Reviewerly/exHarmony huggingface dataset:
Psycho-semiotic model of thinking. Combining Klein and Peirce theory of symbol for more comprehensive model of mind

Thesis: Aim of the paper is to present Melanie Klein and Charles S. Peirce concept of symbol in order to combine them into scheme that presents conscious and unconscious aspect of thinking through symbolic signs (signs based on convention).&#x0D; Presented concepts: Paper presents concept of symbol in psychoanalytical and semiotic perspective. Psychoanalytical view is based on interpretation of symbol according to object relation paradigm proposed by Klein. There are two reasons for selecting her theory for the model: it is most closely bound with interdependency between communication and thinking plus her concept of proper symbol fulfills definition of symbolic sign in Peirce theory, due to deployment of matter of absence in substitution process. Peirce theory however is selected to present semiotic perspective not only for its good linkage to Klein’s “proper symbol” but also for its accurate understating of object representation in quasi- mind through Representamen and as a result recognition of symbol embedment in code through unlimited semiosis. Chosen concepts are consolidated into psycho-semiotic model of thinking which recognizes symbol to be co-created by unique internal world of unconscious phantasy with simultaneous employment of semiotic devices oriented to external, group order perspective.&#x0D; Results and conclusions: Proposed psycho-semiotic model of thinking enhances psychoanalytic view, based on drive for object, by recognizing communication means required for meaningful relation and with that for thinking itself. As a result conceptualizing thinking processes is enriched with semiotic discoveries such as mechanics and structure of Representamen and Interpretant, along with indispensable code rules, with unlimited semiosis at its core. In turn psychoanalytical view adds to semiotic perspective sensitivity to individual potential and constrains when code is in use and with that raises precision of exploration in the field.&#x0D; Contribution to the field: Proposed model enriches theory of thinking based on object relations with semiotic sign theory, which being focused on communication serves as a frame for establishing object relations and their conceptualization. In turn employing psychoanalytic perspective into semiotic field brings back code theory to actual code usage, and by that expands it to various unconscious forces, which ultimately determine Interpretant
The Relationship Between Dollarization and Inflation: " Egypt at period 1994-2016 is A Model "

The paper aims to make comprehensive view of theoretical frameworks that determent the relationship between dollarization phenomenon and inflation rate and fluctuation. It also aims at analyzing and explanting the mechanisms on the impact played by one toward the other . The paper, as well as, try to makes quantitave model for testing and determining the nature, value, and direction of the relationships between those two variables and the capability of the actual situation in Egypt during the period 1994-2016 for reflecting the theoretical situation. The Autoregressive Distributed Lag methodology (ARDL) was used by Pesaran et al. (2011) is adopted to achieve that. The results of the measurement show a statistically significant negative impact on the inflation rate (INF), a significant positive effect of fluctuations in inflation rates (GINF), and no significant effect on the difference between inflation and exchange rate fluctuations (GIEX) In the dollarization index Duration of study.
THU_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers

Toponym resolution is an important and challenging task in the neural language processing field, and has wide applications such as emergency response and social media geographical event analysis.Toponym resolution can be roughly divided into two independent steps, i.e., toponym detection and toponym disambiguation.In order to facilitate the study on toponym resolution, the SemEval 2019 task 12 is proposed, which contains three subtasks, i.e., toponym detection, toponym disambiguation and toponym resolution.In this paper, we introduce our system that participated in the SemEval 2019 task 12.For toponym detection, in our approach we use TagLM as the basic model, and explore the use of various features in this task, such as word embeddings extracted from pre-trained language models, POS tags and lexical features extracted from dictionaries.For toponym disambiguation, we propose a heuristics rule-based method using toponym frequency and population.Our systems achieved 83.03% strict macro F1, 74.50 strict micro F1, 85.92 overlap macro F1 and 78.47 overlap micro F1 in toponym detection subtask.
Visibility Estimation Using Roadside Cameras: A Calibration Based Method

Poor visibility in bad weather has significant impacts on traffic safety. An ability to accurately monitor visibility can help reduce the driving risk under this condition. However, visibility measuring based on sensors are precise but costly, and measuring based on vision are lower cost but more complicated in training and deployment. This paper proposes a simple yet effective method to estimate visibility using roadside cameras. The method is established based on the definition of visibility in meteorology, taking a great distance at which an object can be clearly discerned from a bright background as the visibility. The method takes the sky region as the reference background image and using relationship mapping for the distance measurements. This proposed method simplifies the calibrating and measuring process, visibility estimation can be obtained without training or configuration, making it easier to use in practice.
Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis

This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue.To address this challenge of predicting the emotion considering its context, we propose a Multi-View Turn-by-Turn (MVTT) model.Firstly, MVTT model generates vectors from each utterance using two encoders: word-level Bi-GRU encoder (WLE) and character-level CNN encoder (CLE).Then, MVTT grasps contextual information by combining the vectors and predict the emotion with the contextual information.We conduct experiments on the effect of vector encoding and vector combination.Our final MVTT model achieved 0.7634 microaveraged F1 score.
Sentiment Analysis on Product Features Based on Lexicon Approach Using Natural Language Processing

