Reviewerly/exHarmony huggingface dataset:
Determinants of ICO Investment Decision: An Exploratory Factor Analysis

An Initial Coin Offering (ICO) is a funding mechanism used by projects to sell their crypto-token in exchange for Bitcoin, Ether, etc. It is the counterpart of the Initial Public Offering in the new digital currency economy. ICOs help projects to be funded in a short time with a high-profit margin.The primary purpose of this study is isolating the principal factors that affect the decision to invest in an ICO. A large array of potential signals, consisting of promotion and marketing effects, investor motivation, founder team effects, whitepaper and ICO specific factors, are considered in an online survey and the responses are analyzed using Exploratory Factor Analysis.The results of the analysis reveal five composite principal factors that determine the decision to invest in an ICO. These five principal factors are the ICO whitepaper, websites for finance news and ICO listings, ICO project sector and founder, ICO project team and social media. Our findings provide a perspective to both investor and ICO founders about which factors to prioritize when deciding to launch an ICO or to invest in one.
PENGARUH MOTIVASI DAN PERSEPSI TERHADAP MINAT MAHASISWA MENGIKUTI PENDIDIKAN PROFESI AKUNTANSI (PPAK) DI KOTA SEMARANG

This study investigates the effect of motivation (quality, career, economic and social) and perception (the cost of education and study duration) on students' interest in taking PPAk. The variables used are quality motivation, career motivation, economic motivation, social motivation, perception of education costs and perception of study duration as independent variables, and student interest in accepting PPAk as the dependent variable. The study used a questionnaire with a total sample of 73 people. The analysis tool uses multiple linear regression. The results showed that quality motivation, career motivation, economic motivation, social motivation, perception of educational costs and perception of study duration simultaneously had a significant effect on the interest of accounting students following the PPAk. The value of the regression model obtained by 0.356, which means that 35.6% of students' interest in participating in PPAk can be explained by the 6 variables. However, based on a partial analysis, only career motivation and social motivation have a significant positive effect on the interest of accounting students of PPA studies in the city of Semarang. While other variables such as quality motivation, economic motivation, perceptions of educational costs and perception of study duration did not affect the interest of accounting students in taking PPAk in the city of Semarang.
Integrated Polarity Optimization of MPRM Circuits Based on Improved Multi‐objective Particle Swarm Optimization

Aiming at the multi-objective polarity design of Mixed-polarity Reed-Muller (MPRM) circuit, such as small area and low power consumption, an integrated polarity optimization scheme based on improved Multi-objective particle swarm optimization (MOPSO) is proposed. In the Improved MOPSO (IMOPSO) algorithm, particles in the external archive can be actively evolved through self-learning operations to find better circuit polarity. The particles in the population achieve selflearning fractals by comparing the differences between their own states and individuals in external archive to enhance the evolutionary level of the population. A multiobjective decision model of area and power consumption is established according to the characteristics of MPRM circuit. The tabular technique and the IMPOPO algorithm are combined to obtain the Pareto optimal polarity set of the MPRM circuit for area and power consumption. The MCNC Benchmark circuit is used to test the performance of the algorithm. The results verify the effectiveness of the proposed algorithm.
OpenStreetMap mapathons support critical data and visual literacy instruction

OpenStreetMap (OSM) mapathons can offer a learner-centered means for critical data literacy and visual literacy instruction. Mapathons have been used as coordinated humanitarian mapping events in which participants use geographic information system (GIS) data and satellite imagery to create open-source maps for humanitarian support. Visual mapping is an effective learning activity because it encourages students to use big data to create a deliverable—an open-source map—that allows instructors to engage learners in data literacy and visual literacy at the highest cognitive level.Virtual Projects are published on an annual basis in the Journal of the Medical Library Association (JMLA) following an annual call for virtual projects in MLAConnect and announcements to encourage submissions from all types of libraries. An advisory committee of recognized technology experts selects project entries based on their currency, innovation, and contribution to health sciences librarianship.
Investigating engineer’s actual usage intention to apply construction technologies in emerging countries

All zones worldwide are working through a once again industrial revolution which is called “Industry 4.0”. It is proposed to have a huge impact on engineering, commercial and standards of livelihood. With the rise and spread of technologies, companies are investing in Construction technologies (c-techs). Still, the success of c-techs relies on individual’ acceptance and adoption of technologies. As per the research of Fortune 500 information construction sector, says that the major hurdles industries are facing are the adoption and rejection of the new technologies. This paper aims to measure the behaviour and actual usuage of individual engineers in acceptance and rejection of certain c-tech, within the framework of Technology acceptance model (TAM) constructs and the extended TAM determinant facilitating condition. The results suggested that strategists and designers must consider that construction technology factors are critical for the success of the business.
Direct and efficient verification of entanglement between two multimode–multiphoton systems

