1,720,961 research outputs found
Reinforcement-supported Artificial Neural Network-based Trajectory Prediction
Full text linkWith the advent of 5G, Beyond 5G, and 6G cellular networks, mobility prediction has become a crucial task to enable a wide range of services such as handover and resource management. Mobility predictors analyze users’ historical mobility data, extract meaningful information, and learn moving patterns to forecast future locations and trajectories of users’ end systems. A trajectory is defined as the sequence of GPS coordinates or cellular antennas’ IDs over time.
We present a trajectory predictor based on Long Short Term Memory (LSTM), a variant of Recurrent Neural Networks (RNNs), to improve the performance of network applications, namely handover management and service migration for the Context Awareness Engine project with Orange SA [1]. When a mobile user moves within a mobile cellular network, transferring the cellular connection from one base station antenna to another is called handover, which requires many signaling operations. The knowledge of the user’s future location can offer a proactive handover mechanism so that the signaling procedure can be done before the mobile user’s arrival at a target antenna. In this way, the total system delay can remarkably decrease. Modern network services aim for reliable communication, low latency, and high throughput, especially in the case of user mobility, where services might easier experience interruption and quality degradation. Distributed technologies such as Multi-access Edge Computing (MEC) and Service Migration are introduced to offload requested data from the cloud to edge servers closer to users. In this context, trajectory prediction enables designing efficient service migration that guarantees service continuity and Quality of Service (QoS) in a proactive manner. Hence, attaining a reliable, accurate, and optimal trajectory predictor is a pivotal task.
Within various machine learning (ML) and artificial neural network (ANN) approaches, LSTM models have achieved a remarkable performance in the trajectory prediction task. However, most state-of-the-art works choose the neural networks heuristically and apply the same model to every user type, which does not guarantee optimal performance. We use Reinforcement Learning (RL) as a self-learning approach that is able to automate the LSTM architecture search process and hyperparameter optimization to explore the best neural architecture for each mobile user individually. To accelerate RL’s optimization process, we apply a Transfer Learning (TL) method. TL is a technique for reusing one task’s developed model as the starting point for another task in order not to initialize the second task from scratch. This way, we transfer the knowledge from a pre-trained RL-suggested neural network to a newly RL-suggested neural network. Although LSTMs have achieved excellent prediction accuracy, high training time and computational power still stay as their main bottlenecks. LSTMs are extremely slow by nature due to the fact that they learn spatio-temporal dependencies in sequential order. As an extension for our work [1], we propose the combination of RL and Convolutional Neural Networks (CNNs) as the optimal solution to save more computational resources. CNNs support parallelism and are computationally faster. One-dimensional CNNs concentrate on sequential data from a global perspective and extract features by applying efficient convolutional operations.
To evaluate the proposed predictors’ performance, we have done our computations on the High-Performance Computing Cluster of the University of Bern known as HPC Cluster - UBELIX, which supports parallel execution of multiple user predictions. Our RL-LSTM achieves on average 69.7% accuracy, which is almost 10% better than other ML approaches. The suggested RL-LSTM predictor consumes 176 minutes to explore-exploit the best neural architecture and to train data, which is only 28% of the time that a Grid Search-based LSTM requires (625 minutes) for performing the same task. On the other hand, our RL-CNN predictor achieves 67.7% accuracy, which is slightly less than RL-LSTM’s accuracy, while its optimization and training time is only 55 minutes, 31% of the time RL-LSTM already had spent. Moreover, in a real distributed scenario, applying thousands of individual RL-CNNs is impossible due to limited computational resources. Therefore, we detect similar trajectory users, build an exclusive RL-CNN per cluster based on a few users’ data, and transfer the pre-trained neural network knowledge between group members. With this approach, we can save up to 90% of computational resources while losing a few couples of percentages of the average accuracy
Attention-based Neural Networks for Multi-modal Trajectory Prediction
