Reviewerly/exHarmony huggingface dataset:
Barrage Text Classification with Improved Active Learning and CNN

Traditional convolutional neural networks (CNNs) use a pooling layer to reduce the dimensionality of texts, but lose semantic information. To solve this problem, this paper proposes a convolutional neural network model based on singular value decomposition algorithm (SVD-CNN). First, an improved density-based center point clustering active learning sampling algorithm (DBC-AL) is used to obtain a high-quality training set at a low labelling cost. Second, the method uses the singular value decomposition algorithm for feature extraction and dimensionality reduction instead of a pooling layer, fuses the dimensionality reduction matrix, and completes the barrage text classification task. Finally, the partial sampling gradient descent algorithm (PSGD) is applied to optimize the model parameters, which accelerates the convergence speed of the model while ensuring stability of the model training. To verify the effectiveness of the improved algorithm, several barrage datasets were used to compare the proposed model and common text classification models. The experimental results show that the improved algorithm preserves the semantic features of the text more successfully, ensures the stability of the training process, and improves the convergence speed of the model. Further, the model’s classification performance on different barrage texts is superior to traditional algorithms.
Tracking and Separation of Smart Home Residents through Ambient Activity Sensors

To enable independent living for people in need of care and to accommodate the increasing demand of ambulant care due to demographic changes, a multitude of systems and applications that monitor activities and health-related data based on ambient sensors commonly found in smart homes have been developed. When such a system is used in a multi-person household, some form of identification or separation of residents is required. Most of these systems require permanent participation in the form of body-worn sensors or a complicated supervised learning procedure which may take hours or days to set up. To resolve this, we study several unsupervised learning approaches for the separation of activity data of multiple residents recorded with ambient, binary sensors such as light barriers and contact switches. We show how various clustering methods on data from a tracking system can, under optimal conditions, separate the activity of two residents with low error rates (&lt;2%, Rand Index of 0 . 959 ). We also show that imprecisions in the underlying tracking algorithm have a significant impact on the clustering performance and that most of these errors can be corrected by adding a single &ldquo;identifying sensor area&rdquo; into the environment. As a consequence, activity monitoring applications need to rely less on body-worn sensors, which may be forgotten or biometric sensors, which may be perceived as a violation of privacy.
Checking the Plausibility of Nutrient Data in Food Datasets Using KNIME and Big Data

As there is no standardized food database with all products available in Europe, many developers of health apps fall back on databases of communities whose quality is often insufficient. In health apps, the quality of the data sets is critical, as poor quality lowers the user's confidence. This paper examines the plausibility of nutrient data from such data sources using similarity analysis, decision support methods and Big Data technology. During a special developed process, the plausibility of the data is to be increased. Finally, the methods used will be evaluated on the basis of test data.
Design of Artificial Neural Networks to Recognize Fingerprint Patterns

Image or pattern recognition system is one of the branches in computer science, this system can help the processing of fingerprint patterns, especially in the banking, police and users of other institutions who really feel the importance of using fingerprints. Several stages in fingerprint pattern image recognition are through the process of scanning, then the resulting digital fingerprint image is converted to a certain value, among others, the threshold process, the division of images, and representation of input values. The training process is carried out using two treatments: the first with a different level of understanding and the second training with different unit numbers, the best training is obtained with a level of understanding of 0.3 and the number of hidden units 10 by producing a short training time and relatively small errors. Fingerprint pattern recognition is done by two trials, based on 1 number of training patterns and 5 number of training patterns. From the research data, the ability of the system to recognize output patterns is greater if the number of training patterns increases, with a number of 1 training patterns, the system is able to recognize 50% external patterns while the 5 system training patterns are able to recognize 70% output patterns.
Dissection of a Generative Network for Music Composition

Controlling what a neural network generates has had great success when applied in the image domain. This thesis explores the performance of similar methods but instead applied in music generation. ...
Automated Histogram-Based Seed Selection for the Segmentation of Natural Scene

Images have been widely used in today’s life varying from personal usage with Flickr, Facebook to the analysis of hyperspectral images. Availability of such huge volume of images in digital form requires an automatic analysis on visual content. The major challenge in content labeling is in segmentation, which divides the image into regions. Our work focuses on the segmentation technique that adapts based on regions in the natural images. The proposed method used automatic seed selection by analyzing the dynamic color distribution of the image. The experimental results on datasets show the better performance of our method.
Improving perceived performance of loading screens through animation

This study investigates the impact that loading animations have on perceived  performance. Two sets of usability tests were conducted to obtain the data for this study. In the first set of usabilit ...
An Interference-aware Virtual Machine Placement Strategy for Small-scale HPC Applications in Clouds

