Reviewerly/exHarmony huggingface dataset:
Digital Literacy in Compensating Children’s Learning Difficulties

Improving digital literacy can help compensate for children's or student learning difficulties.Today's students have become adults in the era of digital media.However, the ability of all social media in IT wealth does not necessarily mean that a child is declared digital literate.That way, children have technological skills to meet the digital demands needed to succeed in school and in life.This article is the result of a study of the extent to which digital literacy acts as a compensation for learning difficulties for children.Besides that, the correlation between digital literacy owned by parents also greatly influences the success rate of family goals in achieving education for their children.
Speed Optimization of the AES Algorithm Using Pipeline Hardware Architecture

Secure encryption algorithms with advanced key executive techniques constantly help to achieve privacy, verification, and security of the data and curtail the overheads of the system. Currently, the prominent cryptographic technique is the Advance Encryption Standard (AES). The 128-bit pipelined cipher AES components adopt the symmetric-block cipher algorithm for encryption of the data. Our application achieves a high-level of encryption of 25.6 Gbps with an effective inter-and-intra-round layout. This module is designed on Xilinx ISE® Design Suite 14.7 and optimized for faster conversion speeds as the module is based on the pipeline architecture to perform the repeated array of operations known as the round. The designed module is suitable for high-security data communication, image processing, and other embedded applications. Pipelined architecture reduces the time associated with each encryption process and decreases the total time it takes for a plaintext block to encrypt.
A distributed business process fragmentation method based on community discovery

For deploying a traditional centralized business process to distributed systems, how to properly partition the process is a key issue. If closely related sub-process fragments are assigned into many different servers, the frequent interactions of the sub-processes will result in large server communication costs. However, most of the current works in business process fragmentation focus on optimizing load balancing to achieve high execution performance, without considering the intrinsic functional aggregation of partitioned sub-processes. This paper proposes a novel distributed business process fragmentation method based on community discovery, which aggregates functional related process partitions together to optimize server communication costs during business process execution. An advanced fuzzy models (AFM) and control-data flow model which are more suitable for community discovery are presented. And the community-based process fragmentation algorithm (CPFA) is developed to get an optimized business process distributed deployment schema, reducing communications between servers and data access communication costs. We evaluate our method with BPI dataset, and demonstrate that it significantly reduces communication costs between servers for process execution efficiency while also maintain good server resource load balancing.
Heuristic Coordination for Multi-agent Motion Planning

Fuzzy logic and potential field methods are competent techniques for autonomous mobile robot navigation. In this paper, a conventional potential field-based scheme for multi-agent motion planning has been presented along with a genetic-fuzzy-based navigation scheme for off-line performance comparison. They are used to generate decision against collision in the best possible manner. The potential field method (PFM) relies on its potential function and GA-Fuzzy works based on its knowledge base. Initially, the number of robots is taken as eight; later on, it is increased to 12. The necessity of the coordination scheme is more for a larger number of robots. Finally, the performances of those two approaches are compared to solve 100 test cases. GA-Fuzzy-based motion planner has shown adaptive in comparison to the PFM.
Decision Support Framework for the Adoption of Software Development Methodologies

There are many software development methodologies that are used to control the process of developing a software system. No exact system was found which could help the software engineers for selection of best software development methodology. This paper presents s Decision Support Framework, which will assist in determining the best methodology to apply in a software project. With the help of Likert scale we created a rule based model which will help the software engineers to select an appropriate development methodology that may increase the probability of system success.
Human Tracking of Single Laser Range Finder Using Features Extracted by Deep Learning

Human recognition using single laser range finder (LRF) is utilized for the task of following a target person such as a cargo transport robot. In these recognition methods, the approach is applied in which human-crafted features is inputted to the one-class classification model to identify whether it is a human or not. In this paper, we propose a method that introduce features extracted by deep learning. In this method, we create an encoder that can extract features from input data using PointNet-based autoencoder. In its experiment, the features extracted by encoder is compared with the human-crafted features, and these extraction process length of time is measured.
A Privacy-Preserving Friend Recommendation Mechanism for Online Social Networks

