With the development of underwater exploration and marine protection, underwater vision tasks are widespread. Due to the degraded underwater environment, characterized by color distortion, low contrast, and blurring, camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings. Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes. To address these issues, we introduce the first underwater camouflaged instance segmentation (UCIS) dataset, abbreviated as UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations. In addition, we propose an Underwater Camouflaged Instance Segmentation network based on Segment Anything Model (UCIS-SAM). Our UCIS-SAM includes three key modules. First, the Channel Balance Optimization Module (CBOM) enhances channel characteristics to improve underwater feature learning, effectively addressing the model's limited understanding of underwater environments. Second, the Frequency Domain True Integration Module (FDTIM) is proposed to emphasize intrinsic object features and reduce interference from camouflage patterns, enhancing the segmentation performance of camouflaged objects blending with their surroundings. Finally, the Multi-scale Feature Frequency Aggregation Module (MFFAM) is designed to strengthen the boundaries of low-contrast camouflaged instances across multiple frequency bands, improving the model's ability to achieve more precise segmentation of camouflaged objects. Extensive experiments on the proposed UCIS4K and public benchmarks show that our UCIS-SAM outperforms state-of-the-art approaches. The code and dataset are released at https://github.com/wchchw/UCIS4K.

Transformer-based text-image matching model, known as CLIP, has garnered significant attention owing to its exceptional performance in text-image retrieval tasks and downstream applications. However, the interpret-ability of CLIP remains underexplored. Existing interpretation methods for Transformers often struggle with incomplete and unreliable attributions within the image and text modalities, respectively. In this paper, we propose a fine-grained interpretation method, termed Class Activation Values (CAV), to provide lucid and faithful visual explanations for CLIP-based text-image retrievals. Specifically, we systematically perform multi-scale accumulation and fusion of class-specific gradients and activation value features to generate high-definition explanations for the image encoder. Furthermore, we present element-wise gradient-based weights to attribute fine-grained relevance between value features and output similarity within the text encoder. The proposed CAV is capable of simultaneously rendering detailed and credible explanations due to its precise feature attribution. Extensive qualitative and quantitative experiments are conducted on the ImageNet-1k and MS COCO datasets, and the experimental results demonstrate that CAV outperforms state-of-the-art interpretation methods in both faithfulness and localization assessments across image and text modalities.

Account shuffling is a crucial method to solve the problems of high cross-shard transaction (TX) ratio and load imbalance in sharded blockchain. However, most existing methods primarily focus on account partitioning, with insufficient attention to account migration, resulting in limited improvements in throughput and latency. Therefore, we propose an efficient account shuffling mechanism based on lightweight migration in sharded blockchain (ASLM-Shard). Specifically, we first propose a migration-aware label propagation algorithm (MA-LPA) to improve the effect of account partitioning by balancing the relationship among account migration overhead, cross-shard TX ratio and load imbalance. Then, we adopt a sparse Merkle tree (SMT) to store account states to support flexible state verification, and propose a transaction-aware lightweight account migration (TLAM) method that leverages a “Lock-Mint” strategy to minimize migration costs while ensuring security. Extensive experimental results show that, compared with the SOTA baseline, ASLM-Shard improves system throughput by up to 27.8% and reduces TX latency by up to 81.1% when the account partitioning strategy is fixed, it also achieves up to 17.9% higher throughput and 23.4% lower latency when the migration method is fixed.

With the explosion of social media platforms, a substantial amount of data is generated from social information network. Tensor-based multi-modal clustering methods have been widely applied in various scenarios of social information network by mining potential correlative relationships from large-scale heterogeneous data. Nevertheless, the accuracy and efficiency of tensor-based multi-modal clustering methods are seriously restricted by noise data and the curse of dimensionality. Therefore, this paper presents a Tucker-based multi-modal clustering (TuMC) and an improved TuMC (ITuMC) to enhance the accuracy and efficiency of multi-modal clustering. First, we propose two Tucker-based attribute weight ranking learning approaches to calculate weight tensor efficiently. Then, we present a calculation approach for Tucker-based selective weighted tensor distance (SWTD) and a TuMC method. Meanwhile, an ITuMC method is explored by optimizing the calculation efficiency of the SWTD to further improve clustering speed. Finally, we present a Tucker-based multi-modal clustering and service framework for social information network. Extensive experimental results based on social Geolife GPS trajectory and electricity consumption datasets demonstrate that the TuMC and ITuMC methods can cluster multi-source heterogeneous data with both higher accuracy and efficiency under complex social information network by DVI, AR and execution time measurement.