Linear-optic photonic networks with multiple single-photon inputs are attracting attention due to their great potential for quantum computation, quantum communication, and quantum sensing. They are also essential for verifying quantum advantages via boson sampling schemes. The quantum coherence of generated multimode–multiphoton states is crucial for various applications. However, the coherence is invisible in the normally obtained photon number distributions, which strictly limits the efficiency of entanglement verification between multimode–multiphoton systems since the required resources for quantum state tomography increase exponentially as the number of photons/modes increases. In this paper, we report the experimental demonstration of direct and efficient verification of entanglement between two multimode–multiphoton systems (one photon in three modes and two photons in three modes) using just two sets of classical correlation tables with and without a discrete Fourier transformation of the optical modes, clearly demonstrating a dramatic reduction in the resources required for entanglement verification. Our experimental demonstration paves the way to understanding the coherence and entanglement of multi-partite systems, not only for photons but for other quantum resources with high-dimensional degrees of freedom.
A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator

Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.
LANGKAH-LANGKAH PENGEMBANGAN MEDIA PEMBELAJARAN MENUJU PENINGKATAN KUALITAS PEMBELAJARAN

This paper explained about the media development toward the improvement of learning quality. This is also contains conceptual information traced through books that are relevant to the literature study approach. The finding of this paper is there were a connection between learning media development to learning quality improvement. This paper is only traced the concept that can achieve the learning quality improvement. Based on that reason, this paper can still be continued by other researcher deeply in practical term.
Generalized nets and intuitionistic fuzzy pairs as tools for modelling of flexible manufacturing systems

The concepts of Generalized Nets (GNs) and Intuitionistic Fuzzy Pairs (IFPs) are described and some of their basic properties are given.GNs are one of the extensions of Petri nets, which include as particular cases all other Petri net modifications.IFPs are the evaluations of elements of a given universe about some intuitionistic fuzzy set.Three GN models using IFPs of a concrete flexible manufacturing system are used for an example of the possibility of the GNs, working with IFPs as evaluations, to describe the functioning and the results of the work of adaptive systems.
Design Fiction as a Practice for Researching the Social

The aim of this paper is to contribute to a new conceptual foundation for design fiction. Much attention is dedicated to theorising how design fictions relate to our so-called actual world. This work can be seen as an attempt at securing the seriousness and legitimacy of design fiction as an approach to design research. The theory of possible worlds has proven promising in this regard. We argue, however, that a detailed understanding of design fiction is still lacking. In design fiction literature, authors often engage in critiquing techno-centric approaches while paying less attention to how design fiction has a potential to foster social change in situated actual affairs. We argue that analysis should start from the messy unfolding of the design event itself rather than from big ontological discussions of the boundaries between fiction and reality. To grasp the messiness of design fiction, we offer an interdisciplinary framework, bridging knowledge domains such as literally theory and design anthropology.
Baguena: A Practical Proof of Stake Protocol with a Robust Delegation Mechanism

Chinese Journal of ElectronicsVolume 29, Issue 5 p. 887-898 ArticleFree Access Baguena: A Practical Proof of Stake Protocol with a Robust Delegation Mechanism Liucheng Shi, School of Mathematical Sciences, Peking University, Beijing, 100871 ChinaSearch for more papers by this authorZhaozhong Guo, Corresponding Author gzzpku@163.com School of Mathematical Sciences, Peking University, Beijing, 100871 ChinaSearch for more papers by this author Liucheng Shi, School of Mathematical Sciences, Peking University, Beijing, 100871 ChinaSearch for more papers by this authorZhaozhong Guo, Corresponding Author gzzpku@163.com School of Mathematical Sciences, Peking University, Beijing, 100871 ChinaSearch for more papers by this author First published: 01 September 2020 https://doi.org/10.1049/cje.2020.08.004AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Proof of stake (PoS), aiming at replacing Proof of work (PoW) in blockchain consensus, has drawn great attention from academia and industry. We present "Baguena", a novel PoS protocol for public blockchain with high practicality and security. It uses a special designed algorithm with properties of uniqueness and anonymity for leader selection, and uses the longest chain rule for chain selection. Besides, entropy is introduced to prevent manipulation of leader selection process by simulating a random beacon based on Publicly verifiable secret sharing (PVSS) and threshold signature with only a linear number of exponentiations. We analyze the protocol's security by a threat model and design a robust delegation mechanism based on triple Elliptic curve digital signature algorithm (ECDSA) proxy signature. We implement Baguena and evaluate its performance on 100 Amazon EC2 virtual machines simulating 50k users, which shows that Baguena confirms transactions in 2 minutes, achieves 2.16x of Algorand's throughput and 6.95x of Ouroboros' throughput. Volume29, Issue5September 2020Pages 887-898 RelatedInformation
Unsupervised Deep Imputed Hashing for Partial Cross-modal Retrieval

