Reviewerly/exHarmony huggingface dataset:
gravityZero

This paper reports the exposition of an artistic installation, gravityZERO, and its ongoing technical development. It consists of virtual sound, VR and robotic technologies in order to simulate the state of zero gravity. Audience members can experience a floating sensation within this virtual environment. gravityZERO (zero gravity) is an installation that combines video, sound, and robotics. Translucent cubes are assembled at the venue. Images are quasi-holographically projected on the cube's surfaces, and speakers are placed in the cube's corners. A person is suspended from the ceiling and floats as if there is no gravity. Each rope can be freely moved in 3D space within the cube through motor operation. This project is based upon an idea that utilizes an interface closely related to a human body, and also pursues new possibilities of the performance of Augmented Reality and Projection Mapping.
Analysis Of The Effectiveness Of The Retention Pool As Flood Control

Abstract Flooding is a condition where there is no water in the drainage canal (times) or impeded flow of water in the drainage canal. Flooding is part of the management of more specific water resources to control flood discharge generally through flood control dams, or improvement of carrier systems (rivers, drainage) and prevention of potentially damaging things by managing land use and flood areas. Retention pool is one of the ways that is used to overcome flooding, but with the existence of retention ponds it does not guarantee that the area is protected from flooding, so it needs to be seen the effectiveness of the retention pool to control flooding. The purpose of this study is to obtain alternative flood control handling from the retention pond and obtain the value of the effectiveness of the reservoir for flood control, and the accuracy in the placement of the location of the retention pool. The steps that will be taken to carry out this research are: 1) Looking for supporting theories about retention pools, 2) Determine the parameters of the value of effectiveness of the retention pool that has been made boundary, 3) namely (the volume of storage and determining the location of the placement of retention pools), 4) Look for available data that has been available in previous planning, 5) Analyze the value of the effectiveness of the retention pool which is then written in the form of a thesis.
Improving the performance of Transformer Context Encoders for NER

Large Transformer based models have provided state-of-the-art results on a variety of Natural Language Processing (NLP) tasks. While these Transformer models perform exceptionally well on a wide range of NLP tasks, their usage in Sequence Labeling has been mostly muted. Although pretrained Transformer models such as BERT and XLNet have been successfully employed as input representation, the use of the Transformer model as a context encoder for sequence labeling is still minimal, and most recent works still use recurrent architecture as the context encoder. In this paper, we compare the performance of the Transformer and Recurrent architecture as context encoders on the Named Entity Recognition (NER) task. We vary the character-level representation module from the previously proposed NER models in literature and show how the modification can improve the NER model’s performance. We also explore data augmentation as a method for enhancing their performance. Experimental results on three NER datasets show that our proposed techniques established a new state-of-the-art using the Transformer Encoder over the previously proposed models in the literature using only non-contextualized embeddings.
The Effect of Learning Facilities, Family Environment, and Learning Motivation on Students' Learning Outcomes of Sman 1 Tanjungpandan

This study aims to determine whether there is an influence of Learning Facilities, Family Environment, and Learning Motivation. Against Student Learning Outcomes of SMA Negeri 1 Tanjungpandan. The method used in this research is a quantitative approach. The population in this study were all students as many as 130 respondents with sampling using random sampling technique. Collecting data using an online questionnaire distributed using Google Forms. The data analysis technique used SPSS (Statistical Product and Service Solutions) software application. The results of the study indicate that there is a significant influence between the independent variables, learning facilities, family environment, and learning motivation on the dependent variable, student learning outcomes.
Intelligent Engineering Construction Management

Urban infrastructure has a large regional span and long cycle, so it has the significant characteristics of large amounts of engineering data, strong surrounding environment uncertainty, and high engineering risk. This chapter explains how to fuse the heterogeneous information of BIM and GIS models to realize smooth roaming through the lightweight and hierarchical processing of the model. Aiming at multi-dimensional and high-frequency time series data, this chapter introduces the MFAD-URP abnormal event diagnosis model based on the meta-feature extraction and self-encoding recursive map technology, which is used for early warning of emergency events, and introduces the method of comprehensive engineering emergency management information system and disposal process design after emergencies. The chapter takes the remote intelligent management of the shield tunnel engineering as an example and describes how to build a platform-level multi-engineering information management system to provide effective remote guidance on issues such as progress and safety.
The Network

