Reviewerly/exHarmony huggingface dataset:
Machine Learning-based Automated Data Visualization: A Meta-feature Engineering Approach

This paper investigates an effective method to automatically visualize a given data set based on machine learning. Basically, the visualization results can be varied according to the purpose of the data analysis, and as the understanding of the data becomes larger, more various results can be obtained. This paper aims at realization of an automatic data visualization system based on machine learning, and introduces a meta-level feature engineering process to construct a visualization recommendation (classification) model. Through various experiments, we have designed various meta- feature variables to determine the significance of the visualization results in order to develop the automatic visualization system and constructed the visualization recommendation model using the meta-features. For performance evaluation, we have used three data sources including UCI ML Repository, Data.world, and R datasets, and have found that the decision tree-based recommendation model provides the best performance.
The Design of Off-grid Home Photovoltaic Power Generation System Considering Multi-weather Conditions

Abstract In the traditional fixed-installed off-grid photovoltaic power generation system, there are disadvantages such as insufficient solar energy collection and low solar energy utilization. In order to be able to effectively develop and utilize solar energy, the method of solar position tracking is generally used. In this paper, a single-chip microcomputer is selected as the main control chip, and a photovoltaic power generation system that combines two methods of photoelectric tracking and computational solar tracking and takes into account various meteorological factors is realized. The latitude and longitude information and time information collected by the single-chip microcomputer are used to calculate the sun position, and the photoelectric position is used to monitor the sun position. The design can automatically change the tracking scheme according to the weather conditions, so that the tracking system can run stably under various weather conditions.
Bringing Harmony to Computational Science Pedagogy

Inherent in a musical composition are properties that are analogous to properties and laws that exist in mathematics, physics, and psychology. It follows then that computation using musical models can provide results that are analogous to results provided by mathematical, physical, and psychological models. The audible output of a carefully constructed musical model can demonstrate properties and relationships in a way that is immediately perceptible. For this reason, the study of musical computation is a worthwhile pursuit as a pedagogical tool for computational science. Proposed in this paper is a curriculum for implementing the study of musical computation within a larger computer science or computational science program at a college or university. A benefit of such a curriculum is that it provides a way to integrate artistic endeavors into a STREAM program, while maintaining the mathematical foundations of STEM. Furthermore, the study of musical computation aligns well with the arts-related components of Human-Centered Computing. The curriculum is built on the following two hypotheses: The first hypothesis is that the cognitive, creative, and structural processes involved in both musical composition and computer programming, are similar enough that skilled computer scientists, with or without musical backgrounds, can learn to use programming languages to compose interesting, expressive, and sophisticated musical works. The second hypothesis is that the links between music, mathematics, and several branches of science are strong enough that skilled computational scientists can create musical models that are able to be designed using vocabularies of mathematics and science. While the curriculum defined in this paper focuses on musical computation, the design principles behind it may be applied to other disciplines.
Application of Long Short Term Memory to Predict Flight Delay on Commercial Flights

Delay in airline services, become an unpleasant experience for passengers who experience it. This study aims to build a model that can predict flight delay (departure) using the Long Short Term Memory method and can find out its performance. In this study there are two scenarios that have different ways of preprocessing. Both of these scenarios produce predictions with error values calculated using Root Mean Squared Error (RMSE), respectively from the first to the second scenario namely: 41, 21. Between the two, the second scenario is better than the first scenario due to extreme data deletion ( anomaly) in the second scenario with an error value using RMSE of 0.116.
Establishing social media firestorm scale via large dataset media analytics

A social media (SoMe) firestorm can present a liability for personal brands via the loss of reputation, as well as for the organisational brand image. The drastic measures often taken in these situations, especially in cases of negative media attention or a scandal, usually involve dismissal of the related persons. Hence, predicting, monitoring, analysing and measuring SoMe firestorms related to organisations or individuals can be beneficial. This paper describes SoMe firestorms and their effect, using media analysis involving opinion mining. The analysis focuses on the human trash (ihmisroska) scandal that was caused by a local centre party politician in Finland. The politician caused a SoMe firestorm by describing homeless people and substance addicts as ‘human trash’. The analysis utilises machine learning to classify 3300 media hits in the Finnish language to analyse their sentiment during the SoMe firestorm. General conclusions are drawn about the spread and influence of the SoMe firestorm to form a basis for wider global generalisation. The study formulates a scale for quantifying and analysing the influence of SoMe firestorms. The scale includes three classes relating to the exponential rise of the effect, starting from 1, with 3 being the highest. This scale aligns with the literature, which states that these events usually follow the same pattern. The case example provides further direction for the presented 1–3 level scale.
Elementary SU(2) Operations to Manipulate the Entropy in Large 2-Level Quantum Systems