Friend recommendation systems is widely applied in the context of online social networks (OSNs). Such systems aim to expand users' social network to increase users' engagement with related OSNs. However, during the cold-start stage, in which situation no sufficient information could be used to provide recommendation to new users, social relationships among existing users might be disclosed. As existing users' social relationship might be used to provide recommendation for new users. In this paper, we solve such privacy problem by applying a privacy-preserving friend recommendation mechanism. The novelty of this mechanism lies in its combination of deep learning and differential privacy method. The balance of privacy preservation and social recommendation is achieved by introducing node2vec to generate users' latent features, then performing the information fusion with Heterogeneous Information Networks (HIN), and finally using deep neural network (DNN) which accords with differential privacy to make privacy-preserving recommendations. The mechanism is experimented on a real dataset Higgs Twitter Dataset. The result shows that our method can achieve certain balance to obtain good effect of recommendation without disclosing users' privacy.
Large-Scale Data Clustering Using Improved Artificial Bee Colony Algorithm

Clustering is grouping the similar data points in the clusters. Large-scale data grouping has discovered wide applications in many fields, particularly in big data analytics. Traditional clustering algorithms do not explore and exploit all feasible solutions of clustering. Artificial Bee Colony algorithm (ABC) is a metaheuristic algorithm applied for clustering. ABC suffers from slow convergence. Hence, Improved ABC (IABC) is used for experimentation. UCI datasets—wine and seed are modified to large scale and used for experimentation. Experimental results show that IABC give the quality clusters than ABC and K-mean for large-scale dataset. ABC gives the better clusters than K-mean.
Recommender Systems on Homomorphically Encrypted Databases for Enhanced User Privacy

Detailed customer statistics play an important role for merchants to distribute targeted advertising and derive sales statistics. For that, merchants are strongly interested in correlations between product groups to generate proper recommendation models. Systems that collect customer data are usually profile-based and pose a threat to customer privacy. For further improving their advertising cooperating merchants can share their customer profiles. We propose to employ homomorphic encryption to ensure all customer profile data remains protected without revealing the merchants operational secrets. Only a set of predefined operations can be performed on aggregated records of these encrypted data to calculate the desired correlations and still protect privacy. We perform an evaluation on the efficiency and feasibility of our prototypical implementation using simulated data. Our experiments show, that such a system can be implemented using commercial off-the-shelf hardware and software with negligible additional effort.
Multimodal Data Security Framework Using Steganography Approaches

Information or data is a very crucial resource. Hence securing the information becomes a critical task. Transfer and Communication mediums via which we send this information do not provide data security natively. Therefore, methods for data security have to be devised to protect the information from third party and unauthorized users. Information hiding strategies like steganography provide techniques for data encryption so that the unauthorized users cannot read it. This work is aimed at creating a novel method of Augmented Reality Steganography (ARSteg). ARSteg uses cloud for image and key storage that does not alter any attributes of an image such as size and colour scheme. Unlike, traditional algorithms such as Least Significant Bit (LSB) which changes the attributes of images, our approach uses well established encryption algorithm such as Advanced Encryption Standard (AES) for encryption and decryption. This system is further secured by many alternative means such as honey potting, tracking and heuristic intrusion detection that ensure that the transmitted messages are completely secure and no intrusions are allowed. The intrusions are prevented by detecting them immediately and neutralizing them.
Probability Modals and Infinite Domains

Recent years have witnessed a proliferation of attempts to apply the mathematical theory of probability to the semantics of natural language probability talk. These sorts of “probabilistic” semantics are often motivated by their ability to explain intuitions about inferences involving “likely” and “probably”—intuitions that Angelika Kratzer’s canonical semantics fails to accommodate through a semantics based solely on an ordering of worlds and a qualitative ranking of propositions. However, recent work by Wesley Holliday and Thomas Icard has been widely thought to undercut this motivation: they present a world-ordering semantics that yields essentially the same logic as probabilistic semantics. In this paper, I argue that the challenge remains: defenders of world-ordering semantics have yet to offer a plausible semantics that captures the logic of comparative likelihood. Holliday & Icard’s semantics yields an adequate logic only if models are restricted to Noetherian pre-orders. But I argue that the Noetherian restriction faces problems in cases involving infinitely large domains of epistemic possibilities. As a result, probabilistic semantics remains the better explanation of the data.
Investigating disciplinary differences in the use of electronic journals by academics in Zimbabwean state universities