June 02 2021 The Network Tinatswe Mhaka Tinatswe Mhaka tina@leonardo.info. Search for other works by this author on: This Site Google Scholar Author and Article Information Tinatswe Mhaka The Network Coordinator tina@leonardo.info. Online Issn: 1530-9282 Print Issn: 0024-094X ©2021 ISAST2021ISAST Leonardo (2021) 54 (3): 370–371. https://doi.org/10.1162/leon_e_02048 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Tinatswe Mhaka; The Network. Leonardo 2021; 54 (3): 370–371. doi: https://doi.org/10.1162/leon_e_02048 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. ©2021 ISAST2021ISAST Article PDF first page preview Close Modal You do not currently have access to this content.
Using a Predictive Rating System for Computer Programmers to Optimise Recruitment

Using a quantitative assessment system, the number of resumes reviewed to identify a suitable developer was reduced to 3.5% with a successful recruitment decision made in 10 working days of posting the job advertisement. This paper summarises the methodology for developing that rating system. The depth and quality of an available talent pool is a function of demand, which is demonstrated by comparing globally-scaled individual performance metrics. Public code repositories are accessed and the code quality assessed algorithmically. The performance score combines accuracy, timeliness and difficulty from a series of challenges. These three attributes form a meaningful predictive measure of performance by using a non-linear optimisation routine. Bootstrapping is used to validate the approach. This process randomly omitted a scored performance observation per coder in order to calculate the performance score from the retained scores. There was a strong relationship (r = 0.70) between the predicted 1-omitted-performance score with the actual omitted score highlighting the predictive power.
Scale-invariant Convolutional Capsule Network

Scale-invariant feature detection plays an important role in computer vision. Inspired by capsule networks and Hough transform, we presented a scale-invariant feature detection and extraction module that is streamlined, lightweight, and interpretable. We reduced the overhead by combining the learnable feature detector with an efficient scale aware parameter voting mechanism. Also, a routing mechanism can be added to further refine the extracted features to boost performance. Compared with popular multicolumn or feature pyramid based methods, our proposed method is more lightweight both parameter wise and architectural wise, while maintaining good multiscale performance, especially in intensively scale transformed scenarios. Meanwhile, its streamlined sequential pipeline makes it easy to integrate into other models in a plug-and-play manner.
Visualization as a Knowledge Transfer

This chapter inspects visualization tools and applications enabling visualization of data, information, and knowledge. The background is provided first about visualizing information in a pictorial yet abstract rather than illustrative way. Further text discusses visual metaphors as a basic structure in metaphorical language of visualization. Selected methods and tools are introduced, and the ways visualization transfers knowledge and mediates between the user and the physical world supporting cognitive ways of learning and teaching are shown. Nature-derived metaphors serve as bio-inspired, interdisciplinary models. The importance of visual and technological literacy is discussed, along with a need of teaching visualization methods as an important part of the current educational strategies. Concluding remarks examine how metaphorical visualization may support learning and teaching, and why visual and technological literacy should be taught and trained since early childhood.
Embedded Unbiasedness: Effect on Optimal FIR Filtering Estimates

In this paper, we give an analysis of the embedded unbiasedness (EU) on optimal finite impulse response (OFIR) estimates. By minimizing the mean square error (MSE) constrained by the unbiasedness condition, a new OFIR-EU filter is derived. We show that the OFIR-EU filter does not require the initial conditions, and occupies an intermediate place between the UFIR and OFIR filters. It is also shown that the MSEs of the OFIR-EU and OFIR filters diminish with the estimation horizon. A numerical example has demonstrated that the OFIR-UE filter has better robustness against temporary model uncertainties than the OFIR and Kalman filters.
Steganography Techniques Based on Modulus Function and PVD Against PDH Analysis and FOBP

