Reviewerly/exHarmony huggingface dataset:
An analysis of the fees and pending time correlation in Ethereum

Summary Ethereum is a new blockchain‐based platform that is also capable of running smart contracts. Despite its increasing popularity, there is a lack of studies on characterizing this system, in special the fees paid by users and the respective delay to confirm the transactions, that is, the pending time . In this sense, we study the main features of Ethereum transactions and evaluate the common belief—for blockchain systems that rely on proof of work—that users who pay higher fees will have their transactions confirmed faster. Specifically, we collect information about 7.2 million of transactions in Ethereum and correlate their pending time to several fee‐related features. Moreover, we conduct our study evaluating different ranges of values for the features, such as default and unusual values adopted by users as well as clusters of users with similar behaviors. Our empirical analysis shows strong evidence that there is no clear correlation between fees‐related features and the pending time. Overall, we conclude from our investigation that transaction's features, including gas and gas price defined by users, cannot determine the pending time of transactions.
BDI Agent Architectures: A Survey

The BDI model forms the basis of much of the research on symbolic models of agency and agent-oriented software engineering. While many variants of the basic BDI model have been proposed in the literature, there has been no systematic review of research on BDI agent architectures in over 10 years. In this paper, we survey the main approaches to each component of the BDI architecture, how these have been realised in agent programming languages, and discuss the trade-offs inherent in each approach.
Quantum circuit for the fast Fourier transform

Abstract We propose an implementation of the algorithm for the fast Fourier transform (FFT) as a quantum circuit consisting of a combination of some quantum gates. In our implementation, a data sequence is expressed by a tensor product of vector spaces. Namely, our FFT is defined as a transformation of the tensor product of quantum states. It is essentially different from the so-called quantum Fourier transform (QFT) defined to be a linear transformation of the amplitudes for the superposition of quantum states. The quantum circuit for the FFT consists of several circuits for elementary arithmetic operations such as a quantum adder, subtractor and shift operations, which are implemented as effectively as possible. Namely, our circuit does not generate any garbage bits. The advantages of our method compared to the QFT are its high versatility, and data storage efficiency in terms, for instance, of the quantum image processing.
A Novel Technique for Behavioral Analytics Using Ensemble Learning Algorithms in E-Commerce

The era of E-commerce and availability of data in every field of operations in an enormous volume that implies to Big Data is one of the biggest sources of competitive advantage for the organizations in this digital world. It provides useful information to grow businesses by posting the advertisement and help consumers find the relevant product according to their preferences. The focus of this research is on the advertisement strategies analysis under which a Business employs several online advertisement strategies in order to appeal to the consumer. This research work will present a detailed analysis in user behavioral to use for business or Online Behavioral advertising and provide the framework of how Enterprise Resource Planning systems track the targeted audience and show their content. The paper's prime objective is to classify and effectively run targeted advertising using the data that shows user's retail behavior. This is where an Enterprise Resource Planning driven data will give rise to behavioral analytics. In addition to this, various data streaming technologies are also emphasized that will help to create a pipeline for the huge amount of data in Enterprise Resource Planning's database.
Development of Knowledge Graph for University Courses Management

The task of Allocating courses to lecturers in many tertiary institutions is done manually by typing using word processor application.Motivated by the widespread application of knowledge graphs in different domains, we present automated approach based on knowledge graph to address the problem of manual course allocation to, a task usually carried out at the beginning of every semester or academic year by departments in tertiary institutions.The development of knowledge graph in a way that enables easy manipulation and automatic generation of course allocation schedule is the core contribution of this paper.Rather than storing the data in relational database tables, the system stores data in a knowledge graph which is in RDF/XML format and refer to it to support intelligent knowledge services.In addition to automatic generation of course allocation schedule, another important feature of the system proposed in this paper is its ability to enable easy implementation of tasks similar to Question Answering that are very important to education administrators, which the existing manual approach does not provide.Testing of the proposed system reveals its ability to perform effectively.Our approach of using Knowledge graph offers advantages such as flexibility and security.
Distributed Non-Communicating Multi-Robot Collision Avoidance via Map-Based Deep Reinforcement Learning

