Reviewerly/exHarmony huggingface dataset:
Topic Detection in Sentiment Analysis of Twitter Texts for Understanding The COVID-19 Effect in Local Economic Activities

During the COVID-19 situation, discussions about the effect of COVID-19 increase on Twitter. Not only affecting the health sector, but the COVID-19 pandemic has also affected other fields, such as economic activities. Issues related to the economy become an essential discussion on Twitter because this sector has close links with other sectors in public activities. It makes twitter relevant as a knowledge extraction medium to identify users' opini comparisons. The contribution of this research is to find the effect of the COVID-19 pandemic on the comparison of sentiment and emotion in three different locations in Surabaya. Based on the results of emotion detection, at the beginning of the COVID-19 pandemic, topics related to economic activities and personal activities were dominated by anger emotion in the ITS campus and the TP mall area. Then, despite the gradual decrease in the intensity of tweets, the dominance of anger emotion tends to be stable. On economics topics, 40% of tweets in the ITS campus area and 84% of tweets in the TP mall area were dominated by anger emotion. Then 37% of tweets in the ITS campus area and 32% tweets in the Tunjungan Plaza mall area based on personal activities were dominated by anger. The economics topic is related to buying-selling and shopping activities, while personal activity is related to lifestyle and daily activities. These results indicate that during the COVID-19 pandemic, anger became the most dominant sentiment related to local economic activity from Twitter users in Surabaya.
CAVE applications: from craft manufacturing to product line engineering

Product line engineering model is suitable for engineering related software products in an efficient manner, taking advantage of their similarities while managing their differences. Our feature driven software product line (SPL) solution based on that model allows for instantiation of different CAVE products based on the set of core assets and driven by a set of common VR features with the minimal budget and time to market.
Computational Intelligence in Web Mining

The increasing demand of web applications and social network websites generates a large volume of data for online accesses. Since web data stored across different web servers and online repositories have grown rapidly, understanding user’s pattern and their content usage trends is essential for service providers. Web mining is an emerging technique in the field of computational intelligence. It is used to discover useful knowledge and insights from web data for a variety of applications such as target marketing, intrusion detection, web monitoring and recommendation, fake news analysis, etc. Web data contains heterogeneous data such as online documents, web structure data, web log, and user profile. Web content mining, web structure mining, and web usage mining are broad categories of web mining based on the type of data used in pattern extraction. This chapter describes basic functionalities of web mining and explores the state-of-the-art web mining techniques.
Blockchain Technology behind Cryptocurrency and Bitcoin for Commercial Transactions

Blockchain is a technology used as a digital data record system connected through cryptography. Meanwhile, cryptocurrency is a digital asset that is understood as a digital currency mainly based on blockchain technology. This research aims to understand how blockchain works inside cryptocurrency by conducting a systematic literature review (SLR). In future, it may the cryptocurrency will replace paper currency into digital currency. Blockchain serves to be a security system to prevent loss or duplication of data. With this new technology makes the security system especially for finance to be improved. Based on our study, it can be concluded that blockchain is the right technology for cryptocurrency in commercial transactions because it allows cryptocurrency to work without a central authority. This can reduce risk as well as transaction costs.
Android-based Decision Support System in Laptop Selection Using ELECTRE Method

Laptop is a personal computer device that has the main advantage of being easy to carry anywhere, this mobility makes laptops want to be owned by the majority. Unfortunately, not everyone understands which specifications must be considered that meet their needs when they want to purchase one. Hence this research was conducted with the aim of building an Android-based laptop recommendation system to resolve the problems. The development of this system is carried out using Elimination and Choice Expressing Reality (ELECTRE) method and the use of Java programming language. ELECTRE is a decision support method using the relation outranking model. The results of the user satisfaction test from questionnaire with the help of USE Questionnaire method show that most of the respondents give positive assessments. Likert scale on user satisfaction test obtained a result of 76,13% which indicates that the system built can help users in providing laptop recommendations.
Image Anonymization using Deep Convolutional Generative Adversarial Network

