Reviewerly/exHarmony huggingface dataset:
A Learning Theory Rubric for Evaluating Mobile Learning Activities

Learning theories underpin the expectations of meaningful outcomes that any given learning task should have. However, educators' understanding and application of such theories is likely to vary with their own experience and context. In this article, we explore the potential value of a rubric for the design of mobile learning activities that is based on a core set of six learning theories, which we have identified from the literature as being highly relevant to the context of mobile learning. The key concepts of these theories have been used to create the evaluation rubric, which supports the analysis of learning activity design from the perspective of each of the chosen learning theories. The application of this rubric is explored from two perspectives. First, we apply it to an existing mobile learning activity to evaluate to what extent the activity embodies the theories within the rubric. Then we propose a redesigned activity by using the rubric as a guiding framework for improving the task design. This process demonstrates the potential value of applying such a rubric to designing mobile learning activities, to ensure that they adequately leverage the components of one or more relevant theories.
Group belief

Abstract While logical formalizations of group notions of knowledge such as common and distributed knowledge have received considerable attention in the literature, most approaches being based on modal logic, group notions of belief have received much less attention. In this paper we systematically study standard notions of group belief under different assumptions about the properties of belief. In particular, we map out (lack of) preservation of belief properties against different standard definitions of group belief. It turns out that what is called group belief most often is not actually belief, i.e. does not have the properties of belief. In fact, even what is called group knowledge is sometimes not actually knowledge either. For example, under the common assumption that belief has the KD45 properties, neither common belief (does not satisfy the negative introspection axiom 5) nor distributed belief (does not satisfy the consistency axiom D) are not actually belief. There has been some confusion in the literature regarding soundness of proposed axiomatizations of logics with distributed knowledge, related to the mentioned lack of preservation. In this paper we also present detailed completeness proofs of sound and complete axiomatizations of KD45 with distributed belief, both with and without common belief.
Proposed framework for Spam recognition in big data for Social Media Networks in smart environment

Social Media Networks (SMNs) are becoming more and more popular across the globe in the past few years. Netizens share all their personal information regarding day to day activities, views, and opinions across various SMNs. Simultaneously, it can be observed that throughout the most popular SMNs face frequent social spam problems in various formats. Big Data theory is gaining much more attention and it is expected that SMNs will have more interactions with each other shortly. This would enable a spam link, content or profile attack to easily move from one social network like Twitter to other social networks like Facebook. Consequently, effective discovery of spam has turn out to be a noteworthy and prevalent issue. Proposed research highlights spam discovery across several SMNs by leveraging the data of sensing analogous spam inside an OSN (Open Source Network) and using it across various SMNs. Authors have selected Twitter and Facebook as the research marks, In this research paper, the authors proposed and presented a spam detection framework to find out spam on more than one social network those are most common features in terms of contents, behavior, posts and user involvement etc. Spam detection techniques can significantly facilitate in various social network to measure the vulnerability. Based on this fact the researchers have proposed and presented a spam detection framework to find out spam on more than one social network.
Smart Contract for Monitoring and Control of Logistics Activities: Garbage Utilities Case Study in a Smart City

Logistics services involve a wide range of transport operations between distributors and clients. Currently, the large number of intermediaries are a challenge for this sector, as it makes all the processes more complicated. In this paper we propose a system that uses smart contracts to remove intermediaries and speed up logistics activities in garbage and recycling management. In addition, a MAS is used to coordinate entire logistics services, smart contracts and compliance with their terms. Our new model combines smart contracts and a MAS to improve the current logistics system by increasing organization, security and significantly improving distribution times in garbage management sector.
Group Codes

Mathematical objects in this paper are group codes. In the first part of the paper we present a survey with some of the main results about group codes, mainly the existence of group codes that are not abelian group codes, the minimal length and the minimal dimension of such codes and the existence of a non-abelian group code that has better parameters than any abelian group code. In particular, in a previous paper [1], we have shown that the minimal dimension of a group code that is not abelian group code is 4. However, all known examples of group codes of dimension 4 that are non-abelian group codes are constructed using groups that are not p-groups. We do not know if such codes exist for the case of p-groups, but in the second part of this paper we prove that, under some restrictions on the base field, all four-dimensional G-codes for an arbitrary finite p-group G are abelian.
Exploring the Effect of Using WhatsApp on Saudi Female EFL Students' Idiom-Learning

