Reviewerly/exHarmony huggingface dataset:
Applications of Cellular Learning Automata and Reinforcement Learning in Global Optimization

Various scientific and engineering problems can be modeled as optimization tasks. Roughly speaking, optimization problems can be categorized as static problems and dynamic problems. Among different optimization approaches, nature-inspired methods such as evolutionary algorithms have attracted considerable interest in recent years. A nature-inspired algorithm is an iterative process. Much effort has been put into the development of versatile nature-inspired optimization methods. However, it is well accepted that a general-purpose universal optimization algorithm is impossible: no strategy or scheme can outperform the others on all possible optimization problems. Besides, a single evolutionary scheme or operator may always follow similar trajectories. Therefore, it would be better to use diverse evolutionary operators to increase the chance of finding optima. Moreover, the parameter and strategy configuration of an optimization algorithm can significantly affect its performance. On a specific problem, proper configurations can result in high-quality solutions and high convergence speeds. Therefore, the utilization of multiple strategies or techniques, along with a suitable adaptation mechanism, can significantly enhance a nature-inspired optimization method. Indeed, some recently developed nature-inspired optimization methods incorporate a collection of strategies in some of their algorithmic elements such as mutation, crossover, or neighborhood structure. In this chapter, we will show how cellular learning automata and reinforcement learning can be utilized for the adaptive selection of proper strategies in nature-inspired optimization algorithms.
A Blockchain-Based Decentralized Framework for Crowdsourcing

Crowdsourcing frameworks have accomplished significant concern and reception as of late. It coordinates the human insight of specific and organizations together from everywhere throughout the world to comprehend convoluted assignments. Despite, these focal frameworks subject to the shortcomings of the trust-based standard like customary monetary establishments, for example, single purpose of disappointment, high administration expense, and security revelation. Here, the concept is a blockchain-based distributed structure for crowdsourcing, in which a requester’s work can be dictated by a horde of laborers with no depend on upon focal publicly supporting frameworks or expected clients to get to administrations with enlisting genuine characters. In consideration, the engineering of the proposed system where Users can enlist, post, or get an undertaking safely under this structure with high user’s privacy and low administrative expenses. By expanding the versatility of crowdsourcing the purpose is illustrating convoluted crowdsourcing logic with the intelligent contract. While posting a task, the requester has to deposit the project amount which is escrowed with the system and the worker will get the project amount on the agreed task completion from the escrow. Due to this task, the requester will not pay more than what task deserves, according to a guideline disclosed when the task is posted; and each worker will surely receive a payment based on the guidelines.
Improving Accuracy of Software Estimation Using Stacking Ensemble Method

In software project management, effort estimation plays an important role in prediction of time and budget values in the initial stages to help in software project planning. Software estimation will be deemed accurate if the project estimator has a good overview of the project issues to take the project to its completion within budget, effort and time and satisfying the prescribed quality. Traditional and parametric estimation measures have been mostly inaccurate due to bias and subjective estimation. Data mining and machine learning methods have proved to be effective in tackling human bias and subjectivity if the data is preprocessed properly and when the relevant features are chosen. This research paper deals with improving accuracy of software estimation using various data preprocessing techniques, feature selection and using ensemble of diverse machine learning algorithms.
Semantic Segmentation-Based Image Inpainting Detection

Image manipulation detection has evolved as a very challenging problem in the field of multimedia forensics. Object removal from the image is one of the most used manipulation operations in the image. Object removal accomplished by exemplar-based image inpainting makes image visually pleasing and physically plausible without any noticeable trace. So, detection of inpainted region has always been a challenge for researchers. In this paper, we have proposed a semantic segmentation-based approach to detect the inpainted region in an image. This is a deep learning-based approach which uses fully convolutional network. We have achieved a remarkable result in the detection of inpainted region. For training and validation of our results, we have created inpainted image database. We have evaluated our results on different performance metrics.
Challenges in Using ML for Networking Research