Abstract Advancement in deep learning requires significantly huge amount of data for training purpose, where protection of individual data plays a key role in data privacy and publication. Recent developments in deep learning demonstarte a huge challenge for traditionally used approch for image anonymization, such as model inversion attack, where adversary repeatedly query the model, inorder to reconstrut the original image from the anonymized image. In order to apply more protection on image anonymization, an approach is presented here to convert the input (raw) image into a new synthetic image by applying optimized noise to the latent space representation (LSR) of the original image. The synthetic image is anonymized by adding well designed noise calculated over the gradient during the learning process, where the resultant image is both realistic and immune to model inversion attack. More presicely, we extend the approach proposed by T. Kim and J. Yang, 2019 by using Deep Convolutional Generative Adversarial Network (DCGAN) in order to make the approach more efficient. Our aim is to improve the efficiency of the model by changing the loss function to achieve optimal privacy in less time and computation. Finally, the proposed approach is demonstrated using a benchmark dataset. The experimental study presents that the proposed method can efficiently convert the input image into another synthetic image which is of high quality as well as immune to model inversion attack.
Evaluating Clustering Meta-features for Classifier Recommendation

Data availability in a wide variety of domains has boosted the use of Machine Learning techniques for knowledge discovery and classification. The performance of a technique in a given classification task is significantly impacted by specific characteristics of the dataset, which makes the problem of choosing the most adequate approach a challenging one. Meta-Learning approaches, which learn from meta-features calculated from the dataset, have been successfully used to suggest the most suitable classification algorithms for specific datasets. This work proposes the adaptation of clustering measures based on internal indices for supervised problems as additional meta-features in the process of learning a recommendation system for classification tasks. The gains in performance due to Meta-Learning and the additional meta-features are investigated with experiments based on 400 datasets, representing diverse application contexts and domains. Results suggest that (i) meta-learning is a viable solution for recommending a classifier, (ii) the use of clustering features can contribute to the performance of the recommendation system, and (iii) the computational cost of Meta-Learning is substantially smaller than that of running all candidate classifiers in order to select the best.
Design of Intelligent Health Monitoring Hybrid Information System for Large Bridge Structures

Because the bridge structure health monitoring system involves multi-disciplinary knowledge, the original system has some defects such as chaotic information storage and poor monitoring precision, which seriously hinders the development of bridge structure health monitoring technology. In order to solve the above problems, the design and research of large bridge structure intelligent health monitoring hybrid information system are proposed. The hardware of the system includes the selection unit of prestressing tensioning measuring device, the selection unit of industrial control machine, the selection unit of data exchange machine and the selection unit of sensor. Through the design of the hardware unit and its software module, the intelligent health monitoring hybrid information system of large bridge structure is realized. Compared with the existing system, the experimental data show that the accuracy of bridge structure health monitoring is higher, which fully proves the effectiveness and feasibility of the design system.
Possibility of using Matlab application to propose fuzzy computer model

Abstract More and more organizations in various fields apply the principles of the Industry 4.0 philosophy. The result, among other benefits, is the acquisition of a large amount of data. Data can be of great importance to them in terms of decision support, analysis and, last but not least, as a resource for simulations and computer models. Currently, various approaches and software applications can be used to create models. One of the applications that allows the creation of computer models in various fields is Matlab. Diversity of use is ensured by different sets of tools, which are specifically focused on individual areas and thus provide the necessary tools. In the presented paper we focus on the possibilities of using fuzzy approach in the design of a computer model in the field of heating with the tools of the Fuzzy Logic Controller toolbox. The basis for creating the model will be historical data obtained from the real object. The individual tools of the toolbox, the creation and presentation of rules will be described, as well as the connection of the proposed model with the Simulink environment.
Dynamic Hunting Method for Multi-USVs Based on Improved Game Theory Model

For multi-USVs hunting problem, a muti-USVs dynamic hunting method based on improved game theory model was designed, and the traditional grid model was improved, the hunting USVs got a smoother hunting path, and the ability to avoid collisions between hunting boats and obstacles was gained. Firstly, mathematical description was made for the hunting, and a Environmental model was established, the termination conditions for hunting were designed. Combined with the actual hunting behavior, the grid strategy set of the hunting USVs was improved. After constructing the profit function for evaluating the pros and cons of the strategy, the dynamic hunting model based on improved game theory was established. The single-step optimal strategy was gained by solving the Nash equilibrium, and then expanded to get the complete optimal dynamic hunting strategy. Finally, the effectiveness of the designed dynamic hunting method was verified by simulation. The effects of different number of hunting USVs and the speed of escaper were compared and verified.
ARF: Automatic Requirements Formalisation Tool

