1,722,237 research outputs found
Educational multimedia adaptation for power-saving in mobile learning
No full textLimited-battery power is a major constraint in mobile learning. It is important to adopt battery power-saving mechanisms in mobile learning applications in order to extend the duration of learning activities. This thesis explores issues related to power-saving in mobile learning. Streaming of online educational multimedia on mobile devices is a power hungry activity due to large amount of wireless data transfer. A number of power-saving multimedia adaptation techniques for streaming multimedia have been developed in the past. Most of these existing approaches achieve power-saving by uniformly lowering the presentation quality of an entire multimedia stream. These generic techniques will typically lower the visual quality of an entire multimedia stream uniformly, without considering its impact on perceived loss of visual information at different points of the multimedia stream.In this thesis, through a user study we suggest that reducing the quality of educational multimedia beyond a certain level - for power-saving adaptation - can cause perceived loss of visual information in quality-sensitive portions of a multimedia. This could have a negative impact on perceived learning effects and leave the resource unsuitable for learning. The results of the study suggest that different parts of a learning multimedia may have different lowest acceptable presentation quality requirements for avoiding perceived loss of visual information. The participants of the study were able to comprehend visual information in one fragment at a lower visual quality but could not comprehend visual information of some other fragments at the same quality level. To address this problem, we proposed a Content-Aware Power Saving Educational Multimedia Adaptation (CAPS-EMA) approach that suggests a way of delivering each portion of a multimedia in a lowest acceptable quality based on the visual contents of each fragment. We demonstrate an implementation of this approach using a prototype system called MoBELearn. The results of our evaluation studies suggest that the way CAPS-EMA adapts multimedia resources is acceptable to users in power-saving situations. CAPS-EMA requires some authoring processes in order to identify fragments and lowest acceptable quality constraints. An expert evaluation described the activities involved in the authoring process as easy to understand and performPower-saving multimedia adaptation mostly results in some compromises in terms of visual quality and information content. Existing techniques offer users little control over the adaptation process and they are obliged to accept the consequences of the adaptation. We propose a Learner Battery Interaction (LBI) mechanism that suggests offering users power-saving options and relevant feedback about the expected compromises for each power-saving option. This would enable users to make informed choices about power-saving. We evaluated the concept of LBI through a user study. The results of the study suggest a positive perceived usefulness of the system and that mobile learning applications may benefit from the idea.In the end, we propose a search mechanism for online adaptive learning resources that would help find a personalised learning resource that would fulfil the information needs of a learner in a battery-efficient way. This proposed mechanism is based on the concept of discovery of online open adaptive learning resources. For this purpose, we proposed an ontology model to describe adaptive learning resources, in terms of its adaptive features: learning and presentation features. This model could be used as a basis for implementing the proposed concept of the discovery of versions of adaptive learning resources in order to enable learners to engage in learning activities in a battery-efficient way by searching for online learning resources
Virtual Machine (VM) Memory Dumps for Ransomware Forensics
No full textThis dataset is for digital forensics of those machines which are infected by ransomware
SUSTAINABILITY EVALUATION AND DRIVING FORCES ANALYSIS IN VIETNAMESE CITIES
No full textArian Martinez Montes de Oca1*, Muhammad Asim
Balancing between Power Efficiency and High Performance on Software-based Intrusion Detection System
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Smart Scheduling of EVs Through Intelligent Home Energy Management Using Deep Reinforcement Learning
No full textThis article presents the deep reinforcement learning (DRL) based smart scheduling in intelligent home energy management system (SSIHEMS) for electric vehicles (EVs) scheduling by utilizing the photovoltaic (PV) on the rooftop for economic dispatch problems. Therefore, optimizing home appliances to minimize consumption cost is challenging because of the randomness of electricity prices and poses a challenge for efficient scheduling. The data-driven model-free DRL-based SSIHEMS is utilized to optimize the decision by managing different home appliances and offering appropriate scheduling EVs to overcome the shortcomings. The decision includes the proper scheduling of battery charging, discharging, and EV to reduce the dependency on the electric grid through a collaborative approach. In addition, the proposed work covers designing a gym-based environment that incorporates the states fed to an agent and receives the reward based on the action taken for scheduling. Hence, the case study is performed to validate the proposed approach. It is verified that the decisions for battery charging, discharging, and EV scheduling are managed well through PV generation with respect to time. Furthermore, to verify the robustness and effectiveness, a comparison of different algorithms such as deep Q-network (DQN), double DQN, and dueling DQN
Efficient Reactive Power Control Using Reinforcement Learning under Inaccurate Power Network Model
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