Reviewerly/exHarmony huggingface dataset:
Design and Development of a Life Insurance Mobile Application for Young Adults

In this modern era, accidents could occur anywhere any time before prevention can be made. Life insurance could help lend a sum of money to reimburse the medical fees. But not everyone has life insurance, some people think that life insurance is a waste of money where they think people should save money at an early age and only pay when there is actual accident occur. This occurs in young adults too. Some young adults do not know the knowledge of life insurance. Therefore, this study is conducted to help young adults understand and provide the awareness of the importance of life insurance by creating a mobile application on Android Operating System using Android Studio. This system was created based on waterfall system development life cycle and evaluated using cooperative evaluation method. The mobile application can display and provide the knowledge of life insurance and calculate the suggested life insurance plan for the user. Five participants were randomly selected for cooperative evaluation. They are young adults age between 20 to 25 years old and have their own income. Out of those five participants, two of them like the mobile application while the other three participants suggested several improvements on the developed mobile application.
A New Tool for Cognitive Workload Assessment in System Design Prototypes

Mental workload is an important indicator of an operator’s performance when executing a certain task. As a design concept, it is fundamental for monitoring and assessing his interaction with technological devices, such as imaging systems. A bad design can affect the mental workload of the operator, and therefore, becomes useful to assess it while performing the required tasks. This work presents a tool for cognitive workload assessment that is composed of two open-source and low-cost hardware parts: an electroencephalography system and an eye-tracker. Both are synchronized via an open-source software: Lab Streaming Layer. This paper describes the architecture of the sensor system, considering also specially developed hardware and software components that were included to allow the integration of the different subsystems. In order to show the performance of the system as tool to assess some psycho-physical indicators, examples of dedicated signal processing are also presented.
Fault-Prone Software Classes Recognition via Artificial Neural Network with Granular Dataset Balancing

In this paper we have investigated the fault proneness of the software source code using artificial intelligence methods. The main contribution lies on improving the data pre-processing step. Before we put the data into an Artificial Neural Network, are implementing PCA (Principal Component Analysis) and k-means clustering. The data-clustering step improves the quality of the whole dataset. Using the presented approach we were able to obtain 10% increase of accuracy of the fault detection. In order to ensure the most reliable results, we implement 10-fold cross-validation methodology during experiments.
The Degree of Openness of Undergraduate Education to Contemporary Art

We live today in a complex, ever changing world, with a wealth of scientific and technological discoveries, along with a great variety of artistic events; a world in which new concepts are emerging that try to define the condition of contemporary man.As a consequence, previously known parameters change; new terms are emerging, determined by the reality of the new times, globalization-driven reality, the diminishing-tending towards disappearance-of the boundaries between culture and subculture and the predominance of the visual.Inevitably, the educational act is influenced by these phenomena.The extent to which education in the artistic sphere is open to understanding these changes will determine the growth and development of people capable of adapting to new social and cultural-artistic realities.Without minimizing the values and results of the teaching experience, it is clear that the younger generation of teachers has a much more flexible approach, even though curriculum documents often operate in fixed frames.
Simultaneous Multivariate Outlier and Trend Detection

We develop a methodology that combines stable principal component pursuit (SPCP) and elastic net regression to perform multivariate outlier and trend detection simultaneously. The SPCP framework detects both univariate and multivariate outliers by decomposing a data matrix into its sparse and low-rank components. Elastic net regression applied to the low-rank matrix identifies the response variables that are well-explained by a set of covariates without being affected by influential outliers. By combining these techniques into a single objective function, we simultaneously detect univariate and multivariate outliers and trend with an accompanying estimate of magnitude for each. Our methodology is applied to both real and synthetic data to show its value and accuracy.
DEBESŲ KOMPIUTERIJOS PROGRAMINĖS ĮRANGOS PASLAUGŲ GREITAVEIKOS TYRIMAI / PERFORMANCE ANALYSIS OF CLOUD COMPUTING SOFTWARE SERVICES

The paper presents the performance analysis of the developed software as a service. In OpenStack cloud infrastructure, the software services for hemodynamic flow modelling and particle technology applications have been developed by using Apache jclouds API. The performance of the hosted cloud infrastructure has been assessed testing virtual memory, CPU, disk IO, network and the developed software services. The measured performance of the virtual OpenStack resources (full XEN virtualization) has been compared with that of the virtual Eucalyptus resources (KVM paravirtualization) and the native hardware.
Project-based optical design practice course and teamwork: from a programmer to a lens designer

