擇性使用者偏好建立 Embedding-based 音樂推薦系統

With online music streaming and subscription becoming the mainstream way of listening to music, people have more songs to choose from. Therefore, the importance of the music recommendation system is more important than before. It can not only reduce the time for users to find songs that they are interested in but explore the potential interests of users. In our research, we build a personalization embedding-based recommendation system for J-pop music, and we will focus on how to capture the user preference more precisely. We use the side information of the song to represent the reason that people may be interested in the song. We consider the relation between these user preferences by domain knowledge. Moreover, we collect reviews on social media as one of the user preferences. We found that people tend to write reviews on social media platforms to describe the feelings of the song, and it brings information that is different from the traditional user preference. When recommending, we consider the long-term and short-term user preferences. The long-term user preference indicates the user's music habit, and the event or the purpose of users listening to music will impact the short-term user preference. Besides, we assume that people listen to different types of music for different reasons and preferences. It means that the user preference will vary with the song, and we called it selective user preference. We design an embedding-based recommendation system by deep learning model. The model includes a knowledge graph and the attention mechanism. Then, we hold an ablation experiment to evaluate the performance of the concept we implement. After successful on the ablation experiment, we discuss the result and provide some findings to prove the concept is reasonable.
Framework design for the application of narrative (Datadriven), a technique for the presentation of data focused for academics and scientists

This article presents the elements of communication that make up a framework for effective communication, analysis and understanding of scientific data through the narrative technique. This framework has the intention of digitization in the future, however, the design and structure created consists in the collection of distinctive techniques, paradigms, cognitive elements and digital devices to develop it.
Rekayasa Perangkat Lunak Aplikasi E-Report Layanan Pengaduan Masyarakat

Information becomes very important in the process of developing a region or region without information it will be difficult for a region to develop, many aspects that can help the development process of a region, for example the construction of public facilities, building a good, comfortable and safe social environment etc. Without access to adequate information, aspects of the development of an area will be hampered and left behind, another negative impact is material losses and even worse casualties, because there is no information that develops from the area a-report in public services that was created as an information tool in responding to events quickly and effectively within the community. Which was conceptualized with a smart city design, to increase city efficiency in providing accurate and fast information services to the community. This application was designed with the water method plunge, where this method applies a sequential and systematic approach to the software development process, while for testing in this application, we use the black box method. Which later, the results of our design will produce applications that can increase the efficiency of the community in providing information that is integrated directly with government.
Double Projection of Motion Graphic Using Glass Reflection: A Case Study of Massacre

With the continuous development of technology and artistic creation, the creation methods and styles of hand-drawn animation have become increasingly diversified. Simultaneously, many creators are focusing on the absorption and reference of different art forms and expression methods. As one of the forms of art design, the application of hand-drawn animation to holographic display technology introduces a completely different aesthetic feeling. Through holographic display technology, visual design and visual technology in hand-drawn animation art are integrated, the expressiveness and richness are significantly enhanced, and the artistic personality and charm are better demonstrated. The main research objective of this paper is the innovative application of holographic display technology in hand-drawn animation. With the help of holographic display technology, the pre-prepared hand-drawn animation is projected on the glass and overlapped with the hand-drawn animation on another screen through a glass to create the effect of overlapping images. In addition, this paper explores the application status and technical implementation of holographic display technology in the field of animation. Furthermore, it analyzes the integration between holographic display technology and animation art in terms of interactive display modes.
Building a Paralympic, frame-based dictionary : towards an inclusive design for Dicionário Paraolímpico (Unisinos/Brazil)

