Reviewerly/exHarmony huggingface dataset:
Location Rating Based on Graph Neural Network

With the rapid development of social networks, people are increasingly interested in sharing their location. Location recommendation has become an important personalization service for location-based social networks (LBSN). Location rating is one of the important tools. However, location-based social networks contain multidimensional network structures and node information. Existing approaches mostly focus on network structures that utilize one of these dimensions, which makes it difficult to efficiently aggregate information from multiple dimensions simultaneously. To overcome these difficulties, one of the recent approaches is the social recommendation based on graph neural networks (GNNs). Based on this, this paper proposes a graph neural network framework for location rating. Graph neural networks are highly inductive and can efficiently aggregate network structure and node information. In particular, the method not only aggregates homogeneous social networks composed of user and heterogeneous bipartite graphs composed of user-location, but also constructs location networks using location sequences to propose an aggregation model for location networks. Experiments are conducted on two datasets, and the results show that the method improves on the root mean square error (RMSE) and mean absolute error (MAE) metrics.
Collaborative Filtering: Comparative Study Between Matrix Factorization and Neural Network Method

With the rise of the Web and technological developments, the amount of data to use or analyse has become very large. It has therefore become difficult to know what data to look for and where to find it. This problem has contributed to the establishment of recommendation systems that allow users to access relevant resources as quickly as possible according to their preferences. Collaborative Filtering (CF) is the most well-known technique for recommendation. CF technique uses the user’ behaviour in form of user-item ratings, as their information source for prediction. This article presents a comparison study between two methods of collaborative filtering: the Singular Value Decomposition (which is considered as the most powerful matrix factorization technique to reduce dimensionality) and deep multilayer perceptron (is a class of feedforward artificial neural network, it can add the non-linear transformation to existing recommendation system approaches and interpret them into neural extensions). Both systems are evaluated on a dataset with metrics: recall at top k, NDCG@k.
Questions Generation for Reading Comprehension Using Coherence Relations

The present scenario requires a large amount of digital content in the context of forums, directories, videos, classes, etc. Questions can thus play a significant role in digital media by quizzes and questions. Producing question is the job of automatically producing questions from natural language, acting as one of the main fields of natural language human–computer interaction. We focus on generating fact seeking, questions using the knowledge base. We implemented a system that takes a reading comprehension text as input and outputs all questions for the selected domain. Our system makes the GQ process into three stages: (1) content selection: selected for question generation, (2) question formation: content transformations to get the question, (3) evaluate the quality of generated question. The framework system is implemented as an end-to-end system that expects a human to specify a topic. The resulting output is a set of questions in natural language that follows the input domain. We show the effectiveness of our approach with previous Heilman and Smith MH method.
Zero-error rule induction using a memetic algorithm

Rule induction in traditional machine learning (ML) concentrates on finding rules that minimize error to produce high accuracy through a trade-off between sensitivity and specificity. But what if the machine learning task is to produce minimal length rules with zero error (the error-free ML optimization problem)? In its most basic form, the problem can be recast as reverse-engineering the syntactically-minimal well-formed formula (wff) that correctly covers all rows of a given Boolean truth table. The main problem for evolutionary approaches to zeroerror rule induction is the design of a suitable fitness function to cope with a search landscape that is not amenable to hillclimbing. In this paper we demonstrate how using the ratio of true to false rows in the truth table can be used as a heuristic to guide a memetic algorithm for exploring and exploiting the search for zero-error, syntactically minimal wffs in randomly generated truth tables for up to 12 variabes. The search depth of candidate solutions is determined by the number of variables in the data and the size of a solution is allowed to increase as long as its coverage improves. Significant differences in solution size were found depending on the ratio of true to false values in the data. The results provide a basis for further formalization and development of zero-error rule induction if ML is to be used confidently in safety critical situations where no error in induced rules is permissible.
High quality error-tolerant phrase mining on text corpus

Phrases are widely used in many text-based expert and intelligent systems. Phrase mining is a critical and preprocessing operation for these systems. With the increase of text data, errors in text corpus widely exist. Existing approaches focus on mining phrases on clean text corpus. However, neglecting to handle these errors may generate inaccurate results, which further leads to quality decline. To address the problem, we propose an error-tolerant phrase mining method, which not only conducts phrase mining in text corpus but also correct those phrases from errors. It could help to improve the performance of text-based expert and intelligent systems. To improve the performance and scalability, we propose several efficient and effective techniques to optimize the mining process. Experimental results show that our method achieves higher performance compared with state-of-the-art methods.
Out-of-the-box Multipath TCP as a Tor Transport Protocol: Performance and Privacy Implications