Computer Aided Optical System Design is an engineering practice course for undergraduate students in College of Optical Science and Engineering, Zhejiang University. When the students have just completed Applied Optics course in spring-summer semester, they will take the practice course in a short semester in summer vocation. This is a projectbased practice course, including optical software developing and lens design. The teaching idea is not only to improve the students’ engineering skills but also to connect different knowledge they have studied, to build a comprehensive knowledge structure, and to develop the team spirit, organizational capability, leadership and communication skills. This course is designed as an engineering project with a contract between the teacher group and every design group of 3 students. In this paper we describe the course program and how to provide a chance to combine different knowledge into a project. In every student group, developing ray trace software of good reliability is the teamwork, and each student will design a photographic lens independently. The lens with the best optical performance will be selected as the team’s result, and be drawn into a system diagram and part drawings. The whole process is both competitive and cooperative, and gives the students the experiences of programmers and lens designers. Most of the students appreciated the practice course and named it as “the most memorable short semester”.
Multimodal Data Fusion with Quantum Inspiration

Language understanding is multimodal. During human communication, messages are conveyed not only by words in textual form, but also through speech patterns, gestures or facial emotions of the speakers. Therefore, it is crucial to fuse information from different modalities to achieve a joint comprehension.
F0 contour prediction for the Kazakh language

The article presents work on predicting the fundamental frequency (F0) values for the Kazakh language. The fundamental frequency plays one of the most important roles in the perception of speech, and at the same time modelling continuous F0 is one of the most difficult tasks in the development of intonational speech synthesis systems. The main and obvious difficulty is that a person is able to say the same sentence with different intonations and with different tones. In this work, we used deep neural networks for accurate and qualitative prediction F0 values as close as possible to the natural sounding of Kazakh speech.
Evaluation of a Feature Set with Word Embeddings to Improve Named Entity Recognition on Tweets

In this paper, we present the Named Entity Recognition system and we evaluate baseline classifiers. We use tweets as informal and noisy texts including emoticons, abbreviations, which significantly degrade the performance of classifiers. We present the dataset format, the feature set, we evaluate and test each classifier subject to different combinations of features. Finally, we discover the most representative set of features. Our experimental results show that the presented system is reached at 72% level in precision, 69% in recall and 69% in F1 (micro average), respectively.
A Refactoring Catalog for Lambda Expressions in Java

Language evolution provides room for improvements in existing programs. Developers can update a system design by using new language features in existing code bases. In this sense, we present a catalog with a set of refactorings to deal with lambda expressions in Java. A case study evaluates the applicability of the proposed refactorings in a set of open source projects, using an Eclipse-based tool for searching for refactoring opportunities dealing with lambda expressions.
Overview of Recommender System Engine

This chapter presents the architecture and mechanism (“engine”) of the proposed Recommender System built from structured and unstructured customer data.
AliISA

Online shopping has been a habit of more and more people, while most users are unable to craft an informative query, and thus it often takes a long search session to satisfy their purchase intents. We present AliISA - a shopping assistant which offers users some tips to further specify their queries during a search session. With such an interactive search, users tend to find targeted items with fewer page requests, which often means a better user experience. Currently, AliISA assists tens of millions of users per day, earns more usage than existing systems, and consequently brings in a 5% improvement in CVR. In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.
Multi-criteria analysis for renewable energy generation: A case of Tokyo

Abstract A multi-criteria analysis to determine the most suitable renewable energy technology in Tokyo, Japan, is presented in this study. Several criteria have been considered for the analysis including technical, social, economic, and environmental. Sensitivity analysis has been done to check the solidity of the data used in the analysis. The results show that the solar photovoltaic technology is preferred in Tokyo, followed by wind and biomass energy.
Character Localization Based on Support Vector Machine

The purpose of character localization is to detect and locate a certain character from the picture, which can be used in automatic testing task. In this paper, we propose a new virtual keyboard character localization pipeline, mainly including image preprocessing, feature extraction, training classification and identification and positioning. It combines HOG feature extraction algorithm and SVM classifier to complete the design and implementation of character positioning, including image sample acquisition and processing, image feature extraction, SVM training, target character recognition and positioning. The experiments are done on characters in different virtual keyboards, and get more accurate results than CNN based text detection method.
Bringing Video Game Characters into the Real World on a Holographic Light Field Display

This paper discusses the design and technical choices of a proof of concept migrating video game character that can move from a game environment to a holographic environment rendered on a novel holographic light field display. We pair these two environments with interactions that are consistent to each, using a game controller for interaction in the game environment and voice, gesture and face tracking in the holographic environment. Finally, we carried out a pilot study to assess the level of social presence, consistent migration and coherent experience in our proposed system.
A Novel Dialogue Manager Model for Spoken Dialogue Systems Based on User Input Learning