The cross-interference problem may occur when applications are executed in virtual machines placed in a same physical machine. Although many previous works have proposed several different strategies for Virtual Machine Placement, neither of them have employed a suitable method for predicting cross-interference nor have considered the minimization of the number of used physical machines at the same time. In this thesis, we define the Interference-aware Virtual Machine Placement Problem for small-scale HPC applications in Clouds (IVMPP) that tackles both problems by minimizing, at the same time, the cross-interference of small-scale HPC applications, that can share physical machines, and the number of physical machines used to allocate them. We propose a mathematical formulation and a strategy based on the Iterated Local Search framework to solve this problem. Moreover, we also propose a quantitative and multivariate model to predict interference for a set of applications allocated to the same physical machine. Experiments executed in a real scenario, by using applications from the oil and gas industry and the HPCC benchmark suite, showed that our method outperforms several heuristics from the related literature in terms of interference, while using the same number of physical machines.
Comparison of Kalman filters and LSTM networks for error reduction problem

The aim of this project is to compare the performance of LSTM and Linear Kalman Filter in the presence of a multipath noise. The Kalman filter approach is based on an intuitively defined process and dynamically changing matrices. Using the past data, the next point of the system is tried to be predicted. This filter may not represent the time correlation between data very well because it uses a user-specified dynamic model. In addition, although the Kalman filter is known to be ideal in the presence of Gaussian noise, its performance in the presence of non-Gaussian noise is not good.RNN’s are used to model time series, sequential data. LSTM is a special type of RNN with memory. LSTM aims to overcome these problems by learning noisy and dynamic models. This is an area where the Kalman filter is expected to perform poorly. Instead of testing this approach in sensor data, randomly generated 1 and 2 dimensional trajectory data were used. Although LSTM was trained with little data, it was concluded that it provided better performance than Kalman filter when appropriate hyper-parameters were provided.
Performance Comparison of Web Backend and Database: A Case Study of Node.JS, Golang and MySQL, Mongo DB

Aims: This study aims to compare the performance of Golang and node.js as web applications backend regarding response time, CPU utilization, and memory usage by using MySQL and MongoDB as databases. Background: There has been a lot of literature and research that addresses web server comparisons and database comparisons, but no one has discussed the combination of the two. Node.js and Golang (Go) were popular platforms that widely used as web and mobile application backends. While MySQL and Go are the two best open source databases that have different characters. Objective: To compare the performance of Golang and node.js as web applications backend regarding response time, CPU utilization, and memory usage by using MySQL and MongoDB as databases. Methods: In this study, we use four combinations of the web server and databases to compare, that is Node.js-Mysql, Node.js-MongoDB, Go-Mysql, and Go-MongoDB. Each database consists of 25 attributes with 1000 records. Each combination has the same routing URLs. From the previous study found a significant time difference between MySQL and MongoDB in query operations with 1000 data, so that in this study, the routing/showAll URL uses 1000 data. Results: The result shows that the combination of Go and MySQL is superior regarding CPU utilization and memory usage, while Node.js and MySQL combination is superior regarding response time. Conclusion: From this study can be concluded that the combination of Go-MySQL is superior regarding memory usage and CPU utilization, while Node.js-MySQL is superior regarding response time. Other: With this research, web developers can determine the right platform for their application; they also can reduce application developing cost so that the development process can be completed more quickly. For the next research best performance platform can be tested for WebSocket communication protocol and real-time technology, because it may provide different results from this research.
s-DRN: Stabilized Developmental Resonance Network

Online incremental clustering of sequentially incoming data without prior knowledge suffers from changing cluster numbers and tends to fall into local extrema according to given data order. To overcome these limitations, we propose a stabilized developmental resonance network (s-DRN). First, we analyze the instability of the conventional choice function during node activation process and design a scalable activation function to make clustering performance stable over all input data scales. Next, we devise three criteria for the node grouping algorithm: distance, intersection over union (IoU) and size criteria. The proposed node grouping algorithm effectively excludes unnecessary clusters from incrementally created clusters, diminishes the performance dependency on vigilance parameters and makes the clustering process robust. To verify the performance of the proposed s-DRN model, comparative studies are conducted on six real-world datasets whose statistical characteristics are distinctive. The comparative studies demonstrate the proposed s-DRN outperforms baselines in terms of stability and accuracy.
Dynamic Evaluation Model of Cloud Platform User Trust Value Based on Game Theory