Abstract SU (2) operations are recurrently required in quantum information because of their simplicity. The SU (2) formalism states that quantum systems can be controlled easily, step by step, by means of such operations if convenient vector bases (normally composed by entangled states) are used as grammar. In this work, we explore the entanglement gained under such operations for the classes GHZ and W , as two well known representatives of maximal entangled states for larger systems.
The Study of Fire Safety for Multi-Storey Container Hotel: A Case Study in Muar PPT Hotel

Abstract Recent trends show diversified usage of container that usually intended as mobile transportation case for trading industry and now applied as building materials especially for commercial building such as hotel. However, as new material, it should comply with basic fire safety requirement which spelled out in the Uniform Building By-Laws (UBBL) 1984. In the construction laws of a building in Malaysia, the characteristics of building must comply with the requirement and regulations before it is being approved in the Certificate of Completion and Compliance (CCC). Thus, this study is focusing on the container materials and its compliance of fire safety requirements and strategies for multi-storey container hotel building. Literature review and case study are used in order to get the information and to compare the ability of the materials and construction to comply with UBBL 1984 in terms of passive design. The results of the evaluation show the possibilities of Multi-Storey Container Hotel to be built and used complied with passive fire safety requirement.
Privacy awareness on cloud storage among students at tertiary level: A case study

Abstract In recent years, the usage on cloud storage has grown, including students at tertiary level. Cloud storage helps students to store and share academic materials without additional costs. However, not many studies investigate on the students’ awareness level particularly in privacy related issues. This study examined the privacy awareness on cloud storage among students at Universiti Teknologi MARA (UiTM), Kuala Terengganu. The study examined the students’ privacy awareness, through its level of agreement on the five elements which were cloud service awareness, privacy protection, risk analysis, security awareness and data availability participants. 90 respondents from three different programs were selected from the Faculty of Computer and Mathematical Sciences (FSKM). An independent t-test and ANOVA test were conducted using SPSS. The results revealed that there was no significance difference between both genders on the privacy awareness on cloud storage. While, there was a significance difference amongst the three different programs in FSKM about privacy protection. This study concluded that these Computer Science degree students were concerned and aware about the privacy on the cloud storage, but they were not aware about the privacy related issues in the cloud storage.
Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch

We present a discriminative learning algorithm for feed-forward Sum-Product Networks (SPNs) [42] and recurrent SPNs [31] based on the Extended Baum-Welch (EBW) algorithm [4]. We formulate the conditional data likelihood in the SPN framework as a rational function, and we use EBW to monotonically maximize it. We derive the algorithm for SPNs and RSPNs with both discrete and continuous variables. The experiments show that this algorithm performs better than both generative Expectation-Maximization, and discriminative gradient descent on a wide variety of applications. We also demonstrate the robustness of the algorithm in the case of missing features by comparing its performance to Support Vector Machines and Neural Networks.
Crashing to Learn, Learning to Survive: Planning in Dynamic Environments via Domain Randomization

Autonomous robots in the real world should avert collisions with pedestrians and react quickly to sudden changes in the environment. Most local planners rely on static environment maps that cannot capture such critical elements of uncertainty. Learning based methods for end-to-end navigation have become popular recently, but it is still unclear how to incorporate collision avoidance in a simple, safe and quick manner. We propose a reinforcement learning curriculum based on domain randomization to train a high performing policy entirely in simulation. The policy is first trained in a simple obstacle-free environment to quickly learn point to point navigation. The learned policy is transferred to a dynamic environment to learn collision avoidance. The key idea is to randomize the obstacle dynamics to obtain a robust planner that can be directly deployed to the real world. The resultant policy outperforms conventional planners in dynamic real world environments, even when the robot is intentionally obstructed.
NI-DBSCAN: DBSCAN under Non-IID