Graph contrastive learning (GCL) has gained significant attention in the self-supervised learning field, which aims to maximize the mutual information of the same sample on different augmented views. How to balance the efficiency and performance of GCL model is a challenging issue. In existing GCL models, the performance is commonly limited by manual graph augmentation. Meanwhile, selecting substantial negative samples results in increased execution time. Therefore, this paper focuses on proposing a Multi-view Negative-Free Contrastive Learning on Adaptive graph augmentation (MNFCLA) method. First, we innovatively combine attention-based adaptive graph augmentation with non-negative sampling to obtain reliable augmented views and improve the computational efficiency of the model. Moreover, the GCL methods typically employ two augmented views, leading to model performance decrease. We design a multi-view Barlow Twins to calculate the cross correlation among multiple view embeddings, improving the model’s performance and robustness. Extensive experimental results on five node classification tasks demonstrate that the proposed MNFCLA model is superior to other baselines in computational efficiency and classification accuracy.

With advancements of diffusion models in generating high-quality, diverse, and creative content, subject-driven image editing based on diffusion models has been growing attention. However, it is challenging to perform precise control in image editing as minor changes in the input can lead to significant differences in generated concepts and content. Prevalent methods face significant limitations in terms of large numbers of trainable parameters and inference latency. Hence, we propose an efficient tensor-based fine-tuning method, Tucker/TT-based Fine-Tuning (TuckerFT and TTFT), for subject-driven image editing on diffusion models. Specifically, we first provide Tucker/TT-based Fine-tuning Convolutional Adapter and Attention Adapter to achieve parameter-efficient fine-tuning in convolutional and attention layers of diffusion models. Additionally, we further present a more sound initializing strategy for the Tucker/TT-based Adapters to guarantee steady training. Extensive experimental results demonstrate that TuckerFT/TTFT surpasses state-of-the-art methods on subject similarity, trainable parameters, and training duration.

As the visual interpretations for convolutional neural networks (CNNs), backpropagation attribution methods have been garnering growing attention. Nevertheless, majority of these methods merely concentrate on the ultimate convolutional layer, leading to tiny and concentrated interpretations that fail to adequately clarify the model-central attention. Therefore, we propose a precise attribution method (i.e., Holistic-CAM) for high-definition visual interpretation in the holistic stage of CNNs. Specifically, we first present weighted positive gradients to guarantee the sanity of interpretations in shallow layers and leverage multi-scale fusion to improve the resolution across the holistic stage. Then, we further propose a denoising strategy based on the fundamental scale component to eliminate the faithless attribution derived from fusing larger-scale features. The proposed method is capable of simultaneously rendering fine-grained and faithful interpretations for CNNs from shallow to deep layers. Extensive experimental results demonstrate that Holistic-CAM outperforms state-of-the-art methods on common-used benchmarks, including deletion and insertion, energy-based point game as well as Remove and Debias on ImageNet-1k, it also passes the sanity check easily.

Transportation big data generated from various Internet of Things devices have the feature of muti-source and heterogeneous. To efficiently represent and analyze these ubiquitous transportation big data, tensor and tensor-based data analysis methods have been widely adopted in recent years. As a tensor-based machine learning method, high-order dominant Z-eigen decomposition (HODZED) in multivariate multi-order Markov model is suitable for multi-modal transportation prediction. However, massive transportation data are usually generated in a streaming way and the transportation system requires frequent updates. To avoid recalculating the history data and provide immediate prediction, we propose a tensor train (TT) based incremental HODZED (TT-IHODZED) method. Concretely, we first present an incremental HODZED (IHODZED) method to update the dominant Z-eigentensor in multivariate multi-order Markov model. Then, TT-based tensor operations are adopted to IHODZED to speed up calculations, especially the repeated Einstein products. Furthermore, to solve the TT-based high-order linear equations in TT-IHODZED method, we also propose a TT-based biconjugate gradient stabilized (TT-HOBiCGS) algorithm. Experimental results based on real-world and synthetic datasets show that, compared to HODZED method, TT-IHODZED significantly improves computation efficiency up to 10 times while keeping the same or even better prediction accuracy.