It is challenging to avoid obstacles safely and efficiently for multiple robots of different shapes in distributed and communication-free scenarios, where robots do not communicate with each other and only sense other robots’ positions and obstacles around them. Most existing multi-robot collision avoidance systems either require communication between robots or require expensive movement data of other robots, like velocities, accelerations and paths. In this paper, we propose a map-based deep reinforcement learning approach for multi-robot collision avoidance in a distributed and communication-free environment. We use the egocentric local grid map of a robot to represent the environmental information around it including its shape and observable appearances of other robots and obstacles, which can be easily generated by using multiple sensors or sensor fusion. Then we apply the distributed proximal policy optimization (DPPO) algorithm to train a convolutional neural network that directly maps three frames of egocentric local grid maps and the robot’s relative local goal positions into low-level robot control commands. Compared to other methods, the map-based approach is more robust to noisy sensor data, does not require robots’ movement data and considers sizes and shapes of related robots, which make it to be more efficient and easier to be deployed to real robots. We first train the neural network in a specified simulator of multiple mobile robots using DPPO, where a multi-stage curriculum learning strategy for multiple scenarios is used to improve the performance. Then we deploy the trained model to real robots to perform collision avoidance in their navigation without tedious parameter tuning. We evaluate the approach with multiple scenarios both in the simulator and on four differential-drive mobile robots in the real world. Both qualitative and quantitative experiments show that our approach is efficient and outperforms existing DRL-based approaches in many indicators. We also conduct ablation studies showing the positive effects of using egocentric grid maps and multi-stage curriculum learning.
STUDENTS� PERCEPTIONS OF E-LEARNING FOR ESL/EFL IN SAUDI UNIVERSITIES AT TIME OF CORONAVIRUS: A LITERATURE REVIEW

Coronavirus (COVID-19) was declared a world pandemic by the World Health Organization (WHO) on January 30, 2020. As a safety measure to protect people, most governments in the world, including Saudi Arabia, decided to close universities and workplaces. Prompted by this shut down, the researchers aimed to review the students� perceptions of e-learning for ESL/EFL in Saudi universities at this Coronavirus time. As such, this review reported on four primary studies that were found relevant to its focus during the period from January 2020 till April 2020 (Abu-Ayfah, 2020; Ahmad, 2020; Alshehri &amp; Cumming, 2020; Hakami, 2020). As a historical perspective of the review�s focus, it also reported on other four primary studies dating back to 2018 and 2019 (Alshehri, Rutter &amp; Smith, 2019; Mutambik, 2018; Oyaid &amp; Alshaya, 2019; Sharma, 2019). The review�s objectives were to report: 1) students� perceptions of e-learning for ESL/EFL, 2) benefits of e-learning for ESL/EFL, and 3) drawbacks of e-learning for ESL/EFL in Saudi universities. Moreover, this review was compared to a conference paper from China (He, 2020) as it presented a strategy to continue college English learning and teaching at Coronavirus time. Results showed students� positive views, many benefits and limited drawbacks of e-learning for ESL/EFL in Saudi universities.Keywords: e-learning; ESL/EFL; students� perceptions; benefits and drawbacks; Saudi education.
An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines

In today's age of industrialization, sensor devices installed on equipment generate a vast amount of data. One of the engineers' main jobs is utilizing these data to provide better solutions to industrial problems. This availability of extensive data partly led to the creation of predictive maintenance (PdM). In PdM, existing and previous conditions of devices are used to predict their future behavior for optimal maintenance. Most of these PdM approaches are typical time-series predictions. Machine learning tools like Recurrent Neural Networks (RNNs) are excellent tools for time-series predictions. However, most RNNs suffer from training issues due to the unstable gradient problem. Thus, networks such as the Echo State Network (ESN), were designed to solve them. The ESN solves the gradient problem by training only the output weights using simple linear regression. Despite this ease, the selection of ESN parameters and topology is a considerable design challenge. This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. Hence, in this work, we design an improved Grasshopper Optimization Algorithm (GOA) based ESN. The proposed technique uses a new solution representation with a simplified attraction and repulsion mechanisms to enhance performance. Our target application is to predict the Remaining Useful Life (RUL) of turbofan engines. The method outperforms the Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Binary PSO (BPSO), the original GOA, the classical ESN, deep ESN, and LSTM. We have provided all implemented codes and data at the GitHub repository. https://github.com/bala-221/Airplane-fault-prediction.
IoTCop: A Blockchain-Based Monitoring Framework for Detection and Isolation of Malicious Devices in Internet-of-Things Systems