Formal verification techniques enable the detection of complex quality issues within system specifications. However, the majority of system requirements are usually specified in natural language (NL). Manual formalisation of NL requirements is an error-prone and labour-intensive process requiring strong mathematical expertise, and can be infeasible for large numbers of requirements. Existing automatic formalisation techniques usually support heavily constrained natural language relying on requirement boilerplates or templates. In this paper, we introduce ARF: Automatic Requirements Formalisation Tool. ARF can automatically transform free-format natural language requirements into temporal logic based formal notations. This is achieved through two steps: 1) extraction of key requirement attributes into an intermediate representation (RCM: Requirement Capturing Model), and 2) transformation rules that convert requirements from the RCM format to formal notations.
Resource Assignment Strategies for Bags-of-Tasks in Distributed Systems

One of the key challenges in distributed systems is the effective and efficient resource assignment for the incoming workload. To this end, two novel task routing strategies are proposed and compared to two well-known policies, Probabilistic and Shortest Queue routing. The proposed techniques aim to provide substantially better performance than the Probabilistic policy and incur significantly less overhead than the Shortest Queue strategy. The performance of the four routing techniques is evaluated via simulation, by taking also into account the implementation effort and the routing overhead required in each case. The performance of the resource assignment strategies is examined under different cases of system load and task service time variability. The simulation results reveal that the proposed routing strategies perform significantly better than the Probabilistic policy. Furthermore, the proposed heuristics could be used in large-scale distributed systems, where the Shortest Queue strategy, even though in theory outperforms other policies, in practice does not scale well, due to the excessive amount of information it requires for each routing decision.
Detecting Corruption in Single-Bidder Auctions via Positive-Unlabelled Learning

In research and policy-making guidelines, the single-bidder rate is a commonly used proxy of corruption in public procurement used but ipso facto, this is not evidence of a corrupt auction, but an uncompetitive auction. And while an uncompetitive auction could arise due to a corrupt procurer attempting to conceal the transaction, but it could also be a result of geographic isolation, monopolist presence, or other structural factors. In this paper, we use positive-unlabelled classification to attempt to separate public procurement auctions in the Russian Federation into auctions that are probably fair and those that are suspicious.
MeLoN: Distributed Deep Learning meets the Big Data Platform

Recent advancements in Artificial Intelligence have brought “Deep Learning” frameworks to be a cornerstone for the 4th Industrial Revolution along with “Big Data” platform technologies such as Apache Hadoop. However, efficient processing of deep learning applications has become challenging as the overall sizes of data and model increase rapidly. To address this problem, we can leverage big data platforms that have successfully provided stable storage and data processing capability during the past decade. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring many interesting research issues related to profiling of expected GPU memory usages of deep learning applications, storage optimizations for deep learning processing, supporting complex deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring many interesting research issues related to profiling of expected GPU memory usages of deep learning applications, storage optimizations for deep learning processing, supporting complex deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem.
Asymptotic properties of one-layer artificial neural networks with sparse connectivity

A law of large numbers for the empirical distribution of parameters of a one-layer artificial neural network with sparse connectivity is derived for a simultaneously increasing number of both, neurons and training iterations of the stochastic gradient descent.
Interactive Multi-level Stroke Control for Neural Style Transfer

We present StyleTune, a mobile app for interactive multi-level control of neural style transfers that facilitates creative adjustments of style elements and enables high output fidelity. In contrast to current mobile neural style transfer apps, StyleTune supports users to adjust both the size and orientation of style elements, such as brushstrokes and texture patches, on a global as well as local level. To this end, we propose a novel stroke-adaptive feed-forward style transfer network, that enables control over stroke size and intensity and allows a larger range of edits than current approaches. For additional level-of-control, we propose a network-agnostic method for stroke-orientation adjustment by utilizing the rotation-variance of Convolutional Neural Networks (CNNs). To achieve high output fidelity, we further add a patch-based style transfer method that enables users to obtain output resolutions of more than 20 Megapixel (Mpix). Our approach empowers users to create many novel results that are not possible with current mobile neural style transfer apps.
Swaying Locomotion: A VR-based Locomotion System through Head Movements