Cross-modal retrieval, given the data of one specific modality as a query, aims to search the relevant data in other modalities. Recently, cross-modal hashing has attracted much attention due to its high efficiency and low storage cost. Its main idea is to approximate the cross-modality similarity via binary codes. This kind of method works well when the cross-modal data is completely observed. However, the real-world application usually avoids this situation, where part of the information is unobserved in some modality. Such partial multimodal data will result in the lack of pairwise information and then destroy the performance of cross-modal hashing. In this paper, we proposed a novel unsupervised cross-modal hashing approach, named as Unsupervised Deep Imputed Hashing (UDIH). It is a two-stage learning strategy. Firstly, the unobserved pairwise data is imputed by the proposed generators. Then a neural network with weighted triplet loss is applied on the correlation graph to learn the hashing code in the Hamming space for each modality, where the correlation graph is constructed with the aid of augmented data. UDIH has the ability to preserve the semantic consistency and difference among data objects. The extensive experimental results have shown that the proposed method outperforms the state-of-the-art methods on two benchmark datasets (MIRFlickr and NUS-WIDE). The source code could be available at https://github.com/AkChen/UDIH.
INDONESIAN SENIOR HIGH SCHOOL STUDENTS’ ATTITUDE TOWARDS LITERATURE IN ENGLISH

This study aimed to investigate senior high school students’ attitude towards advantages of learning literature in English. The participants of this research were 22 eleventh grade students in SMAN 2 Padang who took English as Specialization class. This study was descriptive qualitative research used in investigating senior high school students’ attitude towards literature in English. The instrumentations of this research were questionnaire and interview. The following are advantages of learning literature in English, encouraging language sources, improving language skill, expanding students’ awareness through environment and willingness to learn English language. The findings of the research showed that students’ attitude towards advantages of learning literature in English is positive.
Acceptance Model of Hospital Information Management System: Case of Study in Indonesia

In this digital era, Hospitals must follow the competition that demands professionally managed services which cannot be separated from the role of computer technology. Therefore, a hospital needs a management information system that can integrate all its activities in order to improve performance and services, which is better known as Hospital Information System Management (HIS). The purpose of this study is to analyze the actual application of hospital management information systems using the Technology Acceptance Model (TAM) method with behavior intention as the intervening variable.&#x0D; This research method is done using the quantitative approach, type of causality in the form of survey, one short study time horizon in the form of a questionnaire using Path Analysis. The analysis units are 100 respondents of employees at Metropolitan Medical Centre Hospital in Jakarta, who used HIS 2020. Purposive Sampling was used as the sampling technique. The results showed that attitude towards using HIS had a positive effect on behavioral intention to use. Perceived Usefulness has a positive effect on behavioral intention to use. Perceived Ease of use has a positive effect on behavioral intention to use. Behavioral intention to use has a positive effect on actual technology to use. Attitude toward using has a positive effect on actual technology to use. Perceived Ease of use has a positive effect on actual technology to use. The research findings indicate that the behavioral intention to use variable is a variable that increases the use of HIS because it can benefit from the use by the user. The implication in realizing the increase in HIS requires policies and commitment of hospital management in mandatory, advocacy, socialization, training, monitoring and evaluation related to HIS in each unit.
Teaching Vocabulary Through Picture Cards in Islamic Elementary School A Case Study in Nida Suksa School, Thailand