The transport design of Tor - the most popular anonymization network - has been identified as a key factor responsible for its performance unfairness. In Tor, traffic from multiple users is multiplexed in a single TCP connection between two relays. While this has positive effects on privacy, it negatively influences performance and is characterized by unfairness as TCP congestion control gives all the multiplexed Tor traffic as little of the available bandwidth as it gives to every single TCP connection that competes for the same resource. To counter this, we propose to use multipath TCP (MPTCP). It allows for better resource utilization and increases throughput of the Tor traffic to a fairer extent. Our evaluation in realworld settings shows that using out-of-the-box MPTCP leads to 15% performance gain. We analyze the privacy implications of MPTCP in Tor settings and discuss potential threats and mitigation strategies.
Shadow-Heap

In the past, stack smashing attacks and buffer overflows were some of the most insidious data-dependent bugs leading to malicious code execution or other unwanted behavior in the targeted application. Since reliable mitigations such as fuzzing or static code analysis are readily available, attackers have shifted towards heap-based exploitation techniques. Therefore, robust methods are required which ensure application security even in the presence of such intrusions, but existing mitigations are not yet adequate in terms of convenience, reliability, and performance overhead.
Introducing a Long-term Course in the Disaster and Safety Field

Currently, in the disaster response process, problems such as limited government responsiveness and the lack of expertise and specialized human resources are emerging. There is an urgent need to introduce an educational course to strengthen systematic specialized education in order to cultivate the necessary job knowledge and on-site responsiveness. Therefore, this paper presents a proposal for introducing a long-term course in the field of disaster safety. The paper analyzes the current state of disaster safety management systems and specialized education and introduces long-term courses in the field of disaster safety through analysis of the government's response to the occurrence of existing large-scale disasters and analysis of questionnaires. The necessity was confirmed in the study, and an improvement plan of the current law and system for establishing the long-term course is proposed. The results showed that there is a need for education to cultivate the expertise of disaster safety management civil servants and the ability to solve problems in the field. Based on the analysis of questionnaires, the basis for promoting the introduction of long-term courses was set up, and it was possible to separately present institutional improvement plans such as securing capacity. The study may contribute to the realization of a safer society through the training of Korean-style emergency managers to develop both disaster safety management ability and the ability to solve problems.
Global Image Correlation Filter with H-D Fusion Mechanism for Visual Tracking

The discriminative correlation filter has remarkable robustness under various circumstances in the visual tracking of videos. Nevertheless, there are some challenges for the further improvement of the tracking performance, including confusion between the tracked target, complex background, and underfitting because of a lack of training samples. The main contribution of the study is developing a new framework which combines with global image sampling enable to significantly augment the negative samples without the need of corruptting those positive samples. Besides we propose a fusion mechanism by combining two image patch representations in accordance with their confidence scores. Obtained results show that the proposed method is an effective scheme in leveraging the complementary properties of deep and hand-crafted features. Based on the conducted experiments on several benchmarks such as OTB2013, TB-50, and TB-100 datasets, it is concluded that the proposed approaches can achieve better result compared to the state-ofthe-art methods.
Leander: Navigating Musical Possibility Space Through Color Data Sonification

Leander is an experimental film that sonifies color data to generate its musical soundtrack. The colors of Lake Michigan, captured in time lapse video, constitute ever-changing probability vectors that govern the behavior of musical sound-events over time. This stochastic, or probabilistic approach to data sonification imagines the musical experience as movement through a virtual possibility space, rather than the end result of a causal process. This pictorial describes how color data guides the various musical parameters at play in Leander through weighted chance.
Text classification on the Instagram caption using support vector machine

Abstract Instagram is Top 10 the most popular social networks worldwide with over 1 billion monthly active users. Instagram is especially prevalent in the United States, India, and Brazil, which have over 130 million, 100, and 91 million Instagram users each. And the fact that so many people use Instagram, Social Media Marketing is also one of the goals of people using social media as a place to market their products. To help people to find out what’s trending on Instagram, this paper using text classification to categorizing Instagram caption into organized groups (fashion, food &amp; beverage, technology, health &amp; beauty, lifestyle &amp; travel). In this paper, we are testing the Support Vector Machine algorithm to classify the trending on Instagram using 66171 captions. The study used the TFIDF method (Term Frequency times Inverse Document Frequency) measure and used variations of percentage for splitting data. The results showed that the use of SVM with a percentage ratio of 70:30 resulted in higher accuracy compared to others.
Retraction Note: Impact of the learning set’s size

