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This study developed a mobile-memo system that supports the knowledge acquisition and posting-question to assist learners' learning in a ML (mobile learning) environment. To understand the effectiveness of our proposed system, the data were collected from the system logs, such as the elements of multimedia used in posting-question and a questionnaire regarding the students' learning attitude as well as satisfaction towards the use of the mobile-memo system. The experimental result showed that the mobile-memo was effective for learners to gather information in construction and reflection during the learning activities. In other words, the mobile-memo system can effectively support learners to acquire knowledge and post question relating to the learning course contents during learning activities. (Contains 3 tables and 3 figures.)

Sustainable freight transportation is one of the essential concepts in the smart city. Under this concept, many people connected with mobile devices produce location data and potential opportunities for transporting small objects in a more environmentally friendly and sustainable way. Crowdshipping, which utilises public people as transportation, is one of the terminal solutions in the last mile delivery scenario. Nevertheless, precisely assigning the delivery to the right crowd willing to accept the job is challenging because the solution space is too large to perform a full search. This article proposes a trajectory feature extraction algorithm and a task‐to‐crowd matching (T2CM) algorithm for coping with the job‐to‐crowd assignment problem. A simulation based on the real‐world dataset is conducted on three different scenarios to justify the outcome from our proposed method to the job assignment results. [ABSTRACT FROM AUTHOR]

This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia., Author(s): Rusul L. Abduljabbar (corresponding author) [1]; Hussein Dia [1]; Pei-Wei Tsai [2] 1. Introduction Freeway short-term traffic prediction models have been researched extensively in the literature [1-8]. The strong [...]

In recent years, researchers have conducted various studies on applying wireless networking technology and mobile devices in education settings. However, research on behavioral patterns in learners' online asynchronous discussions with mobile devices is limited. The purposes of this study are to develop a mobile learning system, mobile interactive teaching feedback system (MITFS), linked to both mobile devices and the internet, to support learners with online asynchronous discussion, and combine content analysis and sequential analysis to compare and contrast the social knowledge construction behavioral patterns of problem-based asynchronous discussion in e-learning and m-learning environments. This study investigated four weeks of online discussions in an "Introduction to Computer Science" course involving forty first year university students. The control group (online asynchronous discussion without mobile devices) and the experimental group (online asynchronous discussion with mobile devices) in the group discussions were explored. By using content analysis and sequential analysis for the problem-based online asynchronous discussion of the behavioral patterns and differences between students in control and experimental group, the results showed that using mobile devices in online asynchronous discussion influenced students' learning performance. Some interesting results were found. Firstly, when the students used mobile devices in discussion situations, they could more engage in reflecting thinking, sharing more information, and further facilitating social knowledge construction among group members. Secondly, the experimental group performed better than the control group in terms of participation and diversity in knowledge construction behavioral patterns. Finally, based upon the findings, some implications are proposed for further research. (Contains 9 figures and 10 tables.)

Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome's building blocks, which is able to reduce the algorithm's running time and ensure the alignment's quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient. [ABSTRACT FROM AUTHOR]

The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime. [ABSTRACT FROM AUTHOR]

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods. [ABSTRACT FROM AUTHOR]

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi. [ABSTRACT FROM AUTHOR]

The paper titled "Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval" by J. Liu and Y. Qiao focuses on the proposed intelligent data classification algorithm based on machine learning. AIoT (Artificial Intelligence of Things) is a relatively new term that has recently become a hot topic that combines two of the hottest acronyms, AI (Artificial Intelligence) and IoT (Internet of Things). AI can multiply the value of IoT; conversely, IoT can promote the learning and intelligence of AI. [Extracted from the article]