This chapter proposes two improved steganography techniques by addressing two problems in the existing literature. The first proposed technique is modulus function-based steganography and it addresses pixel difference histogram (PDH) analysis. The modulus function is used to calculate an evaluation function and based on the value of the evaluation function embedding decision is taken. There are two variants of this technique: (1) modulus 9 steganography and (2) modulus 16 steganography. In modulus 9 steganography, the embedding capacity in a pair of pixels is 3 bits, and in modulus 16 steganography the embedding capacity in a pair of pixels is 4 bits. Both the variants possess higher PSNR values. The experimental results prove that the PDH analysis cannot detect this technique. The second proposed technique is based on pixel value differencing with modified least significant bit (MLSB) substitution and it addresses fall off boundary problem (FOBP). This technique operates on 2×2 pixel blocks. In one of the pixels of a block data hiding is performed using MLSB substitution. Based on the new value of this pixel, three difference values with three neighboring pixels are calculated. Using these difference values, PVD approach is applied. Experimental results prove that the PDH analysis and RS analysis is unable to detect this proposed technique. The recorded values of bit rate and peak signal-to-noise ratio are also satisfactory.
Visual Computing

Visual computing regards the computer as a visual processing machine. It takes images and all forms of data and inputs them to the computer. Visual computing uses predefined algorithms to produce results in a visual form such as images, tables, or animations. The internal algorithms perform the functions that the user requires on the data. The results that are output are intended to be assimilated by a human. The input and processing phases may be subject to intervention by the user, if exploration of the input data, or steering the processing, is required. This human–computer interaction can take various forms from simple menu-driven controls to advanced virtual reality systems.
Ecological education in the regional library: Searching for efficient ways and solutions

Based on the experience of V. G. Belinsky Library Medicine and Ecology Department, the key components of the ecological education in libraries are identified.
Construction of College English "2+2" Blended Learning Mode Based on MOOC in Post Pandemic Era

Blended Learning has got a fast development based on a variety of mature platforms such as MOOC, Coursera. Influenced by the outbreak of COVID-19 in the whole world, education has come into the stage of post pandemic era. More and more scholars and educators begin to find a more flexible and effective way to conduct Blended Learning based on the increasingly developed ICT, aiming to achieve more effective learning outcomes. This essay will focus on the design of "2+2" Blended Learning mode for college English courses based on MOOC and explain its basic idea and way of operation.
Simple Online Unmanned Aerial Vehicle Tracking with Transformer

Multi-Object Tracking (MOT) mainly implements certain complex multi-step tracking by using detection algorithms, which respectively perform target detection, feature extraction and data association. The main challenges of Unmanned Aerial Vehicle (UAV) tracking are complex background and mutual occlusion. In this work, we propose a simple online UAV tracking with Transformer. It uses the encoder-decoder mechanism to introduce a set of object query in the pipeline, which can achieve the detection of new targets accurately. This method uses an online joint-detection-and-tracking pipeline based on the encoder-decoder mechanism. The complex and multi-step components in the previous method are simplified. Further, it is a new structure based on Transformer. Object query detects the object in the current frame. The object feature query in the previous frame associates these current objects with the previous object. We proposed a method that can be well applied to small targets such as UAVs, achieved 64.1% MOTA competition on the MOT15 challenge dataset.
R Scripts

In this chapter, we report the R scripts implemented to perform identifiabilityIdentifiability analysis and regression, mentioned in the previous chapters. The input files are only available upon request to the author.
Introduction

This chapter introduces to the solar energy in general and in perspective as far as the applications and utility of solar energy systems are concerned. It gives the brief overviews of all types of solar energy systems starting with their classifications followed by their modeling and optimization issues. Work sheds light on both solar thermal systems and solar photovoltaic (PV) systems. The performance measurement of these solar energy systems strongly depends on various system and operating parameters along with their ambient or climatic parameters, and it is a multi-criteria decision-making (MCDM) optimization problem. Based on the previous studies, the statistics as regards to the modeling and optimization of different solar energy systems and the basics of MCDM techniques with their classifications are discussed. The chapter ends with detailed summary with possible scope of future work on modelling and optimization of solar energy systems using various individuals plus hybrid MCDM techniques.
Making Citizens' Activities Flourish Through a Crowdsourcing-Based Social Infrastructure