An Erratum to this paper has been published: https://doi.org/10.1007/s10639-020-10422-x
Cloud Computing Big Data Adoption Impacts on Teaching and Learning in Higher Education

There is a rapid evolution in the purpose and value of higher education brought about by technological advancement and data ubiquity. Data mining and advanced predictive analytics are increasingly being used in higher education institutions around the world to perform tasks, ranging from student recruitment, enrolment, predicting student behaviour, and developing personalised learning schemes. This chapter evaluates and assesses the impact of big data and cloud computing in higher education. The authors adopt systematic literature research approach that employs qualitative content analysis to establish their position with regards to the impact, benefits, challenges, and opportunities of integrating big data and cloud computing to facilitate teaching and learning.
Modulation Characteristics of Coherent Motion Field for the Moving Vehicles

The method based on the coherent motion field is an important idea to achieve the rapid vehicular collision detection. In this paper, a novel model of coherent motion field is established by using the modulation concept of communication principle. The ideal coherent motion field can be achieved by modulating the amplitude and position of the generalized bell-type difference function with the speed field from Farneback's method. We propose the area difference to characterize the symmetry of positive and negative areas of coherent motion field, and the area difference stably presents the modulation characteristics of normal frames and collision frames, which is of great significance to the rapid detection of the traffic accidents in a wide range of roads.
Scalable Feature Matching Across Large Data Collections

This article is concerned with matching feature vectors in a one-to-one fashion across large collections of datasets. Formulating this task as a multidimensional assignment problem with decomposable costs (MDADC), we develop fast algorithms with time complexity roughly linear in the number n of datasets and space complexity a small fraction of the data size. These remarkable properties hinge on using the squared Euclidean distance as dissimilarity function, which can reduce (n2) matching problems between pairs of datasets to n problems and enable calculating assignment costs on the fly. To our knowledge, no other method applicable to the MDADC possesses these linear scaling and low-storage properties necessary to large-scale applications. In numerical experiments, the novel algorithms outperform competing methods and show excellent computational and optimization performances. An application of feature matching to a large neuroimaging database is presented. The algorithms of this article are implemented in the R package matchFeat available at github.com/ddegras/matchFeat. Supplementary materials for this article are available online.
Random Forest Parameterization for Earthquake Catalog Generation

An earthquake is the vibration pattern of the Earth’s crust induced by the sliding of geological faults. They are usually recorded for later studies. However, strong earthquakes are rare, small-magnitude events may pass unnoticed and monitoring networks are limited in number and efficiency. Thus, earthquake catalog are incomplete and scarce, and researchers have developed simulators of such catalogs. In this work, we start from synthetic catalogs generated with the TREMOL-3D software. TREMOL-3D is a stochastic-based method to produce earthquake catalogs with different statistical patterns, depending on certain input parameters that mimics physical parameters. When an appropriate set of parameters are used, TREMOL-3D could generate synthetic catalogs with similar statistical properties observed in real catalogs. However, because of the size of the parameter space, a manual searching becomes unbearable. Therefore, aiming at increasing the efficiency of the parameter search, we here implement a Machine Learning approach based on Random Forest classification, for an automatic parameter screening. It has been implemented using the machine learning Python’s library Sci-Kit Learn.
Spatial Distribution and Influencing Factors of Stroke in China

To understand the current status of stroke in Chinese people over 45 years old in 2011, 2013 and 2015, explore the spatial distribution rules and influencing factors related to stroke, and provide basis for the prevention and control of stroke in middle-aged and elderly people. Spatial autocorrelation and hot spot analysis were performed by ArcGIS 10.6 software. SPSS 20.0 software was used for univariate analysis and binary logistic regression analysis. The results showed that there were significant differences in the geographical distribution of stroke, with the highest incidence in northeast, central and southern regions. People with high blood pressure and diabetes are at higher risk as they get older. At the same time, the strength of the individual constitution will also cause the change of disease.
Generation of Alternative Clusterings Using Multi-objective Particle Swarm Optimization