This paper presents some theoretical and methodological issues emanating from the building of Dicionario Paraolimpico (Paralympic Dictionary), an online lexicographical resource that will describe the lexicon of Paralympic sports in Portuguese and English, structured according to the notion of semantic frame. It follows the lead of previous works published by the SemanTec research group (Unisinos/Brazil), such as Dicionario Olimpico (Olympic Dictionary, 2016). For the current project, some features from the previous works were kept, such as the basic microstructure of scenarios and the megastructure. There are, however, significant changes to be introduced in the Paralympic Dictionary. Some of them are the result of the Olympic Dictionary’s revision, and address issues such as content multiplicity of the sport and scenario definitions, and the absence of relevant information in the microstructure of lexical units. In addition, some of the changes concern the features that distinguish the Paralympic Dictionary from the Olympic Dictionary, since Paralympic sports have specific frames. Another important issue to be addressed refers to the accessibility of the dictionary itself by people with disabilities. After discussing these issues, the paper concludes by outlining future plans, including further developments for the Paralympic Dictionary and its broader implications in the context of the SemanTec research group.
Privacy-preserving techniques in big data

Big data is a collection of a massive volume of data from various sources like social networks, the Internet of Things (IoT), and business applications. As multiple parties are involved in these systems, there is an increased chance of privacy breaches in the big data domain. Preservation of privacy plays a crucial role in preventing sensitive information from being visible to others. This chapter gives insights on overview of big data, associated privacy challenges in different phases of the big data life cycle, and focus on various privacy-preserving techniques in big data. It also briefs about the privacy-preserving solutions in resource-constrained devices.
Principles of Neural Computation from the Evolution of Neural-like Systems

Biologically realistic, neural-like systems were evolved in computer simulations to solve a variety of life-like dynamical problems. Problem-independent properties of the networks were found which suprisingly also prevail in real nervous systems. These include inequalities between inhibitory and excitatory parameters, and the tendency of neurons to cluster into feed-forward inhibitory and other biologically common circuits. A simple scheme for connecting the neurons, based on idealized axons and dendrites was found to have advantages over full connectivity, because of its relative sparcity in connections and parameters and superior scaling behavior with network size.
Heraclitus and Sonic Thinking

“The material Flux”; “the hidden mobility beneath”; “Sonic Logos.” Any classicist familiar with the fragments of Heraclitus would be surprised to find these concepts developed in today’s theories about sonic art from authors from different traditions such as Salomé Voegelin, Julian Henriques, Jean-Luc Nancy, and Christoph Cox. The present paper intends to open a dialogue between these authors and Heraclitus, claiming that there is an underlying connection beyond mere coincidence. Sonic thinking proposes listening as the way to access or produce a particular knowledge—one that would otherwise be too difficult or impossible to grasp. This knowledge is produced by practices such as listening and musical meditation instead of intellectual activity alone. To make the case, the authors will present a general outline of what sonic thinking entails to compare it with the relevant points in Heraclitus’s philosophy. The purpose of this paper is twofold: to provide a new sonic framework to read Heraclitus and to provide an old framework to read sonic thinking.
Comparative Evaluation of Academic Performance in Waikato Environment for Knowledge Analysis Using Multiple Classification Algorithms

Data mining (DM) is the process of applying algorithms on large databases with the aim of discovering knowledge that would help in taking informed decisions by the management of academic institutions (Chalurapruek, S, et al, 2018).This paper seeks to discover the best classifiers to be used on educational data when using Waikato Environment for Knowledge Analysis (WEKA).The variables of importance namely carry-over, marital status, age range, entry mode and accommodation location were selected by J45 classifier.Four sampled datasets from four schools/faculties namely School of Physical Sciences (SPS), School of Environmental Studies (SES), School of Technology and Science Education (STSE), School of Agriculture and Agricultural Technology (SAAT) belonging to Modibbo Adama University of Technology (MAUTECH), Yola, Nigeria, were used for the DM task.All the classifiers available in WEKA suite were applied independently on the four different datasets.Notably, classifiers such as J48, NaiveBayes, Logistics and Regression gave better performances when compared with the rest.In the comparative analysis, the Regression model had the overall best performance of 98.366 %, 99.3197 %, 96.3964 % and 96.875 % on the four datasets respectively.The computed average performance of each of the four classifiers on the four datasets gave97.5446%, 94.6954 %, 95.670125 %, and 97.739275 % respectively.
Online Doctors Consultation And Medical Dispenser In Health Care Using Machine Learning