Leveraging innovations in Machine Learning (ML) research is of great current interest to researchers across the sciences, including networking research. However, using ML for networking poses challenging new problems that have been responsible for slowing the pace of innovation and the adoption of ML in the networking domain. Among the main problems are a well-known lack of data in general and representative data in particular, an overall inability to label data at scale, unknown data quality due to differences in data collection strategies, and data privacy issues that are unique to network data. Motivated by these challenges, we describe the design of Emerge1, a novel framework to support efforts to dEmocratize the use of ML for nEtwoRkinG rEsearch. In particular, Emerge focuses on the problem of providing a low-cost, scalable, and high-quality methodology for labeling networking data. To illustrate the benefits of Emerge, we use publicly available network measurement datasets from Caida's Ark project and create and evaluate data labels for them in a programmable fashion.
Implementation of Moodle e-learning in Mathematics

Abstract The rapid development of science and technology provides opportunities for computer experts to create attractive, interactive application programs. As the 2013 curriculum takes effect on the characteristics of student-centered learning, various electronic learning media have developed to improve the quality of education. One of the interactive media produced is e-learning based on Moodle for Mathematics. Moodle is a learning management system that is designed using web-based applications and provides online learning services. Moodle is equipped with three main elements in the learning process, there are users like teachers, users as students, and the subject matter. Together Moodle can be designed distance education and a modern and productive learning environment. Moodle can build students’ creativity in doing problem-solving, including in solving mathematical problems. This will create HOTS for students. Based on these assumptions, it is necessary to conduct a study regarding the implementation of Moodle e-learning in Mathematics. The purpose of this study is to apply Moodle e-learning to the material of linear equations. This research is Research and Development. The results showed that Moodle could improve students’ high-level thinking skills in solving mathematical problems. In conclusion, the development of Moodle e-learning can improve mathematics learning outcomes.
Exploring a knowledge-based approach to predicting NACE codes of enterprises based on web page texts

Classification of enterprises by main economic activity according to NACE codes is a challenging but important task for national statistical institutes. Since manual editing is time-consuming, we investigated the automatic prediction from dedicated website texts using a knowledge-based approach. To that end, concept features were derived from a set of domain-specific keywords. Furthermore, we compared flat classification to a specific two-level hierarchy which was based on an approach used by manual editors. We limited ourselves to Naïve Bayes and Support Vector Machines models and only used texts from the main web pages. As a first step, we trained a filter model that classifies whether websites contain information about economic activity. The resulting filtered data set was subsequently used to predict 111 NACE classes. We found that using concept features did not improve the model performance compared to a model with character n-grams, i.e. non-informative features. Neither did the two-level hierarchy improve the performance relative to a flat classification. Nonetheless, prediction of the best three NACE classes clearly improved the overall prediction performance compared to a top-one prediction. We conclude that more effort is needed in order to achieve good results with a knowledge-based approach and discuss ideas for improvement.
Evaluation of emerging educational technologies in the classroom

This virtual workshop will bring together practitioners and researchers from the fields of HCI and the learning sciences to discuss the challenges associated with the evaluation of emerging educational technologies in the classroom. In alignment with the conference's "designing for the future" theme, the aim of this workshop is to stimulate a discussion with the educational technology research community around the best practices and challenges of evaluation of such emerging educational technologies in the classroom as well as the challenges associated with moving beyond targeting numeracy and literacy to aim for the development of 21st century skills.
Upper-Bound Vibration Spectrum Computation for Membrane Response from a Few Point Measurements

This paper proposes a method to derive the upper-bound vibration spectrum of a membrane structure from a few selected point measurements. The upper-bound spectrum exceeds the measured spectra by a certain proportion. Three types of square membranes subjected to corner tension loads, namely, flat, wrinkled, and composite membranes, were tested. Full-field surface shape measurements via the grating projection method revealed that the point-to-point variation of the spectral values in each frequency has similar distribution when a vibration mode is composed of the repetition of a fundamental mode. Then, the validity of the proposed computational method was validated for the three types of membranes. This study offers a practicable approach to estimate the maximum dynamic response of square membranes under corner tension loads through point measurements at selected locations.
Convergence, continuity, recurrence and Turing completeness in dynamic epistemic logic1