The study aimed at investigating the effect of using WhatsApp-based learning activities on developing idiom knowledge among Saudi university English as a foreign language (EFL) students compared to conventional method. It also examined the students’ perceptions towards the application of WhatsApp in idiom-learning. The rationale for conducting this study is to provide students with useful learning tools that may improve their idiomatic knowledge and also help them to practice English idioms anytime and anywhere outside the classroom. It also aimed to help teachers to streamline EFL instruction, making it more efficient, effective, and user-friendly. As such, the main research questions are what is the effect of WhatsApp-based learning activities on developing university female EFL students? and what are Saudi female EFL students’ perceptions on learning idioms using WhatsApp?. To answer these questions, 70 EFL female students in the Department of English Language and literature at Imam Mohammad ibn Saud Islamic University (IMSIU) involved in the study. They were assigned into two groups; experimental group, who was taught English idioms via WhatsApp-based learning activities, and control group, who was taught English idioms conventionally in classroom. Two instruments were used to collect the data; a pre-post idioms achievement test and a post-study questionnaire. The findings demonstrated that the experimental group significantly outperformed the control group in idioms achievement post-test. Moreover, the results showed that the majority of the experimental group had a positive perception towards learning English idioms via WhatsApp.
Link Prediction Using Multi Part Embeddings

Knowledge graph embeddings models are widely used to provide scalable and efficient link prediction for knowledge graphs. They use different techniques to model embeddings interactions, where their tensor factorisation based versions are known to provide state-of-the-art results. In recent works, developments on factorisation based knowledge graph embedding models were mostly limited to enhancing the ComplEx and the DistMult models, as they can efficiently provide predictions within linear time and space complexity. In this work, we aim to extend the works of the ComplEx and the DistMult models by proposing a new factorisation model, TriModel, which uses three part embeddings to model a combination of symmetric and asymmetric interactions between embeddings. We perform an empirical evaluation for the TriModel model compared to other tensor factorisation models on different training configurations (loss functions and regularisation terms), and we show that the TriModel model provides the state-of-the-art results in all configurations. In our experiments, we use standard benchmarking datasets (WN18, WN18RR, FB15k, FB15k-237, YAGO10) along with a new NELL based benchmarking dataset (NELL239) that we have developed.
Compositional Semantics

Abstract Many important applications in NLP fields rely on understanding more complex language units such as phrases, sentences, and documents beyond words. Therefore, compositional semantics has remained a core task in NLP. In this chapter, we first introduce various models for binary semantic composition, including additive models and multiplicative models. After that, we present various typical models for N-ary semantic composition including recurrent neural network, recursive neural network, and convolutional neural network.
Machine Learning Techniques Application

Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.
Big Data Techniques for Supporting Official Statistics

Following the advent of Big Data, statistical offices have been largely exploring the use of Internet as data source for modernizing their data collection process. Particularly, prices are collected online in several statistical institutes through a technique known as web scraping. The objective of the chapter is to discuss the challenges of web scraping for setting up a continuous data collection process, exploring and classifying the more widespread techniques and presenting how they are used in practical cases. The main technical notions behind web scraping are presented and explained in order to give also to readers with no background in IT the sufficient elements to fully comprehend scraping techniques, promoting the building of mixed skills that is at the core of the spirit of modern data science. Challenges for official statistics deriving from the use of web scraping are briefly sketched. Finally, research ideas for overcoming the limitations of current techniques are presented and discussed.
Development Mode of Internet Economy Based on Artificial Intelligence Technology

