Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
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E-LEARNING (30 journals)

Showing 1 - 30 of 30 Journals sorted alphabetically
Advanced Technology for Learning     Full-text available via subscription   (Followers: 125)
Aprendo con NooJ     Open Access   (Followers: 1)
Australasian Journal of Educational Technology     Open Access   (Followers: 17)
Computer Assisted Language Learning     Hybrid Journal   (Followers: 28)
Computer Speech & Language     Hybrid Journal   (Followers: 13)
Education in the Knowledge Society     Open Access   (Followers: 1)
Educational Technology Research and Development     Partially Free   (Followers: 45)
eLearn Magazine     Full-text available via subscription   (Followers: 4)
European Journal of Open, Distance and E-Learning - EURODL     Open Access   (Followers: 11)
Human-centric Computing and Information Sciences     Open Access   (Followers: 6)
Interdisciplinary Journal of e-Skills and Lifelong Learning     Open Access   (Followers: 3)
Interdisciplinary Journal of Information, Knowledge, and Management     Open Access   (Followers: 12)
International Journal of Adult Education and Technology     Hybrid Journal   (Followers: 17)
International Journal of Ambient Computing and Intelligence     Full-text available via subscription   (Followers: 3)
International Journal of Computer-Assisted Language Learning and Teaching     Full-text available via subscription   (Followers: 14)
International Journal of Online Pedagogy and Course Design     Full-text available via subscription   (Followers: 7)
International Journal of Research Studies in Educational Technology     Open Access   (Followers: 10)
Journal of Assistive Technologies     Hybrid Journal   (Followers: 19)
Journal of Computers in Education     Hybrid Journal   (Followers: 9)
Journal of Machine Learning Research     Open Access   (Followers: 61)
Jurnal Inovasi Teknologi Pendidikan     Open Access  
Jurnal Komtika     Open Access  
Nordic Journal of Digital Literacy     Open Access  
Online Journal of Distance Learning Administration     Open Access   (Followers: 12)
Research and Practice in Technology Enhanced Learning     Open Access   (Followers: 8)
Research in Learning Technology     Open Access   (Followers: 72)
RIED. Revista Iberoamericana de Educación a Distancia     Open Access   (Followers: 1)
RU&SC. Revista de Universidad y Sociedad del Conocimiento     Open Access   (Followers: 1)
Tidsskriftet Læring og Medier (LOM)     Open Access   (Followers: 1)
UOC Papers. Revista sobre la sociedad del conocimiento     Open Access   (Followers: 1)
Similar Journals
Journal Cover
Human-centric Computing and Information Sciences
Journal Prestige (SJR): 0.658
Citation Impact (citeScore): 3
Number of Followers: 6  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2192-1962
Published by SpringerOpen Homepage  [228 journals]
  • The shift to 6G communications: vision and requirements

    • Abstract: Abstract The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.
      PubDate: 2020-12-21
       
  • Social Internet of Things: vision, challenges, and trends

    • Abstract: Abstract IoT describes a new world of billions of objects that intelligently communicate and interact with each other. One of the important areas in this field is a new paradigm-Social Internet of Things (SIoT), a new concept of combining social networks with IoT. SIoT is an imitation of social networks between humans and objects. Objects like humans are considered intelligent and social. They create their social network to achieve their common goals, such as improving functionality, performance, and efficiency and satisfying their required services. Our article’s primary purpose is to present a comprehensive review article from the SIoT system to analyze and evaluate the recent works done in this area. Therefore, our study concentrated on the main components of the SIoT (Architecture, Relation Management, Trust Management, web services, and information), features, parameters, and challenges. To gather enough information for better analysis, we have reviewed the articles published between 2011 and December 2019. The strengths and weaknesses of each article are examined, and effective evaluation parameters, approaches, and the most used simulation tools in this field are discussed. For this purpose, we provide a scientific taxonomy for the final SIoT structure based on the academic contributions we have studied. Ultimately we observed that the evaluation parameters are different in each element of the SIoT ecosystem, for example for Relation Management, scalability 29% and navigability 22% are the most concentrated metrics, in Trust Management, accuracy 25%, and resiliency 25% is more important, in the web service process, time 23% and scalability 16% are the most mentioned and finally in information processing, throughput and time 25% are the most investigated factor. Also, Java-based tools like Eclipse has the most percentage in simulation tools in reviewed literature with 28%, and SWIM has 13% of usage for simulation.
      PubDate: 2020-12-18
       