Unlike conventional servers housed in a centralized and secured indoor environment (e.g., data centers), Internet-of-Things (IoT) devices such as sensor/actuator are geographically distributed and may be closely located to the physical systems where IoT devices are utilized. However, the resource-constrained nature of IoT devices limits their capacity to deploy sophisticated security solutions. The proposed approach assumes that a device can be compromised and hence, the need to be able to automatically isolate the compromised device(s). In order to enforce security policies even when devices are compromised, we propose using blockchain in the monitoring framework. Unlike existing centralized or distributed security solutions (which do not consider the possibility that the solutions themselves can be compromised), the proposed blockchain-based framework can enforce the security policies as long as a majority of the devices are not compromised. By employing the permissioned blockchain (Hyperledger Fabric) and add-on hardware modules, the proposed framework offers significantly lower latency and overhead compared to permissionless blockchain frameworks (e.g., Ethereum) and allows existing IoT devices to join the framework without modification.
Modeling an Optimized Approach for Load Balancing in Cloud

Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.
The Impact of Flipped Project-Based Learning on Self-Regulation in Higher Education

Student-centered learning approaches such as project-based learning and flipped classroom stress the active role of the learner by applying knowledge rather than absorbing knowledge, and preparing higher education students for professional development. Student-centered learning environments are more effective when students regulate their learning and learn autonomously. There-fore, the purpose of this study is to examine the impact of a proposed ap-proach of flipped project-based learning on various facets of students' self-regulated learning, including motivational beliefs and learning strategies in higher education. A flipped project-based learning environment was designed and developed to improve students’ self-regulated learning skills. In this regard, multiple case studies were conducted according to a pretest-posttest quasi-experiment design to investigate the effectiveness of the proposed approach by four groups of students from different disciplines. The study employed a mixed-method research approach for data collection. Overall, the results re-vealed that the flipped project-based learning approach significantly enhanced students’ self-regulated learning skills. It was found that the approach fostered the students’ self-regulation performance among different groups across dif-ferent disciplines and levels.&#x0D; Moreover, participants also claimed that the approach was useful and ef-fective. The findings indicated that students who actively engaged within flipped PBL activities demonstrated increases in cognitive and metacognitive functioning both individually and collaboratively. This study contributes to an advance in the understanding of how the development of SRL can be inte-grated into a flipped project-based learning environment in higher education.
A homomorphic-encryption-based vertical federated learning scheme for rick management

With continuous improvements of computing power, great progresses in algorithms and massive growth of data, artificial intelligence technologies have entered the third rapid development era. However, With the great improvements in artificial intelligence and the arrival of the era of big data, contradictions between data sharing and user data privacy have become increasingly prominent. Federated learning is a technology that can ensure the user privacy and train a better model from different data providers. In this paper, we design a vertical federated learning system for the for Bayesian machine learning with the homomorphic encryption. During the training progress, raw data are leaving locally, and encrypted model information is exchanged. The model trained by this system is comparable (up to 90%) to those models trained by a single union server under the consideration of privacy. This system can be widely used in risk control, medical, financial, education and other fields. It is of great significance to solve data islands problem and protect users? privacy.
A New Image Encryption Scheme Based on a Novel One-Dimensional Chaotic System

This article introduces a novel one-dimensional sine chaotic system (1DSCS) with large parameter interval. The evaluation of 1DSCS indicates that the system has good chaotic characteristics and large parameters space. Based on 1DSCS, a new image encryption scheme is proposed. First, the image is scrambled by the row and column indexes, and then scrambled by the dynamic parameters Arnold map. This scheme avoids the inadequacy of row-column index scrambling and the periodicity of Arnold map. Second, four highly sensitive dynamic diffusion formulas related to plaintext are designed, and the dynamic formulas are selected through the chaotic sequence generated by 1DSCS. Third, the scheme proposed in this article can also be applied to color image encryption. The experimental results demonstrate that the 1DSCS system is suitable for image encryption and the encryption scheme has good security to resist common attacks.
A Multi-Robot Coverage Path Planning Algorithm for the Environment With Multiple Land Cover Types