Abstract Based on the background of artificial intelligence technology, there are different situations in the mutual development of the Internet economy and artificial intelligence technology, which has a relatively large impact on the healthy development of my country’s Internet economy. Whether it is Internet economy or artificial intelligence technology, their ultimate development goal is to achieve the rapid development of our country’s economy. This article will conduct an in-depth analysis of the role of artificial intelligence technology in the development of the Internet economy and study its huge commercial value in the development of the Internet economy. This article first analyzes the relevant concepts and characteristics of artificial intelligence technology in the development of the Internet economy, briefly describes the analysis of the development of my country’s network market economy, and compares the development of artificial intelligence technology in the market Internet economy with the traditional substantive economic development model. Subsequently, it demonstrated the necessity and feasibility of implementing artificial intelligence technology for the development of the Internet economy, and conducted investigations and studies on the way that 10 Internet companies in the east coast of China used artificial intelligence technology to improve economic development. Finally, it puts forward a targeted strategy for accelerating the improvement of Internet companies in the network economy market and future development goals. The experimental research results show that the application of the Internet and artificial intelligence technology has brought us great convenience, and at the same time promoted the growth of our country’s economy, and created huge economic benefits for our country.
Adoption of the HTA Technique in the Open Source Software Development Process

The growth in the number of non-developer open source software (OSS) application users and the escalating use of these applications have led to the need and interest in developing usable OSS. OSS communities do not generally know how to apply usability techniques and are unclear about which techniques to use in each activity of the development process. The aim of our research is to adopt the HTA usability technique in the OpenOffice Writer OSS project and determine the feasibility of adapting the technique for application. To do this, we participated as volunteers in the project. We used the case study research method during technique application and participation in the community. As a result, we identified adverse conditions that were an obstacle to technique application and modified the technique to make it applicable to OSS projects. We can conclude from our experience that these changes were helpful for applying the technique, using web artifacts like forums and collaborative tools like Cacoo, although it was not easy to recruit OSS users to participate in usability technique application.
Integration of Petrophysical Log Data with Computational Intelligence for the Development of a Lithology Predictor

Abstract Wrong manual interpretation from the log data about the formation type and other important information can be catastrophic for the company-operator. With Machine-Learning (ML) (a branch of Artificial Intelligence) algorithms, the interpretation of formation type from the log data has been addressed. As a result, we have successfully developed a program able to accurately predict the type of formation. Using the conventional Machine Learning technique of splitting the data into training, validation and test sets, we tried six different ML algorithms to fit with the training part of the data and then verify their prediction accuracy with cross-validation scores and cross-validation predictions which tests the performance of the classifiers (ML algorithms) on the validation set. The three best performing classifiers were selected and further improved by a search of classifier's best hyperparameters. These improved classifiers are further tested on unseen data to produce a comparative analysis. Our prediction accuracy with Receiver Operating Characteristic (ROC) scores and ROC-Area Under-the-Curve (ROC-AUC) for each type of formation from the log data lies in the range of 95-99%, except for formations such as shaly sandstone and shale (50% and 84% respectively). The reason for this seemed to be under-fitting i.e., during the training, the classifiers did not see enough instances of these types of formation to know exactly what characteristics of the data make the type of formation to be shaly sandstone or shale. The issue of under-fitting was verified by skimming through the data. To resolve this problem, we suggest training classifiers with a larger data with more targets (types of formation). Furthermore, during the data cleaning (prior to classifier training) and data analysis phases we have discovered important relationships between well logs and defined relative importance of each well log for different formations. This observation can be investigated further to help eliminate the use of multiple well logs while dealing with some formations (based on prior geological knowledge) and reduce the cost of the well logging operations. Using our program with a larger well log data consisting of more formation type instances, we can train the classifiers to accurately predict the formation type irrespectively of differences in formation type. Our program is dynamic in the sense that with different targets, i.e., type of formation fluid instead of type of formation or both together, it can successfully predict either or both targets. Increasing the numbers of data instances resulted in a better training and thus, more accurate predictions. Utilization of the program will make the formation-evaluation process easier, faster, automated and more-precise.
Physical Cryptography

We recall a series of physical cryptography solutions and provide the reader with relevant security analyses. We mostly turn our attention to describing attack scenarios against schemes solving Yao’s millionaires’ problem, protocols for comparing information without revealing it and public key cryptosystems based on physical properties of systems.
Experimental Studies