Trust value mode is becoming more and more significant in computer access control systems. In this paper, we present a dynamic evaluation model of cloud platform user trust value based on game theory in order to resolve the problem of illegitimate access by user. The reward and punishment factors are set in the model, and the legal access behavior will be rewarded, the illegitimate access behavior will be punished. It will make users to choose legal access under their own interests, which will protect the system. We make an experiment in cloud platform using this model, and the result shows that the model has a good effect on the user trust value adjustment.
An Interview with Theo Breuers, Chief Executive Officer of COMPO Software, the Netherlands

This interview introduces how innovative firms are employing interesting new technologies to improve their product line up and better serve customer needs. Facial recognition and biometric identifi...
A Case Based Reasoning Coupling Multi-Criteria Decision Making with Learning and Optimization Intelligences: Application to Energy Consumption

Optimization energy is a technique helpful to manage electricity consumption of home devices according to the electric system. CBR is used to predict consumption but lacks to be generic. This paper intends to design a more generic CBR approach by relying on various intelligences. The retrieve proce
DasNet: Dynamic Adaptive Structure for Accelerating Multi-task Convolutional Neural Network

Multi-task learning solves a collection of different tasks by sharing a large encoder (common network). However, due to high computational demand for large common network, deploying multi-task learning on resource constrained devices is a challenging task. To guarantee overall accuracy with less computation, we introduce DasNet, which (1) automatically searches the adaptive common network sub-structure for each task; (2) fine-tunes corresponding decoders to adapt to the common network sub-structure. Our method can accelerate the real time inference procedure by dynamically building adaptive structure according to specific task requirements in actual scenarios. Notably, all extra memory and calculations we need for our method can be negligible. Experiments conducted on four public datasets (i.e., ILSCRC-2012, Birds200, CatVsDog, MIT67) demonstrate that our proposed method performs effectively compared with general multi-task network architecture and present related state of the art (SOTA) methods.
Exploration of Physics Concepts of Jatim Park: From Classical Physics to Digital Technology

Abstract As the common understanding, physics extend well in every part of life, include in many holiday destinations in Indonesia. In the east of Java, Jatim Park group has established by the combination of education and entertainment, produce the concept of learning while playing. However, most of the props didn’t contain the farther information about the cause or the process of certain phenomena. This particular study is part of the combination of the observation, literature study from relevant sources and authors’ knowledge in physics concept analysing. Accordingly, this paper describes the physics concept in two different locations of Jatim Park group, Jatim Park 1 and Jatim Park 3 (Infinite World). Moreover, the physics exploration captures the modernity of that destination, from classical physics to digital technology. In addition, the authors represent the standardization of the physics props on both of that park.
Improving UD processing via satellite resources for morphology

This paper presents the conversion of the reference language resources for Croatian and Slovenian morphology processing to UD morphological specifications.We show that the newly available training corpora and inflectional dictionaries improve the baseline stanfordnlp performance obtained on officially released UD datasets for lemmatization, morphology prediction and dependency parsing, illustrating the potential value of such satellite UD resources for languages with rich morphology.
SELECTION OF STUDENT RESPONSE SYSTEM FOR GAMIFICATION AT THE HIGHER EDUCATION LEVEL

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 10449-10455ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.2559Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Criminological perspectives on cybercrime: risk, routine activity, and cybercrime

Most organisations in both the private and public sectors rely heavily on the convenience brought by the Internet and computers. Inevitably, cybercrime and information security problems have lessened that convenience. This chapter examines cybercrime through the theory of the risk society and routine activity theory. It discusses how the nature of cybercrime fits into the framework of routine activity theory. Learning from the risk society and routine activity theory, it will also discuss the formation of strategies aimed at cybercrime prevention.
Personal library as a reflection of the life and work history of A.V. Vysheslavtsev

We analyze A.V. Vysheslavtsev life history and creativity, and consider the importance of his personal library for professional pursuits and artistic hobbies. It is shown that reading played an important role in the upbringing of aesthetic taste and love of the beautiful in the Vysheslavtsev family. The estate library of the family estate Volkhonshchina accumulated a huge number of publications in Russian and foreign languages. Great importance in replenishing the rare foreign books collection played A.V. Vysheslavtsev’s uncle, who bought literature in the course of his foreign trips. On the basis of biographical research, the circumstances of the appearance of “Essays with quill and pencil out of circumnavigation of the world” we reveal their artistic and scientific value. We study the reasons of A.V. Vysheslavtsev’s foreign travels, and give the review of the books bought abroad. The analysis of the thematic composition of the library shows the diversity of readers’ interests of its owners. There were books on the history of European, Byzantine and Russian art. Collected for several decades, this collection can act as one of the elements of knowledge transfer to the Russian province. On the basis of studying foreign books A.V. Vysheslavtsev built scientific activity, prepared a number of serious studies on the Western art history. The heritage of A.V. Vysheslavtsev, without a doubt, has important historical, cultural and local history significance, reveals the creative laboratory of one of the most famous art critics of Russia of the 19th century.
Enhancing website security against bots, spam and web attacks using <i>l</i>CAPTCHA