Abstract DBSCAN (Density Based Spatial Clustering of Application with Noise) is an example of density-based clustering algorithm. Aiming at problem that DBSCAN algorithm assumes that the data are independent and identically distributed and the traditional distance formula is difficult to accurately calculate the similarity degree between categorical data. Density Based Spatial clustering algorithm of Application with Noise under Non-IID (NI-DBSCAN) is proposed. The unsupervised clustering problem of categorical data is dealt with by means of the Non-IID (non-independent and identical distribution) thought. Using coupling similarity to measure similarity can better reflect the “real relationship” between categorical data. The experimental results on the UCI dataset show that the algorithm can obtain satisfactory clustering results and improve the applicability and accuracy of the algorithm.
Graphically Display Database Transactions to Enhance Database Forensics

Database forensics presents a set of unique challenges and these may contribute to the reason for the lack of available tools and methods for database forensics. Yet considering the pervasiveness of databases in society, the amount of sensitive data stored within these repositories, and the number of data breaches, it is perplexing why there are not more database forensic software applications. This paper contains a discussion of a design for the graphical display of database transactions from a MySQL database. Using tools such as Elasticsearch and Kibana to aggregate the log files and pre-process the data into an intelligent format facilitates the expedient analysis of volumes of database transactions. The capability to search through terabytes of data within mere seconds and visually display this data is invaluable and considered as the vital contribution of this work.
Cooperative Integrated Reading and Composition (CIRC) on Writing Achievement across Self-Efficacy

Abstract : This study was conducted to reveal the effectiveness of Cooperative Integrated Reading and Composition (CIRC) implementation on writing achievement by considering the level of students’ self-efficacy. The findings revealed that CIRC contribute a better effect on student’ writing achievement than conventional method. This also revealed that writing achievement was higher on students who categorized in high self-efficacy than those categorized in low self-efficacy. Furthermore, it was noted that self-efficacy and teaching method had no interaction. Key Words : cooperative integrated reading and composition, writing achievement, self-efficacy Abstrak : Penelitian ini dilakukan untuk mengungkap efektivitas penerapan Cooperative Integrated Reading and Composition (CIRC) pada prestasi menulis dengan mempertimbangkan tingkat efikasi diri siswa. Temuan ini mengungkapkan bahwa CIRC berkontribusi efek yang lebih baik pada prestasi menulis siswa daripada metode konvensional. Selain itu, temuan ini juga mengungkapkan bahwa prestasi menulis lebih tinggi pada siswa dengan efikasi diri tinggi daripada siswa dengan efikasi diri rendah. Lebih lanjut, penelitian ini mencatat bahwa efikasi diri dan metode pengajaran tidak memiliki interaksi. Kata kunci : cooperative integrated reading and composition, prestasi menulis, efikasi diri
On the Infinite Regress of a Speaker’s Intentions

Intention-based semantics (IBS) serves as the paradigm in the field of speaker meaning analysis. However, it has grappled with a well-known problem: the infinite regress of a speaker’s intentions. Theorists such as Grice, Schiffer, Davis, and Green have tried to remedy the situation; however, no one has found any solutions until now. The present paper claims that the inability of IBS theorists to resolve the regress problem may be attributed to the conflict between two basic assumptions that they espouse: representationalism and the transparency of speaker meaning. When both are adopted alongside each other, as the current paper shows, the regress problem immediately emerges. It follows, then, that it would be prudent to reject IBS to sufficiently analyze speaker meaning.
Method for Realizing on Line Condition Monitoring of Protective Device Based on Data of Information Processing System

Aiming at the problems existing in the traditional fault mode and impact analysis (FMEA) method in the risk assessment of relay protection devices, a fuzzy TOPSIS-FMEA method combining fuzzy evidence reasoning and TOPSIS is introduced to calculate the risk assessment of relay protection devices. . The method uses the fuzzy confidence structure to carry out the fuzzy processing of O (Occurrence), S (Severity) and D (Detection) of the three fault factors of FMEA, and reduces the subjective error of the maintenance personnel's rating in principle. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is introduced as a multi-index comprehensive evaluation sorting method to solve the problem that the traditional FMEA cannot distinguish two fault modes due to the same RPN value in the calculation process. Finally, the effectiveness of the method is proved by an example.
When does ambiguity fade away?

the prior support is finite, long-run ambiguity is known to be a possible outcome only if the learning problem is misspecified (Marinacci and Massari, 2019). We show that if the prior support is naturally rich, long-run ambiguity cannot occur.
The Hammersley–Chapman–Robbins inequality for repeatedly monitored quantum system