  • A model-driven approach to ensure trust in the IoT

    • Abstract: Abstract The Internet of Things (IoT) is a paradigm that permits smart entities to be interconnected anywhere and anyhow. IoT opens new opportunities but also rises new issues. In this dynamic environment, trust is useful to mitigate these issues. In fact, it is important that the smart entities could know and trust the other smart entities in order to collaborate with them. So far, there is a lack of research when considering trust through the whole System Development Life Cycle (SDLC) of a smart IoT entity. In this paper, we suggest a new approach that considers trust not only at the end of the SDLC but also at the start of it. More precisely, we explore the modeling phase proposing a model-driven approach extending UML and SysML considering trust and its related domains, such as security and privacy. We propose stereotypes for each diagram in order to give developers a way to represent trust elements in an effective way. Moreover, we propose two new diagrams that are very important for the IoT: a traceability diagram and a context diagram. This model-driven approach will help developers to model the smart IoT entities according to the requirements elicited in the previous phases of the SDLC. These models will be a fundamental input for the following and final phases of the SDLC.
      PubDate: 2020-12-14
       
  • Multiple Kinect based system to monitor and analyze key performance
           indicators of physical training

    • Abstract: Abstract Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.
      PubDate: 2020-12-14
       
  • An efficient attribute-based hierarchical data access control scheme in
           cloud computing

    • Abstract: Abstract Security issues in cloud computing have become a hot topic in academia and industry, and CP-ABE is an effective solution for managing and protecting data. When data is shared in cloud computing, they usually have multiple access structures that have hierarchical relationships. However, existing CP-ABE algorithms do not consider such relationships and just require data owners to generate multiple ciphertexts to meet the hierarchical access requirement, which would incur substantial computation overheads. To achieve fine-grained access control of multiple hierarchical files effectively, first we propose an efficient hierarchical CP-ABE algorithm whose access structure is linear secret sharing scheme. Moreover, we construct an attribute-based hierarchical access control scheme, namely AHAC. In our scheme, when a data visitor’s attributes match a part of the access control structure, he can decrypt the data that associate with this part. The experiments show that AHAC has good security and high performance. Furthermore, when the quantity of encrypted data files increases, the superiority of AHAC will be more significant.
      PubDate: 2020-12-05
       
  • The effect of eye movements and cultural factors on product color
           selection

    • Abstract: Abstract A color is a powerful tool used to attract people’s attention and to entice them to purchase a product. However, the way in which a specific color influences people’s color selection and the role of their eye movements and cultural factors in this process remain unknown. In this study, to delve into this problem, we designed an experiment to determine the influence of specific colors on people’s product preferences by using an eye-tracking device, intending to identify the role of their eye movements and cultural factors. Based on the experimental data, a detailed influence path model was built to describe the effect of specific colors on product evaluations by an integrated moderation and mediation analysis. Our findings show that in the influence process, the effects of specific colors on product evaluations are mediated by eye movements. Additionally, cultural factors partly moderate the process as an influencing factor. The research findings from this study have important implications for user-centered product design and visual marketing management.
      PubDate: 2020-11-24
       
  • Collaborative behavior, performance and engagement with visual analytics
           tasks using mobile devices

    • Abstract: Abstract Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visualizations. This research investigates the use of co-located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non-Shared) and how being placed at various position arrangements (Corner-to-Corner, Face-to-Face, and Side-by-Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 participants divided into 6 groups (2 modes \(\times\) 3 positions) using a tool that we developed to support the exploration of 3D visual structures in a collaborative manner. Our results show that the shared control and view version in the Side-by-Side position is the most favorable and can improve task efficiency. In this paper, we present the results and a set of recommendations that are derived from them.
      PubDate: 2020-11-22
       
  • Facial UV map completion for pose-invariant face recognition: a novel
           adversarial approach based on coupled attention residual UNets

    • Abstract: Abstract Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods.
      PubDate: 2020-11-10
       
  • Medical image processing with contextual style transfer

    • Abstract: Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.
      PubDate: 2020-11-10
       