The complexity of the dialogue manager is a major issue in spoken dialogue systems. In this work, a novel dialogue manager based on user input learning is proposed to overcome this issue. In the proposed model back-end functionality is considered as a set of functions a user can trigger through the dialogue manager. It uses these functions as classes for the classification of user inputs. To maintain the context of the dialogue interactions, a context tree is used. Consequently, the model performs its task as two classification tasks to identify the function a user input may trigger and use the context to maintain the discourse of the dialogue. The model shows promising results and proves that a dialogue manager can be integrated into a spoken dialogue system much more directly with less hassle.
A Local Feature Curve Extraction Algorithm for Dongba Hieroglyphs

Dongba hieroglyph is a kind of very primitive picture hieroglyphs; they express meaning by using pictures. This makes the same type words have some similarities in morphology and structure, and they are distinguished only by local differences. In this paper, we analyse the basic structural of the single graphemes in Dongba hieroglyphs. By analysing the writing methods and habits, we give a local feature curve extraction algorithm for Dongba Hieroglyphs based on CDPM (Connected Domain Priority Marking Algorithm) and DCE (Discrete Curve Evolution Algorithm). The algorithm can extract local feature curves which can contain one or more glyphic features, and it can also combine redundant curves based on template curves to facilitate the comparison between the local curves and template. Extracting the local feature curves of Dongba hieroglyph is helpful to analysis the local similarities in different hieroglyphs, classify and retrieve hieroglyph. Moreover, it also has very important significance in studying the creation method of Dongba hieroglyph.
Processing multidimensional optical signals for measurement solid deforming

This article has been described some features of processing multidimensional optical signals for measurement solid deforming. The object shape is measured by optical triangulation scanner. The solid deforming is defined by compare measured profile and underwear profile of aim object. The developing algorithm for profiles comparison has been shown in this research. It consists by two stages. The first stage is splitting set of measuring point’s profile on subset. The subset is certain part of object, such as straight line and circular arc. The second stage is superposition part of objects and corresponding line’s equation. The sample of superposition measured rail profile and underwear rail profile, compassion with existing method of solid deforming has been shown in conclusion.
State Estimation Under Ordinary Impulsive Inputs

InEstimationstate this section, we deal with the problem of state estimationEstimationstate for systems that develop under unknown but bounded ordinary impulsive inputs. This requires to treat the so-called “observation problem” which is solved here. However, the problem allows two types of settings—the one of guaranteedMeasurementguaranteed estimationEstimationguaranteed calculated in advance, namely, before the arrival of the available measurement (as a worst case situation) and the one calculated after its arrival, on-line, as a closed-loop version [1, 3, 5–8]
A hybrid general framework of cuckoo, immune and genetic search algorithms

Several well-known population-based algorithms have been proposed in the literature and demonstrated its effectiveness. In this paper, we propose a hybrid general framework that uses Cuckoo, Immune and Genetic search operators to iteratively evolve its current population. Then, the proportionate selection mechanism is used to select from the produced populations based on their fitness values.
The Concept of Interaction Triggers in Audiovisual Design Model and Its Application to Develop an Interactive Museum

This article describes how the methodology of Audiovisual Design was used in the production process of audiovisual content for an Interactive Museum, named Student Citizen. It focuses on how Interaction Triggers were developed considering the diversity of the Museum’s audience, especially high school students. To validate the methodology, personas and user scenarios were developed focusing on interaction and role changes. Each role described in the Audiovisual Design model has associated affordances to engage (Triggers of Action) or to relax (Triggers of Inertia). How individuals change roles and behave at each level represent a central element in production of contents for complex audiovisual systems.
Mining Patterns from Genetic Improvement Experiments

When conducting genetic improvement experiments, a large amount of individuals (≈ population size * generations) is created and evaluated. The corresponding experiments contain valuable data concerning the fitness of individuals for the defined criteria, such as run-time performance, memory use or robustness. This publication presents an approach to utilize this information in order to identify recurring context independent patterns in abstract syntax trees (ASTs). These patterns can be applied for restricting the search space (in the form of anti-patterns) or for grafting operators in the population. Future work includes an evaluation of this approach, as well as extending it with wildcards and class hierarchies for larger and more generalized patterns.
Adjustment and Correction Demarcation Points in Dongba Hieroglyphic Feature Curves Segmentation