We now typically live in modern cities, where ubiquitous computing technologies such as advanced sensing enhance various aspects of our everyday lives. For example, smart phones offer necessary information to make our everyday lives convenient anytime, anywhere in the city; energy management and traffic management have become smarter, making our everyday lives more convenient and efficient. However, from a citizen perspective, the well-being of citizens needs to be more essential than merely achieving efficient and convenient smart city infrastructures. We think that this issue is particularly crucial for establishing the next generation of smart city design. In this chapter, we propose a social infrastructure named flourished crowdsourcing to make our society flourish, so diverse citizens will live comfortably and happily. To achieve a flourishing society, one of the most essential issues is making diverse citizens activists who will participate in socially collective activities. Traditional approaches such as gamification typically make it possible to guide the social activities of the average number of citizens, but it is not easy to maintain activities for diverse citizens. By incorporating fictionality into the real space, our approach is to increase the social awareness of citizens to achieve a flourishing society within each citizen's community so that they see the necessity of their contribution. To design and analyze fictionality, we also propose a gameful digital rhetoric as design abstractions. The design abstractions are extremely different from traditional approaches; designers can explicitly focus on the enhancement of the meaning in the real space from multiple perspectives; thus designers can change the meaning incrementally according to rapidly changing social situations or citizens' diverse preferences.
Nonlinear parameter estimation by the point-mass method taking into account correlation of partial estimates

A new solution to the problem of nonlinear parameter estimation is considered. The peculiarity of this problem is that the arguments of a nonlinear function are not only the measurement data and the required parameters, but also supplementary parameters. Supplementary parameters are a priori unknown, but they are necessary only to find optimal estimates of the required parameters. The estimate of the desired parameters is formed according to the point-mass method as a weighted sum of partial estimates obtained for the specified values of supplementary parameters. This solution allows us to eliminate a priori probabilities for supplementary parameters by taking into account additional covariance of weight coefficients and (or) specified partial estimates. The considered approach is effective in solving specified nonlinear estimation problems characterized by low accuracy of available measurement data and (or) their few number.
IMPACTS OF CYBER CRIME ON INTERNET BANKING

Internet banking or e-banking refers to the facility through information and communication technology. Internet banking is increasingly becoming popular because of both convenience and flexibility. Computer fraudsters are always trying to gain illegal access to the information of financial and business sectors for fraudulent activities. The customers of Internet banking always fear for their financial data when dealing with Internet banking and its services. There is a need to create awareness among Internet banking customer on how to avoid the available threats. The research in this paper critically analyzes and discusses the effects of cyber threats when dealing with online banking services. It is concluded that by the research that there is a need to increase safety measures in available cyber crimes when dealing with Internet banking and sensitive financial data.
Enhanced Trust Path Between Two Entities in Cloud Computing Environment

Trust is the common factor of any network security. In cloud, trust is the major factor as this trust develops a relation between the user and resource of the service provider. To develop a strong trust there has to be a strong trust path between two entities. The model proposed builds a strong trust path between two important entities in cloud namely user and resources of the service provider. The trust path thus built strengthens the security of the resources as well as the authentication of the user. The implementation proved that trust model developed is more efficient in terms of computation time.
Integrative teaching materials for Indonesian Speakers of Other Languages based on Sundanese gastronomy text

This research aims to develop teaching material with integrative model of Indonesian for Speakers of Other Languages (ISOL) based on Sundanese gastronomy. ISOL teaching materials through an integrative model are compiled as an alternative teaching material addressed to international students who are learning Bahasa Indonesia. The tasks in the teaching materials are thoroughly compiled considering four aspects of language skills: leading, speaking, listening, and writing skills. The research began with the process of formulating the concept of Sundanese gastronomy from several references. Furthermore, the concept of Sundanese gastronomy was included as teaching materials in four language skills by referring to several aspects, namely, (1) content eligibility, (2) presentation, (3) readability, along with (4) ethnic, race, religion, and gender background. The research found that the experts moderately assessed all aspects of the teaching materials by giving 5 points on the Likert scale. The research findings consequence from the guideline was that whenever the point was less than 5, the teaching materials would continue to be improved until discovering the ideal form of ISOL teaching materials. As a recommendation, the development of Sundanese gastronomy-based ISOL teaching materials is significant to be exposed to international students, especially for students who are learning Bahasa Indonesia both as compulsory material in the Indonesian tourism department and as supplementary material in different majors.