We derive the Hammersley–Chapman–Robbins inequality for discrete quantum parameter models in the presence of time dependent measurements. The extension determines a discrete counterpart of the classical Fisher information. We provide an illustration concerning a quantum optics problem.
Spawned Target Tracking Algorithm Based on GLMB Model in RFS Theory

This paper proposes a spawned targets tracking algorithm-STT-δ-GLMB-by extending the δ-GLMB algorithm. In the course of the research, we found that for the original δ-GLMB algorithm, performance of the spawned target tracking is seriously insufficient in the environment of the low signal-to-noise ratio. For this problem, the biggest difficulty is spawned target birth detection, and we need to model spawned target. Therefore, this paper uses the characteristics, position and velocity of the spawned target, and multiple frames of measurements to detect the spawned target. By referring to parent target state arguments, we model spawned target based on δ-GLMB. Simulation results show that under low detection rate and strong clutters, the proposed algorithm can accurately estimate spawned targets, and the performance is significantly better than the original δ-GLMB algorithm.
Studying Attention Models in Sentiment Attitude Extraction Task

In the sentiment attitude extraction task, the aim is to identify «attitudes» – sentiment relations between entities mentioned in text. In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task. For this task, we adapt attentive context encoders of two types: (I) feature-based; (II) self-based. Our experiments ( https://github.com/nicolay-r/attitude-extraction-with-attention ) with a corpus of Russian analytical texts RuSentRel illustrate that the models trained with attentive encoders outperform ones that were trained without them and achieve 1.5–5.9% increase by $$F1$$ . We also provide the analysis of attention weight distributions in dependence on the term type.
Extending BigTim Distributed Clustering Platform to Support Mobile Devices

The majority of present clustering algorithms are designed to work in a sequential manner processing offline data on a single machine. Different approaches were considered for parallelizing the existing clustering algorithms to run in a parallel manner for increasing the size of the dataset that can be processed by a computational system. This paper suggests an extended architecture of the BigTim platform such that to be capable of running clustering algorithms in a distributed manner on mobile devices. The paper presents an enhanced version of the BigTim platform, which will run clustering algorithms on a cluster of devices composed by mobile devices (mobile phones and tablets) and computers connected through a network and working together in a MapReduce manner.
A Hierarchical Modeling Method for Intelligent Satellite Application Software

This paper puts forward a hierarchical modeling method, using which developers can create an application software system with greater efficiency, accuracy and convenience, applying the theory of component software framework and layered software architecture of Intelligent Satellite (iSAT).To ensure a robust and reliable interaction among the application software, we design two diagram models and an inner behavior model to divide the software components into three layers. And a template components library is provided to improve the reusability of component models. Finally, the hierarchical modeling method and traditional manual coding method are compared in the case study of a communication signal processing unit. As a result, the hierarchical modeling method shows high feasibility and better performance in rapid development.
Rely on Negotiable Hash Algorithm of Data Exchange in the Relational Data Privacy Protection

Abstract In order to ensure that data privacy in the deal, people need to deal with the data privacy fully autonomous, it was a man of data made by a reflection, is also an effective method for individuals to take the initiative to protect privacy. At the same time in the data business, real-time data is a kind of valuable data, how to prevent the real-time data privacy, and ensure the data transaction security implementation is also an important part of the problem. So far, researchers have put forward a lot of privacy protection method; however, these models cannot effectively protect after fully autonomous data privacy, and for the privacy of the real-time release wasn’t reasonable privacy protection model is put forward.
Exploring Target Size and Visual Distance of a Responsive Eye-Controlled System

According to geometric optics, the perceived size of an object becomes smaller when the object is further from the viewer. However, smaller targets are harder to activate. This study investigates the effect of target size and visual distance on an operating eye-controlled interface. Four tasks comprising of different combinations of visual distance and target size were assessed by user. The results showed that visual distance and target size affect selection time and task load of eye-controlled interfaces operation.
Self-taught hashing using deep graph embedding for large-scale image retrieval

J. of Electronic Imaging, 29(3), 033014 (2020). https://doi.org/10.1117/1.JEI.29.3.033014 Online Publication Date: 19 June 2020 Withdrawn from Publication: 03 September 2020. This paper was retracted from the SPIE Digital Library on September 3, 2020, at the request of the authors, who found several serious errors in the experimental results due to incorrect previous experimental setup.