Abstract The paper analyses dynamic epistemic logic from a topological perspective. The main contribution consists of a framework in which dynamic epistemic logic satisfies the requirements for being a topological dynamical system thus interfacing discrete dynamic logics with continuous mappings of dynamical systems. The setting is based on a notion of logical convergence, demonstratively equivalent with convergence in Stone topology. Presented is a flexible, parametrized family of metrics inducing the Stone topology, used as an analytical aid. We show maps induced by action model transformations continuous with respect to the Stone topology and present results on the recurrent behaviour of said maps. Among the recurrence results, we show maps induced by finite action models may have uncountably many recurrent points, even when initiated on a finite input model. Several recurrence results draws on the class of action models being Turing complete, for which the paper provides proof in the postcondition-free case. As upper bounds, it is shown that either 1 atom, 3 agents and preconditions of modal depth 18 or 1 atom, 7 agents and preconditions of modal depth 3 suffice for Turing completeness.
Real-Time Object Tracking via Adaptive Correlation Filters

Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.
Flow characteristics and velocity fields of a front opening unified pod at open door condition

Particle contamination decreases product quality in semiconductor industries. Nowadays, the size of the chips and features has significantly reduced; thus increasing the importance of moisture removal from front opening unified pods during manufacturing and transportation of semiconductor wafers. An innovative laminar air curtain and a diffuser purge device were designed at Taipei Tech. These devices are currently used in many semiconductor industries. Smoke flow visualization was used in the present study to evaluate and justify the performance of three different moisture removal methods: air curtain, conventional purge, and diffuser purge. A mini-environment and front opening unified pod model were constructed with the same size and shape as the real applications at manufacturing facilities. An innovative laser sweep technique was designed at Taipei Tech, which can provide a plane laser light sheet during the experiments. The smoke flow patterns were recorded by a digital camera. The relative humidity was monitored using relative humidity sensors that were installed on dummy wafers inside the front opening unified pod. Study of the flow patterns, velocity fields, and relative humidity data show that the combination of air curtain and diffuser purge techniques presented the highest performance, and is therefore recommended to the industry.
K-Means Clustering Algorithm for Determination of Clustering of Bangkalan Regional Development Potential

Abstract Regional potential is the ability of an area that can be developed to improve the area according to its regional characteristics. This potential can cover social, economic, infrastructure, natural resources, and human resources and their work. To improve regional development, a strategic policy is needed that leads to improving the economy of a region. In this study the regional grouping of 3 agricultural, plantation, and livestock groups in Bangkalan Regency consisted of 18 sub-districts using the K-Means Clustering method, from which the data was processed preprocessing using Scaling transformations starting from the lower limit search and the upper limit, looking for the Lower Limit and Upper Limit, then grouping using K-Means Clustering. From the research, it was found that testing in the agricultural sector with 3 clusters for the initial cluster center 5,7,15 obtained final clusters according to the highest yield criteria in cluster 1 compared to clusters 2 and 3. For testing in the plantation sector with 3 clusters for the initial cluster center 4,7,16 the final clusters were obtained according to the highest yield criteria in cluster 2 compared to clusters 1, and 3 and for testing the livestock sector with 3 clusters at the initial cluster center 3,11,13 the final clusters were obtained according to the criteria it was known that more height in cluster 2 is compared to cluster 1 and 3.
Artificial evolution based cost-reference particle filter for nonlinear state and parameter estimation in process systems with unknown noise statistics and model parameters

The cost-reference particle filter (CRPF) is a variant of the particle filter (PF). It is simpler, more robust, and more flexible than the standard PF. Particularly, it does not require any statistical information on both state noises and observation noises in its application. However, in order to successfully apply CRPF to the nonlinear state estimation in dynamic process systems, the knowledge of the model parameters should be known a priori. The standard CRPF cannot handle the problem of state and parameter estimation (SPE) with unknown noise statistics and model parameters. To eliminate the above limitation, this paper proposes an evolution algorithm for the SPE in nonlinear dynamic process systems. The algorithm is the combination of the artificial evolution (AE) with CRPF, called AE-CRPF, to estimate the states and the parameters simultaneously. The proposed AE-CRPF is applied to two nonlinear dynamic process systems for practical applications. The results demonstrate the effectiveness and robustness of the proposed method.
Context Aware Text Classification and Recommendation Model for Toxic Comments Using Logistic Regression