Sentiment analysis has played an important role in identifying what other people think and what their behavior is. Text can be used to analyze the sentiment and classified as positive, negative or neutral.Applying the sentiment analysis on the product reviews on e-market helps not only the customer but also the industry people for taking decision.The method which provides sentiment analysis about the individual product's features is discussed here.This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2.sentiment analysis regarding the product's feature present in the product review [Sub Domain: Mobile Phones].It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market.The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity.The second part of the paper is an extension to the first part in which the customer review's containing product's features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis.Here, mobile phones are used as the product with features as screen, processors, etc.This gives a business solution for users and industries for effective product decisions.
DEVELOPMENT PROJECT MANAGEMENT CAPABILITY OF THE INFRASTRUCTURE PROGRAM. CHERNOBYL CASE

The Project Management methodology became one of the bases for the development of the ChNPP. The subject of research is the process of development the project management capability for infrastructure program based on the Chernobyl case. Objective: to develop and apply a tool for development key competence as the organization capability for managing infrastructure program using Chernobyl NPP experience. Tasks: to analyze the historical facts, factors influencing the organizational competence and the possibility of introducing key competence in the organization‘s activities, to obtain a general algorithm for the development project management capability. Research methods: historical analysis, logical generalization, analysis and synthesis, structural analysis. Results: The article considers the key competence of the organization as the main component of the management capability of the complex program the company implements. The facts given above say that the activity which is carried out on a platform of the Chernobyl NPP, in many aspects is innovative and at creation on the Chernobyl NPP of an effective Knowledge Management System of project implementation experience, this knowledge can be applied as the professional development of acting project managers of the ChNPP (for example, to development of target courses), and to transfer of the accumulated experience (through realization of the benchmarking mechanism) to external costumers. The weighed system approach to Knowledge management according to the Methodology of project management can not only render invaluable service to system of preparation of the personnel of the NPP, but also to promote definition of possible ways of collecting, storage and commercial realization of intangible assets of the Conclusions: The state-owned enterprise ChNPP stands at the border of a radical reorganization, when project-oriented business management is implemented alongside the existing standard organizational structure of business management. The activities at the Chernobyl NPP site are in many respects innovative and must be taken into account when organizing work. In order to create an effective project management system, the professional knowledge of the existing Chernobyl NPP project managers must be applied, and the accumulated experience (through the implementation of the benchmarking mechanism) to external customers must be transferred. A weighted systemic approach to knowledge management in accordance with the project management methodology can not only provide an invaluable service to the NPP personnel training system, but also help to identify possible ways of collecting, storing and selling intangible assets.
Revenge porn as a form of cyber misogyny

Among many forms of abuse of privacy in the cyberspace and social media networks, particular attention is given to the so-called cyber misogyny. It refers to the existence of deeply rooted prejudices against women and encompasses various forms of gender- based hatred, harassment and violence against women that occur in a virtual (cyber) space. The subject of this paper is an analysis of emerging forms of cyber misogyny, particularly focusing on one of its most widespread forms: revenge porn. Through revenge porn in the cyberspace, misogyny can be manifested through publishing someone?s intimate photos, that is, photos of their intimate private life or sexually explicit photos and videos. In all these cases, publications in cyberspace or social media occur without the knowledge, willingness or consent of the photographed person. The purpose behind such activity is to shame and/or humiliate the victim by causing her pain and moral suffering for reasons of revenge. The anonymity of the victim is not preserved; on the contrary, her identity and other relevant data are listed in order to identify and make the victim more visible. Therefore, the aim of the paper is to point out to the seriousness of this phenomenon that occurs in the cyberspace, which presents violation of privacy and causes difficult consequences for the victim. Furthermore, the aim is to investigate the possibilities of criminalization and protection of the (mostly female) victims of this criminal act. The main topics analysed in this paper include: a definition of cyber misogyny and revenge porn, an explanation of the most common forms of its manifestation, existing forms of protection, and criminalization of revenge porn worldwide and in the Republic of Serbia.
An Exploratory Study of Requirements Engineering Practices in Indonesia â€“ Part 1: Concepts and Problems

This paper provides the first part of statistical research findings of an exploratory study of the requirements engineering practices implemented in software development processes in Indonesia. This first part attempts to reveal facts regarding concepts and problems exist in such requirements engineering practices. We developed a comprehensive online questionnaire consisting of both closed- and open-ended questions in order to capture such facts. We invited 158 participant candidates representing industry and higher education institutions, however, 31 of them joined our web-based survey. Results show that most of participants confirm that requirements engineering must be properly performed in order to clearly define the valid product. They, however, encounter some difficulties in implementing requirements engineering concepts in practice due to a tight schedule, inappropriate skill of the analysts and inadequate tools used.
Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.
Comparative Analysis of Some Efficient Data Security Methods among Cryptographic Techniques for Cloud Data Security

