Reviewerly/exHarmony huggingface dataset:
Success of Initial Coin Offering. The Empirical Evidence from 2016-2019

Since 2013 Initial Coin Offerings (ICO) have allowed companies to attract financing with the help of cryptocurrencies. In August 2017, the total capitalization of the crypto currency market exceeded $150bln. From the 1st quarter of 2015 to November 2017, the volume of funds raised amounted to $3,78bln. Statistics of ICO shows that demand continues to grow with claims of over $15bln raised in the first half of 2018. The first quarter of 2019 ended with $902 million total raised funds and $8.4 million average raised funds. The increasing number of projects financed by ICO (4954 projects during 5 years) as an alternative method to venture capital (VC) or initial coin offering (IPO) is caused by several reasons: the possibility of reselling the received tokens at a higher price after the launch of the project or earlier; the possibility of obtaining the company's services at lower prices; the increased popularity of crypto currencies (e.g. Bitcoin, Ethereum, etc.); the expectation of high profitability; the high speed of transactions (including the speed of attracting initial investments) and anonymity. However, due to difficulties in regulation, the ICO market attracts smaller investments on average (average investments in ICO projects equal to $19 million in comparison with the average size of IPO - $207,2 million) and there exists more chance for manipulation in the market, which leads to the fact that the results relevant to the IPO market cannot be applied directly to ICO. Due to the fact that blockchain technology and cryptocurrencies are new tools in finance, and the data for research has just begun to appear, there is a limited number of academic papers in this area of studies. Moreover, they are devoted mainly to the description of the mechanisms of the ICO, the advantages and risks of ICO, the review of existing ICO and crypto exchanges, legislation. The absence of fundamental works emphasizes the relevance and scientific novelty of the forthcoming research. The contribution to the existing literature is the identification of factors that affect the successful of ICO. Scientific novelty consists in the formation of the model evaluation of ICO success. Empirical results show that volatility of main cryptocurrencies has significant impact on the success of ICO. The constraints of the platform for Smart Contacts (ERC-20) and dependence on the Ethereum volatility overcome all other factors. Our data contributes with existence literature and shows the insignificance of sector of the project, almost all location region and fluctuation of influence of quality of team. This result may be explained by the uncertainty of the investor about the project (weak signals), absence of the regulation and legal framework. This result is beneficial for owners of companies since it is an argument for decreasing costs for marketing.
A New Learning Algorithm with General Loss for Neural Networks with Random Weights

Neural networks with random weights (NNRWs) which randomly assign weights to new hidden nodes, provide a new and promising stochastic approach for the research of neural networks, and have been proved to enjoy the universal approximation property. However, the design of existing learning algorithms for NNRWs is only based on the square loss function, which hinders the development of NNRWs. For strictly convex and strongly convex loss functions, it is unclear how to design learning algorithms, and what the convergence rates are. In this paper, we answer the questions affirmatively. First, we propose a new supervisory mechanism for constructing NNRWs, and prove the universal approximation property. Then we design a new learning algorithm and analyze the convergence rates of the algorithm, based on which the time complexities are also analyzed. To be specific, the proposed algorithm enjoys sublinear convergence for smooth and strictly convex loss functions, and linear convergence for smooth and strongly convex loss functions. Finally, experimental results on several real-world datasets verify the convergence rates of the algorithm with different loss functions.
Validity Test of Self-Organizing Map (SOM) and K-Means Algorithm for Employee Grouping

Managing employee work discipline needs to be done to support the development of an organization. One way to make it easier to manage employee work discipline is to group employees based on their level of discipline. This study aims to group employees based on their level of discipline using the Self Organizing Map (SOM) and K-Means algorithm. This grouping begins with collecting employee attendance data, then processing attendance data where one of them is determining the parameters to be used, then ending by implementing the clustering algorithm using the SOM and K-Means algorithms. The results of grouping that have been obtained from the implementation of the SOM and K-Means algorithms are then validated using an internal validation test consisting of the Dunn Index, the Silhouette Index and the Connectivity Index to obtain the best number of clusters and algorithms. The results of the validation test obtained 3 best clusters for the level of discipline, namely the disciplinary cluster, the moderate cluster and the undisciplined cluster.
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation

This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020).We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, leveraging heterogeneous knowledge from both the structured knowledge base (i.e.ConceptNet) and unstructured text to better improve the ability of a machine in commonsense understanding.This model has a powerful commonsense inference capability via utilizing suitable commonsense incorporation methods and upgraded data augmentation techniques.Besides, an internal sharing mechanism is cooperated to prohibit our model from insufficient and excessive reasoning for commonsense.As a result, this model performs quite well in both validation and explanation.For instance, it achieves state-of-the-art accuracy in the subtask called Commonsense Explanation (Multi-Choice).
Tren Penggunaan Framework COBIT, ITIL, dan ISO 27001 Pada Rentang Tahun 2014-2018 di Indonesia