The study investigated disciplinary differences in the use of electronic journals by academics at three state universities in Zimbabwe. The study employed a quantitative approach using a survey research design. Data were collected through structured questionnaires that were administered to a sample of 363 academics. The sample drew from all disciplines at the targeted institutions. The response rate was 58.4%. Findings showed that there were disciplinary differences in awareness, use, and possession of technological skills needed to utilise electronic journals. Academics from agriculture and environmental science, engineering, medicine, and science and technology were more aware of the resources, used them more, and possessed better technological skills than their counterparts from arts and humanities, social sciences, and commerce. The study provides important insight to university librarians regarding the policies, practices, and strategies of promoting full use of electronic journals to academics rooted in different disciplines that respond differently to the resources.
An Automated Measurement System for Modeling and Characterization of NFC Communication Systems

In this paper, we present a new automated measurement system for modeling and characterization of NFC communication systems. The increasing effort of interoperability testing for different NFC ecosystems, caused by the steep marked ramp up of the NFC technology, raised the need of modeling and characterization of such communication systems. We develop a configurable test device to model different NFC transponder properties regarding the return link behavior and integrate it into an automated measurement system. We perform measurements on real world products and show how the results can be used to decrease the effort for interoperability testing and reduce the overall development time for new NFC-enabled devices.
Social media data analytics using feature engineering

In this fast-growing digital world, social media analytics is gaining attention in the field of big data. Big data is the collection of huge amounts of raw digital data that is difficult to analyze with conventional analysis methods. Due to the popularity of social media sites such as Twitter and Facebook, a vast amount of public unstructured data is generated by millions of users every day. This raw bulk data cannot be directly used in decision making and prediction tasks. Therefore many researchers have been working on converting this huge unstructured information into meaningful information through big data analytics. Developing an efficient data analytics tool is essential to understand the multimodal social media data that improves the performance of decision-making and prediction systems. This chapter overviews the strengths of the current state-of-the-art feature engineering approaches that have been developed to represent the characteristics of social media data. In addition, this chapter outlines the proposed framework for analyzing the social media data including the shared images, tags, associated comments, and its social relationship.
Hybrid Robotic Reinforcement Learning for Inspection/Correction Tasks

The ability to rapidly program robots for complex tasks is an important precursor to wider adoption of robotics in industry. Robot programming is often time consuming and brittle to unanticipated variations in processing. Automated robot task learning is a solution to this problem. Reinforcement Learning (RL) is a commonly used approach for a robot to autonomously learn simple tasks. In RL, rewards are used to guide the robot towards learning an optimal plan or control policy. RL, however, has proven to be of limited value for problems with large-state spaces and considerable environmental variability. In this paper, we investigate formulation of the RL approach for inspect/correct types of tasks, specifically a misplaced block in a simple grid-world environment (requiring searching the gird world to identify a missing block and returning the missing block back to the target). We use a hybrid method, combining the SARSA algorithm and a model of the environment. The model of the environment is used as a reference model to reduce the state space, avoiding unnecessary exploration of the environment. A main focus of this research is the impact of task variability on RL performance.
Proposition of the recommendation system for the author based on similarity degrees

At present, there is a rapid increase of educational resources in various learning stands. The lesson producer is a fundamental as well as a responsible actor in the creation and the outline of educational resources. First, due to the cost caused by the process of producing new educational resources, and because of the fullness within these lasts. The author is invited to evade all sorts of vain duplications, and so take advantage and join the efforts. Our aim is to suggest a reference system to the author profile during the lesson creation, which may require the production of several educational resources to respond to general and specific objectives. Therefore, we have previously created a mode or a representation for the resource sought or the creation's object by the author throughout a global ontology. In this paper, we hope for the launch of research based on the criteria drawn from the ontology and the mensuration techniques depending on the similarity degrees. We will bound this paper to two recommendations criteria that are; educational objectives and tags. Indeed, each educational resource contains, not only but an educational objective and at least a tag alone from its creation environment (Open Educational Resource, MOOC or e-learning). During the lesson creation, the author is beholden to identifying, above all the domain assigned, the specific objective, few tags and annotations or keywords; these elements are going to serve our ontology. Based the measures' techniques of similarity amidst the author and the bank's content of utilization cases, the recommendation system has to place a result sorted through a descending similarity degree. The author requires the possibility of updating the results by interposing under an administrator's supervision a weighting, an indexation or a memorization related to each resource. For this reason, we firstly are going to limit our learning environment to; OER MOOC and e-learning. Then, we will limit the research domain to the Artificial Intelligence. Afterwards, we are going to perform researches on resources acknowledging the concept in question. Lastly, we will proceed to a comparative study amongst these studies in order to be able to choose the convenient technique to our work context. The first section consists of presenting the adapted method for the extraction educational resources. Thereafter, we are going to move towards the enhancement of our ontology from the identified extraction. Finally, we are going to prioritize the criteria according to our needs and present some measures techniques and choose one to adopt for our context.
Secure Cryptography Infrastructures in the Cloud