  • Newspaper article-based agent control in smart city simulations

    • Abstract: Abstract The latest research on smart city technologies mainly focuses on utilizing cities’ resources to improve the quality of the lives of citizens. Diverse kinds of control signals from massive systems and devices such as adaptive traffic light systems in smart cities can be collected and utilized. Unfortunately, it is difficult to collect a massive dataset of control signals as doing so in the real-world requires significant effort and time. This paper proposes a deep generative model which integrates a long short-term memory model with generative adversarial network (LSTM-GAN) to generate agent control signals based on the words extracted from newspaper articles to solve the problem of collecting massive signals. The discriminatory network in the LSTM-GAN takes continuous word embedding vectors as inputs generated by a pre-trained Word2Vec model. The agent control signals of sequential actions are simultaneously predicted by the LSTM-GAN in real time. Specifically, to collect the training data of smart city simulations, the LSTM-GAN is trained based on the Corpus of Contemporary American English (COCA) newspaper dataset, which contains 5,317,731 sentences, for a total of 93,626,203 word tokens, from written texts. To verify the proposed method, agent control signals were generated and validated. In the training of the LSTM-GAN, the accuracy of the discriminator converged to 50%. In addition, the losses of the discriminator and the generator converged from 4527.04 and 4527.94 to 2.97 and 1.87, respectively.
      PubDate: 2020-11-03
       
  • TSME: a trust-based security scheme for message exchange in vehicular Ad
           hoc networks

    • Abstract: Abstract A Vehicular Ad hoc NETwork (VANET) is a self-organized network formed by connected vehicles, which allows the exchange of useful traffic information in a timely manner. In such a context, evaluating the reliability of transmissions is vital. Trust can be used to promote such healthy collaboration. In fact, trust enables collaborating vehicles to counter uncertainty and suspicion by establishing trustworthy relationships. The main contribution of this paper is the proposition of a trust-based security scheme for message exchange in a VANET called TSME. Because of VANET characteristics, including dynamicity and high speed, we first proposed a VANET Grouping Algorithm (VGA); a suitable clustering algorithm organizing the network into groups with elected Group-Heads. Second, built on the VGA, we defined our trust management scheme dealing with vehicles’ reputations. Finally, we proposed a formal specification of the scheme using an inference system, and conducted a formal validation to assess its completeness and soundness rather than conducting simulations where some potentially rare conflicting or malfunctioning situations might not be detected. Soundness was proven by showing that there were no conflicts in our scheme, and completeness was established by assessing that all potential situations could be handled. The results obtained showed that our scheme for evaluating the veracity of exchanged messages is formally sound and complete.
      PubDate: 2020-10-17
       
  • TMaR: a two-stage MapReduce scheduler for heterogeneous environments

    • Abstract: Abstract In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.
      PubDate: 2020-10-07
       
  • Deep learning scheme for character prediction with position-free touch
           screen-based Braille input method

    • Abstract: Abstract Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.
      PubDate: 2020-09-19
       
  • A collaborative healthcare framework for shared healthcare plan with
           ambient intelligence

    • Abstract: Abstract The fast propagation of the Internet of Things (IoT) devices has driven to the development of collaborative healthcare frameworks to support the next generation healthcare industry for quality medical healthcare. This paper presents a generalized collaborative framework named collaborative shared healthcare plan (CSHCP) for cognitive health and fitness assessment of people using ambient intelligent application and machine learning techniques. CSHCP provides support for daily physical activity recognition, monitoring, assessment and generate a shared healthcare plan based on collaboration among different stakeholders: doctors, patient guardians, as well as close community circles. The proposed framework shows promising outcomes compared to the existing studies. Furthermore, the proposed framework enhances team communication, coordination, long-term plan management of healthcare information to provide a more efficient and reliable shared healthcare plans to people.
      PubDate: 2020-09-11
       
  • SD2PA: a fully safe driving and privacy-preserving authentication scheme
           for VANETs

    • Abstract: Abstract The basic idea behind the vehicular ad-hoc network (VANET) is the exchange of traffic information between vehicles and the surrounding environment to offer a better driving experience. Privacy and security are the main concerns for meeting the safety aims of the VANET system. In this paper, we analyse recent VANET schemes that utilise a group authentication technique and found important vulnerabilities in terms of driving safety. These systems also suffer from vulnerabilities in terms of management efficiency and computational complexity. To defeat these problems, we propose a lightweight scheme, SD2PA, based on a general hash function for VANET. The proposed scheme overcomes the non-safe driving problem that resulted from the critical driving area. Moreover, the vehicle authentication is only done once by the VANET system administrator during the vehicle’s moving, so the authentication redundancy for the entire system is reduced and system management efficiency is enhanced. The SD2PA scheme also provides anonymity to protect the vehicle’s privacy, unless an important action needs to be taken against a malicious vehicle. A deep computational cost and communicational overhead analysis indicates that SD2PA is better than related schemes, as well as efficiently meeting VANET’s security and privacy needs.
      PubDate: 2020-09-02
       