Locomotion systems used in virtual reality (VR) content have a significant impact on the content user experience. One of the most important factors of a walking system in VR is whether it can provide a plausible walking sensation because it is considered directly related to the user’s sense of presence. However, joystick-based and teleportation-based locomotion systems, which are commonly used today, can hardly provide an appropriate sense of presence to a user. To solve this problem, we present Swaying Locomotion, which is a novel VR-based locomotion system that uses head movements to support a user walking in a VR space while actually sitting in real space. Our user study suggests that Swaying Locomotion provides a better walking sensation than the traditional joystick-based approach.
Automatic Construction of a Domain-specific Knowledge Graph for Chinese Patent Based on Information Extraction

Knowledge graph has been proved as an effective tool in diverse domains including patent service. However, despite partial structure of data, most patent information lies in unstructured data like abstract text, from which it is relatively difficult to build a knowledge graph, since traditional methods rely on predefined human-annotated resources of entities and their relationships. Furthermore, the difficulty in Chinese language processing worsens the problem. This paper proposed an unsupervised method to automatically construct a Chinese patent knowledge graph without pre-built dataset. This research first builds a basic graph frame using structured patent data. Then the proposed method mainly utilizes keyphrase extraction algorithm to find patent properties and semantic role labeling method to dig deeper relations. The experimental result proves the effectiveness of the proposed method.
Mobile Information System for English Teaching Based on Edge Computing and VR

There are many difficulties in using cloud computing technology to solve this problem, and edge computing technology just provides an effective way to solve this problem. Changing from two-dimensional to three-dimensional will make education more realistic. This article uses the VR technology algorithm to design and analyze the English teaching system, combined with the current environment and requirements, in the construction of teaching resources, in order to clarify the direction in practical application. Secondly, according to the scene of human behavior recognition, a human behavior data collection, processing and analysis system based on edge computing is designed and implemented. And on the basis of detailed analysis of VR technology, the theoretical basis of VR technology applied to experiment teaching is discussed, and the development of virtual experiment is analyzed concretely using systematic teaching design ideas. VR technology greatly stimulates students’ learning motivation but also significantly improves their interest in learning. In addition, the application of virtual technology has greatly improved students’ learning enthusiasm. According to the findings of this article, based on the application of virtual reality technology, students’ English comprehension skills have increased by 19%, and the degree of resource sharing has increased by 11%.
Design of Human Resource Management Information System Based on Decision Tree Algorithm

Traditional human resource management information system has poor algorithm performance in the process of information classification, and the calculation process is cumbersome, resulting in low efficiency of information classification. To solve this problem, a human resource management information system based on decision tree is designed. In the hardware design of the system, the circuit board of the system microcontroller is mainly designed, and the startup mode is changed through different step length settings to realize the power supply between the modules; in the software design, the decision tree algorithm is introduced into the system to control the information in the system. Carry out precise classification, establish a human resource scoring model based on the results of data processing in the enterprise, and design the database conceptually and logically. In the process of system performance testing, different data sets are selected to set reasonable parameters to solve the system’s classification accuracy and average number of rules for the data. The test results show that the designed system has fewer average rules and higher classification accuracy.
A Study on the Development of Evaluation Criteria for Quality Management of General Education Curriculum

This study was conducted to develop evaluation criteria to manage and improve the quality of the university's General Education curriculum. To this end, the evaluation area and evaluation criteria for the management of the quality of education were first derived through literature research. The evaluation tool obtained feasibility of feasibility verification and research results through two revised Delphi surveys (N=10). as well as through meetings with practitioners(N=7) in charge of quality management of the liberal curriculum. The results are as follows. First, this study developed a systematic evaluation criteria for the entire curriculum, such as curriculum development, support, operation, performance and improvement, rather than fragmenting the curriculum based on the CIPP evaluation model. Second, this study applied modified Delphi techniques to manage the quality of the General Education curriculum to derive a total of seven sub-items and 17 evaluation criteria. Also, the content feasibility (CVI) and inter-evaluator agreement(IRA) results developed evaluation criteria with a validity score above 0.80. Based on these findings, the university presented measures and implications for managing the quality of the liberal arts curriculum.