Unsupervised nature of clustering algorithms makes it hard to discover the complex hidden patterns in the dataset. Conventional clustering methods discover only single clustering while complex patterns may have more than one pattern. These multiple patterns or alternative clustering can be valuable and interesting from the point of view of knowledge discovery. Alternative clustering offers various use cases from finance to environmental science and psychology to climatology. A multiple objective-based particle swarm optimization (PSO) approach is introduced for generating alternative clusterings. In multiple objectives, two objectives are being used: cluster quality within-cluster and dissimilarity between clusters. These objectives formulate minimization and maximization type of objective functions. Particle swarm optimization optimizes these objective functions efficiently and finds a wide scatter of solutions with good convergence to true Pareto optimal solutions. The obtained experimental outcomes indicate that the proposed approach is able to generate alternative clustering with a good amount of accuracy.
Pengaruh Motivasi Belajar, Fasilitas Belajar dan Kedisiplinan Belajar Terhadap Prestasi Belajar MIPA di SMA Negeri 1 Karangdowo

Objectives Specifically, this study has the following objectives. (1) To find out the influence of learning facilities at home on the learning achievement of Mathematics and Natural Sciences in Karangdowo 1 High School students. (2) To find out the effect of learning motivation on Mathematics and Natural Sciences learning achievement in students at Karangdowo 1 High School. (3) To determine the effect of learning discipline on the learning achievement of Mathematics and Natural Sciences in Karangdowo 1 High School students.The results showed, (1) There was a joint effect between learning facilities at home, MIPA learning motivation and joint learning discipline influenced the MIPA learning achievement of XIP MIPA students in SMA Negeri 1 Karangdowo Klaten Regency, so that the research hypothesis was accepted . (2) Learning facilities at home have a positive and significant effect on the learning achievement of Mathematics and Natural Sciences students of class XI MIPA in SMA Negeri 1 Karangdowo, Klaten Regency. (3) Mathematics and Natural Sciences learning motivation has a positive and significant effect on Mathematics and Natural Sciences learning achievement of the 11th grade students of Mathematics and Natural Sciences in SMA Negeri 1 Karangdowo, Klaten Regency, so that the research hypothesis is accepted. (4) Discipline of learning had a positive and significant effect on the learning achievement of MIPA students of XI MIPA grade students in SMA Negeri Karangdowo Klaten Regency. (5) Mathematics learning motivation is the most influential variable on the learning achievement of Mathematics and Natural Sciences students of class XI MIPA in SMA Negeri 1 Karangdowo, Klaten Regency.
Analysis of Learning Model for Improvement of Software Education in Korea

The importance of domestic software education is emphasized and the implementation of mandatory education is gradually spreading. Therefore, in this paper, the overall current state of software education in South Korea and five examples of teaching-learning methods that are currently applied were analyzed and problems in the teaching-learning methods were presented. Unstructured data were collected mainly from domestic software education related web sites to extract elements necessary in software education thought by teachers and learners, and based on the result, improvement points of five learning models that are currently used in South Korea were derived.
Integrated Joint Probabilistic Data Association and Interactive Multiple Model Filter

In this paper we develop the integrated Joint Probabilistic Data Association (JPDA) and Interactive Multiple Model (IMM) Filter. This combined filter is developed for applications where the targets can change their dynamical behavior and the measurements have association ambiguity. The potential applications involve airplane tracking, satellite tracking, vehicle and pedestrian tracking for autonomous vehicles. We provide the complete derivation with intuitive insights and the algorithmic implementation details. Further, to improve accuracy, the filters are developed to use sigma or quadrature points. Finally, multi-target tracking examples are used to illustrate the working and efficacy of the integrated JPDA and IMM filter. An example of tracking airplanes with measurement association ambiguity is used to demonstrate the combined filter using the Extended Kalman filter, Unscented Kalman Filter and the recently developed Conjugate Unscented Transform points.
Checklist as an Effective Means of Information Delivery in On-Demand Learning

On-demand classes have become popular in the last decade due to their convenience and asynchronous nature of teaching. However, due to the lack of precise communication and effective information delivery, students need to put a significant effort to follow and comprehend on-demand lectures. In this paper, we investigate the practicability of using checklists to improve information delivery in on-demand learning. A case study is conducted on six on-demand lectures covering three lecture topics. We assess the report writings of over 141 student participants, divided into experimental and controlled groups. Natural Language Processing (NLP) and part-of-speech segmentation are used to analyse survey responses. The findings indicate a statistically significant difference in performance between instructions delivered through a checklist and simple text. At an average, 18% of the students avoid making mistakes when a checklist is used as the mode of instruction delivery. Our pilot study proves checklists' potential as a feasible and effective tool for information delivery in on-demand education.