The concept of cloud computing model is to grant users access to outsource data from the cloud server without them having to worry about aspects of the hardware and software management. The owner of the data encrypts it before outsourcing to a Cloud Service Provider (CSP) server for effective deployment of sensitive data. Data confidentiality is a demanding task of cloud data protection. Thus, to solve this problem, lots of techniques are needed to defend the shared data. We focus on cryptography to secure the data while transmitting in the network. We deployed Advanced Encryption Standard (AES) used as encryption method for cloud data security, to encrypt the sensitive data which is to be transmitted from sender to receiver in the network and to decrypt so that the receiver can view the original data. Arrays of encryption systems are being deployed in the world of Information Systems by various organizations. In this paper, comparative analysis of some various encryption algorithms in cryptography have been implemented by comparing their performance in terms of stimulated time during Encryption and decryption in the network.&#x0D; Keywords: AES, Data Control, Data Privacy, Data Storage, Encryption Algorithms, Verification.
Errors in Students' Writing Composition in Simple Present Tense “MY DAILY ACTIVITY”

ABSTRACT&#x0D; This research is to find out the types of error analysis found in the Students’ Writing Composition in the aspect of grammar, punctuation and spelling also is to find out the most dominant kind of error analysis found in Students’ Writing Composition in Simple Present Tense form. This study applies the quantitative and qualitative methods. This study used documentation as a method of collecting data. From the analysis of this research, it shows that the students’ error in writing composition of simple present tense consists of three aspects of writing, they are they are in grammar, punctuation and spelling. In the aspect of grammar, there are 66 data or 56.89 %, which consist of some errors in class words (part of speech) such as in in verb (25 data or 37.88 %), determiner/article (16 data or 24.24 %), preposition (15 data or 13.64 %), noun (9 data or 13.64 %) and pronoun (1 data or 1.51 %). In punctuation, there are 28 data or 28.14 %, while in spelling there are 22 data or 18.97 %.&#x0D;
Hybrid Symbolic Regression with the Bison Seeker Algorithm

This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression. While the basic version of symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. This paper aims to compare the basic version of symbolic regression and hybrid version with the lifetime adaptation of individuals via the Bison Seeker Algorithm. Author also investigates the influence of the Bison Seeker Algorithm on the rate of evolution in the search for function, which fits the given input-output data. The results of the current study support the fact that the local algorithm accelerates evolution, even with a few iterations of a Bison Seeker Algorithm with small populations.
Multi-objective Artificial Bee Colony Algorithm with Information Learning for Model Optimization of Extreme Learning Machine

As an important branch of neural networks, extreme learning machine with single-hiddenlayer feedforward have been a effective tool for regression and classification applications. However, it is difficult for ELMs to strike a balance between testing accuracy and generalization due to the random input weights and hidden biases. In this paper, a novel multi-objective optimization method of ELM based on swarm intelligence behavior is proposed to obtain good generalization ability and high testing accuracy simultaneously. The multi-objective optimization algorithm is used to select optimal input weights by minimizing this testing error and the norm of output weight. In order to improve optimal performance, an information learning method is introduced to multi-objective artificial bee colony algorithm. Experiments on four UCI data sets are conducted, and original ELM, ELM with nondominated sorting genetic algorithm and the proposed algorithm are compared. The results show that the proposed algorithm can generally obtain better generalization performance and higher accuracy with more compact network than original ELM and ELM with nondominated sorting genetic algorithm simultaneously.
GRAMMATICAL INTRICACY DALAM CERITA PENDEK KARYA PENULIS ANAK INDONESIA

This paper aims to describe the grammatical intricacy of short story written by an Indonesian child author. Grammatical intricacy is built of clause, nominal group, and lexis. These three aspects show us how the author expresses his mind into language. The data source is short story titled “Mahkota Surga untuk Ayah”. The author is an Indonesian child whose her story has been published by Lintang. This research is a kind of descriptive qualitative using observation-notification technique (simak-catat). The data are clauses, nominal groups, and lexis of the text. The result shows that the author exploited the language in 148 clauses and most of them are complex clauses. The thing goes different for nominal groups, most of them are in simplex. Most of lexis are exploited in congruent forms. From the characteristics of the language author used this text is written in spoken language and has a high grammatical intricacy.
A novel firefly algorithm with self-adaptive step strategy

In the standard firefly algorithm, the random moving step is very important to the direction of the firefly movement, and the parameter alpha plays an important role in the random moving step. In this paper, we proposed a self-adaptive step strategy based on distance control in this paper, and we called it SASFA. Thirteen well-known benchmark functions are used to verify the performance of our proposed method; the computational results show that SASFA is more efficient than many other FA algorithms.