Dongba hieroglyph is a kind of very primitive picture hieroglyphs. Its picture features allow us to analyse the overall and local features of Dongba characters in combination with the existing feature extraction, simplification and segmentation algorithms for shape in computer vision. Moreover, the analysis of the local features of Dongba hieroglyphs play an important role in the study of Dongba hieroglyph’s writing, the evolution process of hieroglyphs and the comparison between similar hieroglyphs. Therefore, in this paper, we first use the Chain-Code Based Connected Domain Priority Marking Algorithm (CDPM) and the Discrete Curve Evolution Algorithm (DCE) to obtain the simplified feature curve of Dongba hieroglyph. Then, we focus on the selection and adjustment of demarcation points and local curve segmentation. And, the experiment proves that our method can further correct the potential errors in the curve segmentation, which is helpful to improve the correct rate of hieroglyph’s local feature extraction.
Parametrizing convection selection

Convection selection in evolutionary algorithms is a method of splitting the population into subpopulations based on the fitness values of solutions. Convection selection was previously found to be superior to standard selection techniques in difficult tasks of evolutionary design. However, reaching its full potential requires tuning of parameters that affect the performance of the evolutionary search process. Performing experiments on benchmark fitness functions does not provide general knowledge required for such tuning. Therefore, in order to gain an insight into the link between the characteristics of the fitness landscape, the parameters of the selection technique, and the quality of the best found solutions, we perform an analysis based on the NKq model of rugged fitness landscapes with neutrality. As a result, we identify several rules that will help researchers and practitioners of evolutionary algorithms adjust the values of convection selection parameters based on the knowledge of the properties of a given optimization problem.
METHODICAL ASPECTS OF USING MENTAL MAPS IN TRAINING OF BACHELORS OF PEDAGOGICAL SPECIALITIES

The article discusses the possibilities of using mental maps in training of bachelors of pedagogical specialties. Technological and didactic features of online services for developing mental maps, such as Mindmaps, Mind42, etc. are described. Methodical aspects are presented also by the description of the principles of introduction of mental maps in training process accompanied with concrete examples. The organization of work on using mental maps in real educational process has to be determined by a number of the methodical principles among which, in particular, one can indicate the principle of freedom of choice of a format of representation of the described object and communications between its elements. Such a choice assumes a possibility of some variation of this or that basic configuration depending on preferences of the student. The principle of activity is to provide conditions for purposeful mastering by students separate actions to build mental maps and operating with them, as well as combinations of these actions of varying degrees of generality. The principle of full coverage involves creation of various opportunities of effective use of mental maps at all stages of educational process (an explanation of new material at lectures, fixing it on laboratory and practical classes, self-preparation for the current and final assessment). The proposed methodological solutions were successfully tested in Penza State University with second and third year students of the Faculty of Physics, Mathematics and Natural Sciences in the study of the discipline “Programming”. As a result of approbation, students had accumulated a kind of bank of mental maps of various formats and different degrees of generality. This bank was actively used in the course of the current and final diagnostics of students’ preparation in the subject. As a result of approbation, the significant advantages of using mental maps in terms of the quality of students’ preparation in the discipline, which was especially clearly manifested in relation to the initially lagging students, were found out.
Towards a Hybrid Minimax Recommender for Free-Roaming Museum Visits

This paper presents a novel minimax hybrid recommender for free-roaming museum visits that is based on a new museum visit concept that was developed to capture the spatial, temporal and content-based dynamics during free-roaming museum visits. The complex hybrid recommender applies a minimax approach as it estimates an overall visitor dissatisfaction and aims its minimisation. As a result, it is able to develop optimal routes, as sequences of points of interest for each individual visitor. This hybrid recommender, still at its fine-tuning phase, has been tested in large scale simulations, using realistic data for visitors and exhibitions and has already shown to outperform the naive baseline recommender that relies on popularity.
The battle over the living room

This chapter presents the social actors taking part in the debate surrounding offensive entertainment content. It addresses the notion of media accountability in the context of entertainment content, by defining the idea of accountable popular culture, and focuses on the various ways in which it is constructed. The chapter discusses a future model for evaluating processes of constructing an accountable popular culture in a society. There are two tracks for constructing media accountability or an accountable popular culture: the liability track and the answerability track. An analysis of decisions made in both tracks concerning perceived offensive entertainment content in a society can shed light on the boundaries of its accountable popular culture. Media accountability processes can be explored in relation to news as well as entertainment content, but most studies focus on journalistic misconduct, with only a few exploring entertainment content.