In digital technological advancement, websites are developed and set live to render service and information for users. To protect web resources from computerized programs, which create fake web login registration, Spam mails, fallacious comments in blogs, and forged online polls, CAPTCHAs are designed. For the effective functioning and security of web resources, CAPTCHAs are set for testing along with user login in WebPages. Users find text CAPTCHAs since it is simple and easy to design and implement. This paper introduces a method for creating CAPTCHAs using regional languages which are user-friendly. Without any difficulties, regional users can access location-based websites. Scrapers and automated bots may find difficulty in cracking this code. The websites can be more protected from intruders if the linguistic CAPTCHA is used. CAPTCHA (lCAPTCHA) generation and implementation process in the website, based on their region, is executed in this paper. This approach and technique provide a reliable platform for websites with high-level security and service by preventing various attacks. Analysis of various CAPTCHA techniques compared with the proposed method is done and interpretations are elucidated.
A Comparative Analysis on Performance of Minimum Spanning Tree and K-Means Clustering Based Vertical Fragmentation Algorithm

Distributed database is an emerging technology in context of database which is supposed to possess a remarkable benefits for data storage in upcoming future. One of the major distributed database design issue rely upon fragmentation of the relations. Among the fragmentation task, vertical fragmentation exhibits an entangled behavior in comparison to other due to its multi solution nature. The main objective of research highlighted in this paper is to find the best vertical fragmentation algorithm to be utilized for splitting the attributes of a database relation. Two partitioning algorithms namely Minimum Spanning Tree based vertical fragmentation algorithm and K-Means clustering based vertical fragmentation algorithm are employed to generate variety of fragments of a database relation. Further, those generated fragments are compared through partition comparator on the basis of data access cost utilized. The final outcome of the partition comparator concludes the best vertical fragmentation algorithm for utilization. As per the experiment, K-Means Clustering based fragmentation generates less data access cost in comparison to Minimum Spanning Tree based fragmentation.
Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings

In recent years, learning user preferences has received significant attention. A shortcoming of existing learning to rank work lies in that they do not take into account the multi-level hierarchies from social choice to individuals. In this paper, we propose a multi-level model which learns both the common preference or utility function over the population based on features of alternatives to-be-compared, and preferential diversity functions conditioning on user categories. Such a multi-level model, enables us to simultaneously learn a coarse-grained social preference function together with a fine-grained personalized diversity. It provides us prediction power for the choices of new users on new alternatives. The key algorithm in this paper is based on Split Linearized Bregman Iteration (SplitLBI) algorithm which generates a dynamic path from the common utility to personalized preferential diversity, at different levels of sparsity on personalization. A synchronized parallel version of SplitLBI is proposed to meet the needs of fast analysis of large-scale data. The validity of the methodology are supported by experiments with both simulated and real-world datasets such as movie and dining restaurant ratings which provides us a coarse-to-fine grained preference learning.
A Study on the Variables Affecting the Student’s Evaluation of Teaching at a University

이 연구는 전일적인 홀리스틱 연구의 관점에서 현재 대학에서 시행하고 있는 강의평가 척도를 가지고 성별, 학년, 강의규모 및 학점변수가 강의평가 점수에 미치는 영향을 알아보고 이후에 강의평가 척도 개발 타당화와 관련한 시사점을 얻기 위해 수행하였다. 연구대상은 A대학교에서 실시한 400명의 학생들의 강의평가 점수를 사용하였다. 수집된 자료는 jamovi(ver. 1.1.7) 통계 프로그램을 사용해서 변인별 기술통계, t검증, 분산분석 및 회귀분석을 수행했다. 연구 결과 성별 및 학년간의 차이는 강의평가 점수와 통계적으로 유의미하지 않은 것으로 나타났다. 또한 학생들이 받은 학점과 강의규모가 대학 강의평가 점수에 미치는 영향을 분석한 결과, 통계적으로 유의하지 않은 것으로 나타났다. 이러한 분석결과를 기초로 추후 연구에 대한 시사점을 제안하였다.This research is to find student factors affecting the student’s evaluation of teaching at university, and to provide some insights into improving the student evaluation program based on the results. To achieve this goals, this study, using the surveyed questionnaires from the students who participated in the class, attempted to analyze the influence of student’s gender, year, class size, and grade on the student evaluation of teaching. The main results of this study revealed that there is no statistical difference between gender, year, class size and student’s grade affecting the student’s evaluation of teaching. Based on the results, future research is also suggested.