  • Modelling email traffic workloads with RNN and LSTM models

    • Abstract: Abstract Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.
      PubDate: 2020-09-02
       
  • Robust cooperative car-parking: implications and solutions for selfish
           inter-vehicular social behaviour

    • Abstract: Abstract Vehicular cooperation mechanisms are known to provide efficiency and scalability benefits but for the mechanisms to be human-centric, there is a need for them to be robust and resilient to anti-social behaviours such as deception. More specifically, decentralised vehicle-to-vehicle cooperation has been shown to be an effective and convenient approach to coordinate the use of dynamically changing common road resources such as car parking. However, the potential for selfish behaviour of some vehicles in the form of sending false information for self-benefit has a significant effect on the value of cooperation. In this paper, we investigate, via extensive simulations, the deception behaviour of malicious vehicles looking to park by sending false information in decentralized vehicle cooperation. Furthermore, Deception Detection Mechanisms (DDMs) are introduced and are shown to be valuable in ameliorating the effects of malicious vehicles. The work has broader implications for an open world of autonomous and adaptive systems with decentralized control and ownership which need to cooperate to use shared resources; they are susceptible to malicious behaviour, and hence, need to be built to be robust to such behaviour.
      PubDate: 2020-08-26
       
  • Automatic, location-privacy preserving dashcam video sharing using
           blockchain and deep learning

    • Abstract: Abstract Today, many people use dashcams, and videos recorded on dashcams are often used as evidence of accident fault. People can upload videos of dashcam recordings with specific accident clips and share the videos with others who request them, by providing the time or location of an accident. However, dashcam videos are erased when the dashcam memory is full, so periodic backup is necessary for video sharing. It is inconvenient for dashcam owners to search for and transmit a requested video clip from backup videos. In addition, anonymity is not ensured, which may reduce location privacy by exposing the video owner’s location. To solve this problem, we propose a video sharing scheme with accident detection using deep learning coupled with automatic transfer to the cloud; we also propose ensuring data and operational integrity along with location privacy by using blockchain smart contracts. Furthermore, our proposed system uses proxy re-encryption to enhance the confidentiality of a shared video. Our experiments show that our proposed automatic video sharing system is cost-effective enough to be acceptable for deployment.
      PubDate: 2020-08-26
       
  • CAPHAR: context-aware personalized human activity recognition using
           associative learning in smart environments

    • Abstract: Abstract The existing action recognition systems mainly focus on generalized methods to categorize human actions. However, the generalized systems cannot attain the same level of recognition performance for new users mainly due to the high variance in terms of human behavior and the way of performing actions, i.e. activity handling. The use of personalized models based on similarity was introduced to overcome the activity handling problem, but the improvement was found to be limited as the similarity was based on physiognomies rather than the behavior. Moreover, human interaction with contextual information has not been studied extensively in the domain of action recognition. Such interactions can provide an edge for both recognizing high-level activities and improving the personalization effect. In this paper, we propose the context-aware personalized human activity recognition (CAPHAR) framework which computes the class association rules between low-level actions/sensor activations and the contextual information to recognize high-level activities. The personalization in CAPHAR leverages the individual behavior process using a similarity metric to reduce the effect of the activity handling problem. The experimental results on the “daily lifelog” dataset show that CAPHAR can achieve at most 23.73% better accuracy for new users in comparison to the existing classification methods.
      PubDate: 2020-08-12
       
  • Cloud spot instance price prediction using kNN regression

    • Abstract: Abstract Cloud computing can provide users with basic hardware resources, and there are three instance types: reserved instances, on-demand instances and spot instances. The price of spot instance is lower than others on average, but it fluctuates according to market demand and supply. When a user requests a spot instance, he/she needs to give a bid. Only if the bid is not lower than the spot price, user can obtain the right to use this instance. Thus, it is very important and challenging to predict the price of spot instance. To this end, we take the most popular and representative Amazon EC2 as a testbed, and use the price history of its spot instance to predict future price by building a k-Nearest Neighbors (kNN) regression model, which is based on our mathematical description of spot instance price prediction problem. We compare our model with Linear Regression (LR), Support Vector Machine Regression (SVR), Random Forest (RF), Multi-layer Perception Regression (MLPR), gcForest, and the experiments show that our model outperforms the others.
      PubDate: 2020-08-09
       
 
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