Information systems are deployed in clouds as virtual machines (VMs) for better agility, elasticity and reliability. It is necessary to safekeep their cryptographic keys, e.g., the private keys used in TLS and SSH, against various attacks. However, existing virtualization solutions do not improve the cryptography facilities of in-cloud systems. This paper presents SECRIN, a secure cryptography infrastructure for VMs in the cloud. SECRIN is composed of a) virtual cryptographic devices implemented in VM monitors (VMMs), and b) a device management tool integrated in the virtualization management system. A virtual device receives requests from VMs, computes with cryptographic keys within the VMM and returns results. The keys appear only in the VMM's memory space, so that they are kept secret even if the VMs were compromised. With the management tool, the operator of virtualization management systems assigns virtual cryptographic devices to a VM as well as other resources, while the tenant (or owner) of a VM still holds proper controls on the keys. The virtual devices work compatibly with live migration, and the cryptographic computations are not interrupted when the VMs are moving from a host to another. We develop the SECRIN prototype with KVM-QEMU and oVirt. Experimental results show that, it works compatibly with existing virtualization solutions, provides reliable cryptographic computing services for applications, and is secure against attacks happening in VMs.
A Levenberg-Marquardt Algorithm Based Incremental Scheme for Complex-Valued Neural Networks

In this paper, an efficient learning algorithm is proposed for complex-valued neural networks. It can well tune the weights and bias by complex-valued Levenberg-Marquardt (LM) algorithm and optimize the network structure by an incremental mechanism. Firstly, compared with the first-order algorithm, such as complex-valued gradient descent algorithm, complex-valued LM algorithm overcomes the problem of slow convergence for training complex-valued neural networks. Secondly, for practical applications, the choice of optimization algorithms is over-emphasized, while the effect of the network structure on performance is ignored. Indeed, the complexity of the structure is crucial to the generalization capability. Therefore, in order to improve the performance of complex-valued neural networks, an incremental mechanism is adopted to determine the network structure during the training process. Experimental results show the effectiveness of the proposed algorithm.
Towards a patient rule induction method based classifier

This paper is an overview of the bump hunting algorithm and in particular the Patient Rule Induction Method and its applications. We also give an overview about interpretability in several key supervised data mining algorithms. This allows for exploring the potential for using PRIM, with its interpretation capability, as a core technology towards building a highly accurate and interpretable classifier in a mixed data space.
Autonomic Workload Change Classification and Prediction for Big Data Workloads

The big data software stack based on Apache Spark and Hadoop has become mission critical in many enterprises. Performance of Spark and Hadoop jobs depends on a large number of configuration settings. The manual tuning procedure is expensive and brittle. There have been efforts to develop online and off-line automatic tuning approaches to make the big data stack more autonomic, but many researchers noted that it is important to tune only when truly necessary because many parameter searches can reduce rather than enhance performance. Autonomic systems need to be able to accurately detect important changes in workload characteristics, predict future workload characteristics, and use this information to pro-actively optimise resource allocation and frequency of parameter searches. This paper presents the first study focusing on workload change detection, change classification and workload forecasting in big data workloads. We demonstrate 99% accuracy for workload change detection, 90% accuracy for workload and workload transition classification, and up to 96% accuracy for future workload type prediction on Spark and Hadoop job flows simulated using popular big data benchmarks. Our method does not rely on past workload history for workload type prediction.
Intelligent transportation systems and blockchain technology

The aim of this chapter is to make an introduction to the blockchain technology and its recent advances. It also provides details on smart contracts and how they are used to increase the flexibility of blockchain applications. Finally, it explains how blockchain can be used in intelligent transportation systems and how smart contracts can be employed in the development of smart mobility applications, such as ride sharing.