The use of Information Technology (IT) frameworks from year to year in Indonesia has shown an increase. However, there is not much research on the use of IT frameworks. The research on IT frameworks can be used by a company to analyze trends in the use of frameworks in Indonesia. That way, it can help a company consider choosing a framework to use. The IT frameworks discussed in this study are COBIT, ITIL, and ISO 27001. Data related to research IT frameworks taken in the 2014-2018 period. Data collection through the Google Scholar web search engine using the Publish or Perish software. The data obtained were analyzed using descriptive quantitative methods. The results of this study can use to determine the use of the IT framework in Indonesia. The amount of raw data obtained from the data collection process was 5,708 data with Authors, Title, Years, and Publisher variables. Research data on titles and keywords obtained in the Indonesian domain amounted to 754 and overseas amounted to 194. From the research results, it can conclude that the most widely used framework is the COBIT framework, with a percentage of 69.3% in the Indonesian and 58.7% in the foreign domain.
KLUMSy@KIPoS: Experiments on Part-of-Speech Tagging of Spoken Italian

In this paper we introduce the main features of the KIParla corpus, a new resource for the study of spoken Italian.In addition to its other capabilities, KIParla provides access to a wide range of metadata that characterize both the participants and the settings in which the interactions take place.Furthermore, it is designed to be shared as a free resource tool through the NoSketch Engine interface and to be expanded as a monitor corpus (Sinclair 1991). KIParla corpus: an introductionThe aim of this paper is to describe the design and implementation of a new resource tool for the study of spoken Italian.The KIParla corpus is the result of a joint collaboration between the Universities of Bologna and Turin and is open to further partnerships in the future.It is characterized by a number of innovative features.In addition to providing access to a wide range of metadata concerning the speakers and the setting in which the interactions take place, it offers transcriptions time-aligned with audio files and is designed to be expanded and upgraded through the addition of independent modules, constructed with a similar attention to the metadata; moreover, it is completely open-access and makes use of open-access technologies, such as the NoSketch Engine platform.Section 2 provides a detailed description of the corpus design, aimed at featuring the geographic, 1
The Construction of College English Translation Teaching Model in the Environment of Information Technology

As one of the more important teaching contents in college English teaching, English translation is also the method that best reflects students' comprehension. Traditional college English teaching cannot improve students' English translation ability due to the single and outdated teaching model. Starting from the information technology environment, this paper analyzes the problems existing in College English translation teaching in China, and puts forward some innovative English teaching modes in order to create an efficient English class and further promote the reform of this teaching.
Centralized Multi-Sensor Robust Recursive Least-Squares Wiener Estimators in Linear Discrete-Time Stochastic Systems with Uncertain Parameters

Among the multi-sensor information fusion estimation problems, this paper proposes the centralized robust recursive least-squares (RLS) Wiener filter and fixed-point smoother for estimating the signal and the state in linear wide-sense stationary stochastic systems with the uncertain parameters in the system and observation matrices. Previously, the robust RLS Wiener filter and fixed-point smoother are proposed by the author in the case of the single-sensor observation for linear discrete-time stochastic systems with uncertain parameters. This paper extends the robust RLS Wiener estimators in the case of the single-sensor observation to the centralized multi-sensor robust RLS Wiener estimators. The signal is observed at each station as degraded by the uncertain parameters in the observation matrix. The centralized multi-sensor robust RLS Wiener filter and fixed-point smoother, proposed in this paper, have the advantage of not using information such as probabilities about the uncertain parameters in the system and observation matrices. Related to the centralized multi-sensor robust RLS Wiener filter, the recursive algorithm for the filtering error variance function of the state is proposed. The estimation accuracies of the centralized multi-sensor robust RLS Wiener filter and fixed-point smoother are superior to the centralized multi-sensor RLS Wiener filter and fixed-point smoother, respectively.
On the generalization of redundancy criteria for linguistic fuzzy rules