Many scholars have proposed different single-robot coverage path planning (SCPP) and multirobot coverage path planning (MCPP) algorithms to solve the coverage path planning (CPP) problem of robots in specific areas.However, in outdoor environments, especially in emergency search and rescue tasks, complex geographic environments reduce the task execution efficiency of robots.Existing CPP algorithms have hardly considered environmental complexity.This article proposed an MCPP algorithm considering the complex land cover types in outdoor environments to solve the related problems.The algorithm first describes the visual fields of the robots in different land cover types by constructing a hierarchical quadtree and builds the adjacent topological relations among the cells in the same and different layers in the hierarchical quadtree by defining shared neighbor direction based on Binary System.The algorithm then performs an approximately balanced task assignment to the robots considering the moving speeds in different land cover types using the azimuth trend method we proposed to ensure the convergence of the task assignment process.Finally, the algorithm improves Spanning Tree Covering (STC) algorithm to complete the CPP in the area where each robot belongs.This study used a classification image of the real outdoor environment to the verification of the algorithm.Results show that the coverage paths planned by the algorithm are reasonable and efficient and its performance has obvious advantages compare with the current mainstream MCPP algorithm.
Being Fair about the Design of FAIR Data Standards

Since 2014 the FAIR data movement has been rapidly altering the landscape of data sharing and re-use. Support for the FAIR movement has seen the evolution of disciplinary-specific standards to foster data that are “finable, accessible, interoperable and reusable.” While these exciting developments should not be minimised, it is important to interrogate how these standards are set. Key questions to ask include how representation in standard setting communities is addressed; what infrastructures and resources these emergent standards are reliant on; and how standards dictate specific interpretations of “value” and “valuable data.” Asking such questions introduces a needed reflexivity into FAIR discussions, as standard setters interrogate what data practices commit present—and future—researchers to.
Students perceptions of using mobile technologies in informal English learning during the COVID-19 epidemic: A study in Chinese rural secondary schools

During the COVID-19 quarantine period, mobile technologies have been greatly promoted to be used by students to assist their learning. Although mobile learning has been well researched, studies investigating rural school students' intentions were not enough. This study investigated rural secondary school students' mobile technology uptake and their perceptions of using mobile technologies in informal English learning during the COVID-19 epidemic. Results suggested Chinese rural school students held positive attitudes towards mobile assisted English learning, and they most tended to use smartphones, followed by portable electronic dictionaries, tablets and laptops in informal English learning. Rural secondary school students' behavioral intentions (BI) were significantly influenced by perceived usefulness (PU), facilitating conditions (FC) and attitude towards use (ATU) of mobile devices. Innovativeness (INNO) and perceived ease of use (PEU) did not significantly influence students' behavioral intentions. These factors explained 83.1% of variance of students' behavioral intentions. Based on the findings, the study offers suggestions that teachers, governments and educational policy makers take measures to pay attention to students' mobile learning in informal English learning.
Modeling Annotator Perspective and Polarized Opinions to Improve Hate Speech Detection

In this paper we propose an approach to exploit the fine-grained knowledge expressed by individual human annotators during a hate speech (HS) detection task, before the aggregation of single judgments in a gold standard dataset eliminates non-majority perspectives. We automatically divide the annotators into groups, aiming at grouping them by similar personal characteristics (ethnicity, social background, culture etc.). To serve a multi-lingual perspective, we performed classification experiments on three different Twitter datasets in English and Italian languages. We created different gold standards, one for each group, and trained a state-of-the-art deep learning model on them, showing that supervised models informed by different perspectives on the target phenomena outperform a baseline represented by models trained on fully aggregated data. Finally, we implemented an ensemble approach that combines the single perspective-aware classifiers into an inclusive model. The results show that this strategy further improves the classification performance, especially with a significant boost in the recall of HS prediction.