Full text linkTrajectory prediction is of great importance in wireless and intelligent networks. Accurate forecast of users’ trajectories can provide efficient handover management, continuous network connection, and generally better network quality of service. A trajectory is defined as the sequence of location logs, e.g., GPS coordinates or cellular antenna IDs, over time. We present a trajectory predictor based on Transformers Neural Networks acquiring the self-attention mechanism [1]. Mobile objects’ mobility patterns are influenced by their nearby neighbors. Thus, learning spatio-temporal dependencies among neighbor-trajectory users can help to better predict their trajectories [2]. In this direction, unlike our previously proposed mobility predictor (based on LSTM and CNN) designed for single agents [3], [4], [5], where agents were acting in isolation, we now propose the INteractive TRAnsformers ReinFORCEd (INTRAFORCE) social-aware neural network. We further employ a reinforcement learning agent to design the highest-performance transformers neural architecture based on the multi-modal trajectory scenario. Evaluations show that using the Orange dataset [4], our transformer-based predictor can remarkably increase the accuracy and decrease the training time and computations concerning our models based on LSTM and CNN [4]. Furthermore, on ETH+UCY datasets [6], INTRAFORCE achieves the least Mean Square Error compared to numerous state-of-the-art mechanisms on this popular dataset
FedForce: Network-adaptive Federated Learning for Reinforced Mobility Prediction
No full textFederated Learning (FL) has become popular in the field of mobility and trajectory prediction due to its privacy-preserving and scalability capabilities. Deploying FL over resource-constrained devices and varying network conditions is challenging for achieving a good tradeoff among prediction performance, computational load, and communication volume. On the other hand, the design of FL’s distributed neural architectures is complex, time-consuming, and dependent on experts’ prior knowledge. To tackle the above limitations, we propose the network-adaptive FEDerated learning for reinFORCEd mobility prediction (FedForce) system. FedForce employs reinforcement learning to design a transformer neural network whose architecture jointly optimizes the prediction accuracy, training time, and transmission time based on the mobility dataset’s unique features, the client’s computing capacity, and the available network throughput. FedForce outperforms several state-of-theart trajectory predictors and achieves an average displacement error of 0.20m on the ETH+UCY dataset and an accuracy of 76% on the Orange dataset (-0.02m and 10% higher than the bestperforming baseline, respectively), while cutting the FL training and transmission time by half. FedForce can save up to 80% of computational resources and 96% of communication overheads with a negligible accuracy decrease
GTP-Force: Game-Theoretic Trajectory Prediction through Distributed Reinforcement Learning
Full text linkThis paper introduces Game-theoretic Trajectory Prediction through distributed reinForcement learning (GTPForce), a system that tackles the challenge of predicting joint pedestrian trajectories in multi-agent scenarios. GTP-Force utilizes decentralized reinforcement learning agents to personalize neural networks for each competing player based on their noncooperative preferences and social interactions with others. By identifying the Nash Equilibria, GTP-Force accurately predicts joint trajectories while minimizing overall system loss in noncooperative environments. The system outperforms existing stateof- the-art trajectory predictors, achieving an average displacement error of 0.19m on the ETH+UCY dataset and 80% accuracy on the Orange dataset, which is -0.03m and 5% better than the best-performing baseline, respectively. Additionally, GTP-Force considerably reduces the model size of social mobility predictors compared to approaches with classical game theory
INTRAFORCE: Intra-Cluster Reinforced Social Transformer for Trajectory Prediction
Full text linkPredicting mobile users’ trajectories accurately is essential for improving the performance of wireless networks and autonomous systems. In this paper, we tackle the problem of trajectory prediction in a multi-agent scenario where the social interaction among users is taken into consideration.We propose Intra- Cluster Reinforced Social Transformer (INTRAFORCE), a novel system to design and train Social-Transformer neural networks that learn the spatio-temporal interactions among neighboring mobile users and predict their joint future trajectories. Unlike state-of-the-art social-aware trajectory predictors that either miss the large-distance interactions or are computationally expensive due to the pooling of all users’ interactions, INTRAFORCE clusters users with similar trajectories and learns their interactions. INTRAFORCE performs Neural Architecture Search to optimize each transformer’s architecture to fit each cluster’s user mobility features using Reinforcement Learning. Through experimental validation, we show that INTRAFORCE outperforms several state-of-the-art trajectory predictors on five widely used smallscale pedestrian mobility datasets and one large-scale privacyoriented cellular mobility dataset by achieving lower prediction error, training time, and computational complexity. Keywords: Social-aware Trajectory Prediction, Transformers, Reinforcement Learning, Neural Architecture Search, Clustering
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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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