The theoretical results presented in the previous chapters are based on experimental investigations. Therefore, in this chapter, the experimental characterisation of the short fibre-reinforced composite is presented. The performed experiments increase in complexity, starting from uniaxial tensile tests at different strain rates up to multiaxial tests like true biaxial tests and the Nakajima test which introduce stress and strain states closer to applicational load cases. At least the results of the Nakajima tests can be regarded as verification experiments for the developed and implemented models. Effects like elasto-plasticity, damage and anisotropy are investigated in detail. The inhomogeneous strain states, which can be observed already in the uniaxial tests due to localisation phenomena in combination with damage, are evaluated using a three-dimensional optical strain measurement on the surface of the specimens. The underlying principles of digital image correlation are explained in detail.
Voting system

This chapter aims to solve the issues of the digital voting process using blockchain technology. Firstly, introducing the problems in the current voting system then goes into a brief explanation of blockchain technology that how it is currently being used in the digital voting process. This chapter represents the digital voting process in detail and the issues they face. Here, we represent the blockchain-based platform to the voting system that ensures the privacy of the voter's vote. This platform also provides data immutability and transparency to the digital voting process.
Augmented Reality in Social Sciences Education

The integration of physical reality and layers of digital information in real time can be diverse (text, symbols, audio, video, and/or three-dimensional objects) with the result of enriching or altering the information of the physical reality. It is also usual in the everyday context of citizens. In other words, augmented reality has been used for years, for example, to mark an offside game in football or to alter the face in real time. Regarding the computer and mobile applications, they are those related to the AR, and it is an increasingly widespread technology in the daily environment with potential in the educational field. Educational software creators have noticed that teachers can make a didactic use of AR technology in the classroom. Beyond contributing to educate in a responsible use of AR, with an adequate didactic approach, AR implies important advantages such as improving of motivation, participation, commitment to the object of study, teamwork, and procedure improvement.
Intelligent Agents and Autonomous Robots

Over decades new technologies, algorithms and methods are evolved and proposed. We can witness a paradigm shift from typewriters to computers, mechanics to mechnotronics, physics to aerodynamics, chemistry to computational chemistry and so on. Such advancements are the result of continuing research; which is still a driving force of researchers. In the same way, the research in the field of artificial intelligence (Russell, Stuart &amp; Norvig, 2003) is major thrust area of researchers. Research in AI have coined different concepts like natural language processing, expert systems, software agents, learning, knowledge management, robotics to name a few. The objective of this chapter is to highlight the research path from software agents to robotics. This chapter begins with the introduction of software agents. The chapter further progresses with the discussion on intelligent agent, autonomous agents, autonomous robots, intelligent robots in different sections. The chapter finally concluded with the fine line between intelligent agents and autonomous robots.
A Deep Reinforcement Learning Method for Mobile Robot Path Planning in Unknown Environments

In this paper, a path planning method without global maps for mobile robots is proposed. At present, the traditional path planning methods have become mature. However, most of these methods are based on map information, and complex models need to be defined. To address these issues, we propose an end-to-end path planning model based on deep reinforcement learning, which takes the lidar sequence and depth image as the inputs, and the robot's velocity and angular velocity as the outputs. With the proposed method, the mobile robot can plan the motion path from the initial position to the target position without colliding with the obstacles in the environment. Besides, we propose a neural network that has the ability of multi-sensor information fusion from different dimensions. We build a simulation environment for mobile robot path planning in the gazebo based on Robot Operating System(ROS). Simulation results show the effectiveness of the proposed method.
Modelling, Control and Prediction using Hierarchical Fuzzy Logic Systems

Hierarchical fuzzy logic systems are increasingly applied to solve complex problems. There is a need for a structured and methodological approach for the design and development of hierarchical fuzzy logic systems. In this paper a review of a method developed by the author for design and development of hierarchical fuzzy logic systems is considered. The proposed method is based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for modelling, control and prediction. Issues related to the design and construction of hierarchical fuzzy logic systems using several applications are considered and methods for the decomposition of complex systems into hierarchical fuzzy logic systems are proposed. Decomposition and conversion of systems into hierarchical fuzzy logic systems reduces the number of fuzzy rules and improves the learning speed for such systems. Application areas considered are: the prediction of interest rate and hierarchical robotic control. The aim of this manuscript is to review and highlight the research work completed in the area of hierarchical fuzzy logic system by the author. The paper can benefit researchers interested in the application of hierarchical fuzzy logic systems in modelling, control and prediction.