In recent days, the online conversations have become a vibrant platform in expressing one’s opinion about the issues prevailing in the society. Increase of threats, abuses and harassment in social websites has stopped many from expressing themselves, and they give upon seeking different opinions due to the fear of being offended and cornered. This can be advocated with the advancements of machine learning and artificial intelligence, which offers a way to possibly classify the comments based on its level of toxicity and its nature. In this following work, we propose an application that classifies the comments based on the nature of toxicity. Here, the learning model is trained with datasets containing toxic comments, and when such comments are used, the prototype identifies and highlights it. This is carried out with the most popular data classifier called logistic regression in which the prototype and feature extraction is done using sklearn’s TfidfVectorizer module. Once the toxic comments are identified by the trained application, warning flag will be raised to the user and analogous assertive statements are provided as suggestion by the recommendation engine, in which item-to-item-based collaborative filtering algorithm is used. The obtained results justifies that logistic regression affords adequate evidence in identifying the toxic comments.
Structured decision making in investigations involving digital and multimedia evidence

Practitioners in all forensic disciplines are under growing pressure to meet the rising demand for interpretation of forensic observations based on strong scientific foundations. Following principles of scientific interpretation increases the reliability and defensibility of decisions throughout an investigation, not only in the final expert testimony phase. Such formalization of decision making is particularly valuable when dealing with digital and multimedia evidence, due to the potential for information overload, inaccuracy, error and bias. To confront these challenges consistently and to reduce the risk of mistakes, this paper extends a well-established logical framework for structured decision making to all phases of a digital investigation. Using examples derived from real world cases, this work demonstrates how the principles of scientific interpretation can strengthen all phases of the investigative process.
Digital Literacy in the World of Digital Natives

Digital literacy includes use of technological communication devices and being conscious while using them and to benefit from accurate and useful information and to produce accurate and useful information in the digital world. This study is an attempt to understand the digital literacy of the digital natives who are currently undergraduate students. The objective is to try to understand how they add new media communication devices to their education and to their daily lives. A qualitative field research was conducted with 28 undergraduate students at Baskent University, Ankara. With this aim, the concept of digital literacy was examined first, and the concept of digital natives was examined second. Finally, results of the field research were analyzed. According to the results of the study, it was seen that participants of the study have digital literacy in the context of using new communication technologies. But they need guidance on which digital resources they can use in the fields in which they take education.
Protocols SMEER (Secure Multitier Energy Efficient Routing Protocol) and SCOR (Secure Elliptic curve based Chaotic key Galois Cryptography on Opportunistic Routing)

Analysis of two protocols "Secure Elliptic curve based Chaotic key Galois cryptography on Opportunistic Routing (SCOR)" and Secure Multitier Energy Efficient Routing Protocol (SMEER) are analysed. The simulation is done on the NS-2 simulation platform and finally performances are evaluated on various simulation parameters. Wireless sensor networks (WSNs), energy efficiency is thought to be an important issue, because of the constrained sensor nodes battery limit. In WSNs, routing protocols are application subordinate and outline objectives change between various applications. Secure Multi-Tier Energy Efficient Routing Protocol uses Ant Lion optimizer technique for selecting cluster head & K-means for doing clustering while this information is transmitted using Spherical grid based multi-curve Elliptical curve cryptographic routing.
Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test

This paper proposes a data-driven knowledge-based system with which aged people can measure the degree of activities of daily living (ADL) by themselves. The proposed system, called E-coach for ADL Test (EAT), provides participants with self-measurement procedures, using e-coaching, which is a guidance mechanism to lead the participants from an initial stage to a target goal. The EAT traces the behavior of the participants to gather ADL data that tell how well they perform the given e-coaching. Driven by the Internet of Things data, the knowledge-based inference of the EAT carries out the e-coaching mechanism that figures out what state the self-measurement procedures stay on and what guidance is necessary for the next state. The EAT ensures that all the procedures for ADL measurement are executed automatically without any help from medical professionals. The experiment described in this paper demonstrates that the EAT distinguishes between dementia patients and normal people. The measurement report assists medical doctors in the diagnosis of certain medical conditions that these people may have.