Abstract. Nowdays, english as a subject learned in primary school level. Its because the important of english towards the student. This study was conducted aimed to know the influence of Picture Cards in teaching vocabulary to the third grade of Nida Suksa School, Thailand. The subjects of the research were students in the third grade. The researcher only took nine students in different abilities from a high level (3/1), middle level (3/2), and low level (3/3). The instrument used by the researcher was an interview. The result showed that the students still had difficulty in understanding the texts because of having a lack of vocabulary, long texts, and limited time to analyze the text in answering the question. The students studied vocabulary through Picture Cards, and they learned vocabulary tests from text book. Furthermore, the finding of this study was: Picture Cards is able to increase and enrich their vocabulary and their knowledge. The implication of Pictures Cards can motivate students to understand the text. Therefore, they can understand many texts in vocabulary tests.Keywords: Teaching Vocabulary, Picture Cards
Multi-camera Torso Pose Estimation using Graph Neural Networks

Estimating the location and orientation of humans is an essential skill for service and assistive robots. To achieve a reliable estimation in a wide area such as an apartment, multiple RGBD cameras are frequently used. Firstly, these setups are relatively expensive. Secondly, they seldom perform an effective data fusion using the multiple camera sources at an early stage of the processing pipeline. Occlusions and partial views make this second point very relevant in these scenarios. The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125mm for the location and 10° for the orientation using low-resolution RGB images. The experiments, conducted in an apartment with three cameras, benchmarked two different graph neural network implementations and a third architecture based on fully connected layers. The software used has been released as open-source in a public repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
On the Comparative Study of Prediction Accuracy for Credit Card Fraud Detection with Imbalanced Classifications

Credit card fraud is one of the critical issues due to its significant losses to both financial institutions and individuals in the society. The accurate detection and prevention of fraudulent activities are necessary to protect financial institutions and individuals. This paper performs a comparative experimental study to detect credit card frauds, as well as to tackle the imbalance classification problem by applying different machine learning algorithms for handling imbalanced datasets. Our study shows that there is no need to process imbalance dataset by applying resampling techniques to measure the performance of our classifiers and it is sufficient to measure the performance through the three-performance measurements (Accuracy, Sensitivity, and Area Under Precision/Recall Curve (PRC)) to prove the accuracy of the prediction of classification. Finally, by handling imbalanced classifications with imbalance datasets, one can minimize the number of false alarms, reduce damages to financial institutions and individuals, increase accurate for fraud detection, and decrease the occurrence of fraud cases using machine learning techniques.
Learn from your environment: A visual literacy learning model

Based on the presupposition that visual literacy skills are not usually learned unaided by osmosis, but require targeted learning support, this article explores how everyday encounters with visuals can be leveraged as contingent learning opportunities. The author proposes that a learner’s environment can become a visual learning space if appropriate learning support is provided. This learning support may be delivered via the anytime and anywhere capabilities of mobile learning (m-learning), which facilitates peer learning in informal settings. The study propositioned a rhizomatic m-learning model of visual skills that describes how the visuals one encounters in their physical everyday environment can be leveraged as visual literacy learning opportunities. The model was arrived at by following an approach based on heuristic inquiry and user-centred design, including testing prototypes with representative learners. The model describes one means visual literacy could be achieved by novice learners from contingent learning encounters in informal learning environments, through collaboration and by providing context-aware learning support. Such a model shifts the onus of visual literacy learning away from academic programmes and, in this way, opens an alternative pathway for the learning of visual skills.&#x0D; &#x0D; Implications for practice or policy:&#x0D; &#x0D; This research proposes a means for learners to leverage visuals they encounter in their physical everyday environment as visual literacy learning opportunities.&#x0D; M-learning software developers may find the pedagogical model useful in informing their own software.&#x0D; Educators teaching visual skills may find application of the learning model’s pedagogical assumptions in isolation in their own formal learning settings.&#x0D;
Predicting the Resource Needs and Outcomes of Computationally Intensive Biological Simulations

Accurately simulating viral dynamics within a human body is often computationally intensive, thus requiring dedicated computing infrastructures. This limits the use of simulations in personalized medicine since patients may not all have access to such infrastructures or the possibility of waiting weeks for predictions. Lowering the computational burden by simplifying the models is a challenge since simulation results must remain accurate in order to safely support the decision-making activities of patients. In this work, we use machine learning to predict the results of a simulation (i.e. surrogate model) and its computational resources (i.e. performance model). This allows patients to use machine learning as a computationally light proxy to simulations and to know how long an accurate simulation would take. We demonstrate that machine learning can build such surrogate and performance models with low errors for five previously developed models of HIV, while outperforming interpolation methods commonly used for surrogate modeling.