The aim of this paper is to generalize existing criteria for determining redundant fuzzy linguistic rules, i.e., fuzzy IF-THEN rules that use evaluative linguistic expressions, and that can be freely omitted from a given rule-base without any impact Perception-based logical deduction (PbLD) is a specific inference method tailored to the linguistic fuzzy rules with linguistic expressions and it has been shown that this setting enables the investigation of redundant rules from a theoretical point of view. First of all, rules suspicious from redundancy are identified. Secondly, their elimination has to be checked as the suspicion can be canceled by a presence of a so-called canceling rule. However, the situation is complicated as even the impact of the canceling rule can be eliminated by another rule. The previous studies considered theoretical results and the final algorithm under the assumption of at most a single canceling rule.This paper generalizes the investigation to a more general case when even sets of rules that cancel a single suspicion can be found. We elaborate new criteria for such a purpose that enable to construct an algorithm for an automatic detection of redundant fuzzy linguistic rules. The elimination of redundant rules in a given system is an important step towards simplicity, clarity, and explainability, that moreover, decreases computational costs.
Experimenting Sentence Split-and-Rephrase Using Part-of-Speech Labels

Text simplification (TS) is a natural language transformation process that reduces linguistic complexity while preserving semantics and retaining its original meaning. This work aims to present a research proposal for automatic simplification of texts, precisely a split-and-rephrase approach based on an encoder-decoder neural network model. The proposed method was trained against the WikiSplit English corpus with the help of a part-of-speech tagger and obtained a BLEU score validation of 74.72%. We also experimented with this trained model to split-and-rephrase sentences written in Portuguese with relative success, showing the method’s potential.
Analysis of the Supply Network for the Internal Combustion Engine Model and Electric Motor Model Using Social Network Analysis

This study analyzes the supply chain of a car manufacture and a supplier (number of parts supplied by supplier, supply per part) by comparing internal combustion engine model (T car model 1.6 gasoline turbo, Noblesse) and electric motor model (T car model Noblesse). Social network analysis was conducted to observe how the centrality of suppliers (nodes) of internal combustion and electric vehicles changes. More specifically, by comparing the strength of the whole network, the engine transmission of the internal combustion, the PE (Power Electric) of the electric vehicle, and the battery parts in detail, how the centrality of eigenvector changes, and how the network structure changes according to the characteristics of each item. As a result, the eigenvector centrality of the electric vehicle is several times larger than that of the internal combustion model. Secondly, the network connection strength of electric vehicle core component suppliers was much stronger than that of the internal combustion model. Finally, as the dependence on battery cell suppliers for electric vehicles increases, the negotiation power within the parts supply system of automobile manufacturers is weakening, leading to the horizontal division of labor. This study aims to provide insights on the supply network following the future transition of electric vehicles.
Carnival Play

Developed within the scope of a SciTech research project, this chapter records in a procedural way the design centred on senior users for a set of three serious games for the eHealth field, designated by the authors as “Carnival.” The chapter, having as its leitmotif the project aforementioned, looking at its motivation, breaks down the systems augmented feedback interfaces—BodyGrip and SHaRe—to evaluate, rehabilitate, and monitor dexterity and manual strength. Topics related to empathy and well-being in the user experience design process, namely guidelines for empathy in different project phases, participatory design, inclusiveness, and amusement are identified. Withal listed the development phases of three games dynamics inherent to the “Carnival” set —“High Striker,” “Claw Machine,” “HotDog Sauce”—punctuating with the discussion and contributions to the e-health area describing its potential for Evaluate, rehabilitate, and monitor dexterity and manual strength.
IMPLENTASI KEBIJAKAN PENERIMAAN PESERTA DIDIK BARU DINAS PENDIDIKAN DAN KEBUDAYAAN KOTA SAMARINDA

One of the very important aspect in the Education is related to the enrollment of new students which has a very complex problems. To overcome all the problems, the District Head Office of the Education and Culture Kota Samarinda, published a policy on the new students enrollment, year 2016/2017 by using online system/model. This research Purposes are (1) to describe the implementation of the District Head Office of the Education and Culture Kota Samarinda for new students enrollment policy, year 2016/2017 for SMA level and (2) to know how the implementation of the District Head Office of the Education and Culture Kota Samarinda for new students enrollment policy, year 2016/2017 can be accepted by community. This research uses qualitative approach with the technique of data collection through indept interview, observation and the documentation study. The sources of the informations for this research are the school headmasters and new student parents/new students. The research findings are (1) according to the school headmasters (the policy implementors), year 2016/2017 that the new students enrollment policy can be implemented on the SMA levels, although there are some problems associated with the students non-academic achievements, disability/Environment Devolopment(ED)/PTK, and the school capacity and the stages of the registration path. While according to the new students’ parents/ new students, the new students enrollment policy, year 2016/2017 can be accepted with four notes, namely the problem of the non-academic students’ achievement, disability/ED/PTK, school capacity and the stages of the new students enrollment path; (2) related to the registration phase students’ non-academic achievements, disability /ED/PTK done before the regular /general path, causes many problems; (3) system/model of selection for students’ non-academic achievements, disability/ED/PTK based on the leves of the highest value of the National Examination results causes problems; and (4) the school capacity according to the policy implementors and new students’ parents/new students hope the government provides enought school capacity for the community; and (5) Headmasters always have their HP on and they are always at school.
A Simple and Efficient Tensor Calculus for Machine Learning

Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. A key concern is the efficiency of evaluating the expressions and their derivatives that hinges on the representation of these expressions. Recently, an algorithm for computing higher order derivatives of tensor expressions like Jacobians or Hessians has been introduced that is a few orders of magnitude faster than previous state-of-the-art approaches. Unfortunately, the approach is based on Ricci notation and hence cannot be incorporated into automatic differentiation frameworks from deep learning like TensorFlow, PyTorch, autograd, or JAX that use the simpler Einstein notation. This leaves two options, to either change the underlying tensor representation in these frameworks or to develop a new, provably correct algorithm based on Einstein notation. Obviously, the first option is impractical. Hence, we pursue the second option. Here, we show that using Ricci notation is not necessary for an efficient tensor calculus and develop an equally efficient method for the simpler Einstein notation. It turns out that turning to Einstein notation enables further improvements that lead to even better efficiency. The methods that are described in this paper for computing derivatives of matrix and tensor expressions have been implemented in the online tool www.MatrixCalculus.org.
Variability Modeling in Software Product Line: A Systematic Literature Review

Variability is the core concept characterizing software product line engineering. Over the past decades, variability modeling has been an emerging topic of extensive research that resulted in different units of variability (e.g., feature, decision, orthogonal, UML) with various variability modeling techniques. Hence, there is a need for a comprehensive study to shed light on the current status, diversity, and direction of the existing variability modeling techniques. The main objective of this research is to characterize the diversity of modeling variability and provide an overview of the status of existing literature. We conducted a systematic review with six formulated research questions and evaluated 74 studies published between the years 2004–2007. The results indicated that the majority of the studies proposed techniques for modeling variability in a separate model rather than modeling variability as an integral part of the development artifact, and the feature model was found as the most common unit of variability. Our study also identified more ambiguity in handling complexity issues as well as the need for a commonly accepted way of addressing variability model evolution. The strength of the evidence in support of the proposed approaches with illustrative examples and lack of robust tooling support that have confined the generalizability of the existing studies need further improvement with more robust empirical studies.
Lexical Relation Mining in Neural Word Embeddings

Work with neural word embeddings and lexical relations has largely focused on confirmatory experiments which use human-curated examples of semantic and syntactic relations to validate against.In this paper, we explore the degree to which lexical relations, such as those found in popular validation sets, can be derived and extended from a variety of neural embeddings using classical clustering methods.We show that the Word2Vec space of word-pairs (i.e., offset vectors) significantly outperforms other more contemporary methods, even in the presence of a large number of noisy offsets.Moreover, we show that via a simple nearest neighbor approach in the offset space, new examples of known relations can be discovered.Our results speak to the amenability of offset vectors from non-contextual neural embeddings to find semantically coherent clusters.This simple approach has implications for the exploration of emergent regularities and their examples, such as emerging trends on social media and their related posts.
Two New Classes of Codebooks Asymptotically Achieving the Welch Bound

Exponential sums over Galois rings have many applications in coding theory, cryptography and algebraic combinatorics. In this article, we employ additive characters and multiplicative characters over Galois rings to present two classes of codebooks, and prove that these codebooks asymptotically achieve the Welch bound. In addition, the resulting codebooks have new parameters.
Factoring polynomials over finite fields

In this paper, we describe a new polynomial factorization algorithm over finite fields with odd characteristics. The main ingredient of the algorithm is special singular curves. The algorithm relies on the extension of the Mumford representation and Cantor’s algorithm to these special singular curves.
Identification of Agricultural Plant Row Using the Clustering Algorithm in the Navigation of Mobile Robot

This paper describes navigation method usable in agriculture plant row for mobile robot. The proposed method with clustering algorithm uses data obtained by Hokuyo laser sensor. Using the created solution it is possible to determine plant in a row when mobile robot using the local navigation principle. Crops in a row are characteristic waypoints that create the vertices of the polygon. From this polygon the center of gravity is calculated, which determines the center position of the plant row. From this polygon the center of gravity is calculated, which determines the center position of the plant row. This position is used for mobile robot control algorithm. The processed data by scanners provide information not only on waypoints but also on any obstacles in the agricultural plant row.