To lead a good life health care is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The proposed idea is to create a medical chat bot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor.Toreducethehealthcarecostsandimproveaccessibilitytomedicalknowledgethemedicalchat botis built. Certain chat bots acts as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a chat bot only when it can diagnose all kind of disease and provide necessary information. A text-to-text diagnosis bot engages patients in conversation about their medical issues and provides a personalized diagnosis based on their symptoms. Hence, people will have an idea about their health and have the right protection.
The Spectra of Filters

This chapter now turns to the discussion of filters, which extend the notion of spectrum beyond signals into the processes themselves. A gentle introduction to the concept of delaying signals, aided by yet another variant of the Fourier transform, the discrete-time Fourier transform, allows the operation of filters to be dissected. Another analysis tool, in the form of the z-transform, is brought to the fore as a complex-valued version of the discrete-time Fourier transform. A study of the characteristics of filters, introducing the notion of zeros and poles, as well as finite impulse response (FIR) and infinite impulse response (IIR) forms, composes the main body of the text. This is complemented by a discussion of filter design and applications, including ideas related to time-varying filters. The chapter conclusion expands once more the definition of spectrum.
The Developer Journey

All great journeys start with a map, a formal way of understanding how to arrive at a destination or achieve a goal. As such, the Developer Journey Map is an invaluable tool in your developer relations program. As developers are increasingly influencing or making decisions about which technologies to adopt, it’s important that you formally map out the journey they have to take from initially discovering your company and product all the way through to them scaling with it. Then you need to deliver it in the most frictionless way possible.
RANCANGAN E-COMMERCE DAN PAYMENT API MIDTRANS UNTUK PRODUK UMKM (STUDI KASUS PADA RUMAH KEMASAN)

In the process of product marketing micro, small and medium enterprises on Shop Rumah Kemasan Pandeglang, still being done by marketing the product through the storefront and the goods are stored in the warehouse so that the search, counting of goods directly gets to the warehouse and recording is still using bookkeeping so that the service process for goods is slow, it often happens that goods are not found resulting in data loss, it is necessary to have a system that manages the transaction process for data collection and online sales in order to increase the number of sales and minimize data loss. The system that will be designed later is the e-commerce and payment API midtrans, this research was conducted to increase sales of micro, small and medium business products by providing a system that can be accessed by everyone anywhere and anytime so that in the system that will be built later, buyers no longer need to wait long to buy products and for payments there is no need to make cash payments just online, and for product sellers can speed up the sales process, organized data management. In the design and development stage of e-commerce applications, researchers use the Extreme Programming method, modeling using the UML which produces a software that is expected, and the language used in this development is the PHP programming language, the MySQL database and for payments based on the Midtrans payment gateway application programming interface.
LibGuides: Course Clearances For Instructors: Procedures for Copyright Clearances of Readings and Resources

Navigate copyright requirements related to the use of resources in online and campus classrooms
Policy Brief No. 03 of 2020-2021 on Women's Access to Agricultural Finance in Kenya

This Policy brief provides a deep understanding of challenges faced by women in accessing agricultural finance in Kenya.
Professional Employee Query Platform

Usually Programmers and Professors solve their technical problems and resolve programming issues by posting them in internet such as “stack overflow”, “Quora” and etc. But a professional software engineer or developer who works for or under a private project can’t discuss their programming issues and main core issues which are related to their current working project which are highly confidential. Every good reputed company may have their own private network to communicate the technical problems or programming issues. What about average, below average scale industries, they can’t afford money to maintain virtual private network, here our platform comes into the picture and provides the good environment, platform for professionals to communicate and discuss their technical problems within their industry people with good security.It features questions and answers on local company projects in computer programming. The main goal of this project is to provide a more open alternative to earlier question and answer in platform such as technical issues and programming issues. The serves as a platform for employees to ask and answer questions, and, through membership and active participation, to vote questions and answers up or down and edit questions and answers in a fashion similar to a wiki or Reddit. Users of this project can earn reputation points and rewards.