In the era of big data, data security poses a paramount challenge in dealing with large-scale data in cloud computing environments. Traditional encryption methods such as RSA and Paillier face the problems that, as the data volume increases, the encryption consumes too much computation resource and the storage size of ciphertext is too large. To tackle these problems, this paper explores secure and lightweight tensor train (TT) based encryption methods for large-scale data in federated cloud environments. First, we develop a novel encryption method called Orthogonalized TT Encryption (OTT-EPT), which can encrypt large-scale plaintext data into a list of small-size ciphertext (TT cores) and store them in different clouds. Second, to tackle the unique characteristics of TT decomposition that adjacent TT cores have the same rank, which could be exploited by attackers to threaten encrypted data, we further propose a Noise-Added Orthogonalized TT Encryption (NOTT-EPT) method. NOTT-EPT can change arbitrary TT-rank by adding noise TT-cores on specific order. Furthermore, we present a TT-based encryption framework and a suite of secure homomorphic operation protocols for large-scale data computation in federated cloud environments. The experimental results indicate that, compared to state-of-the-art methods, our proposed methods not only ensure the integrity and security of encrypted data but also reduce both time and space consumption by about 1000 times.

To solve the problems of slow convergence and limited prediction ability of the Differential Evolution (DE) algorithm, an enhanced DE algorithm with the trustworthiness of a dynamic-selection framework (denoted by DEDF) is proposed. DEDF develops a trusted framework containing five mutation strategies to realize the dynamic selection of mutation strategies. On the basis of the framework, the mutation factor, crossover factor, and local exit strategy are improved to balance the algorithm’s local search and global search ability. A series of tests on the function set CEC2017 has been performed, and the findings show that compared with other benchmark algorithms, the DEDF has advantages in convergence speed and accuracy. The proposed algorithm DEDF can be effectively leveraged to address multi-objective optimization issues in IIOT.

Combining data-driven approaches and physical laws for industrial trajectory prediction can improve the performance of industrial applications such as path planning of robots and route selection of vehicles in transportation systems. In the era of industrial Big Data, the application of industrial trajectory prediction based on the physical Markov process and tensor model has attracted much attention. To synchronously improve the prediction accuracy and computational efficiency, this article proposes a tensor-train (TT)-based multiuser multivariate multiorder (3M) physical Markov prediction approach for multimodal industrial trajectory pattern mining. First, we propose a TT-based unified product calculation rule with its scalable computation approach based on decomposed TT cores to speed up the execution efficiency. Then, a TT-based 3M (TT-3M) Markov transition approach is presented. Furthermore, we put forward a TT-based power method to calculate the stationary joint eigentensor (SJE) and an SJE-based multimodal prediction algorithm to mine concealed trajectory patterns. Experimental results based on real GPS trajectory dataset show that compared with the original tensor-based 3M approach, the TT-3M approach can improve the computational efficiency up to three times and reduce the storage space proportion to a minimum of 1‰ while ensuring basically consistent prediction accuracy.

Knowledge graph builds the bridge from massive data generated by the interaction and communication between various objects to intelligent applications and services in Internet of Things. The graph representation learning technology represented by graph neural networks plays an essential role in the understanding and reasoning of the knowledge graph with complicated internal structure. Although they are capable of assigning different attention weights to neighbors, the graph attention network (GAT) and its variants are inherently flawed and inadequate in modeling high-order knowledge graphs with high heterogeneity. Therefore, we propose a novel multirelational GAT framework in this article for knowledge reasoning over heterogeneous graphs by employing tensor and tensor operations. Specifically, we formulate the general high-order heterogeneous knowledge graph first. Then, the tensor GAT (TGAT), composed of three components: 1) heterogeneous information propagation; 2) multimodal semantic-aware attention; and 3) knowledge aggregation, is developed to simulate rich interactions between mixed triples, entities, and relationships when aggregating local information. What is more, we utilize the Tucker model to compress the parameters of TGAT and further reduce the storage and calculation consumption of the intermediate calculation process on the premise of maintaining the expressive power. We conduct extensive experiments to solve the link prediction task on four real-world heterogeneous graphs, and the results demonstrate that the TGAT model proposed in this article remarkably outperforms state-of-the-art competitors and improves the hits@1 accuracy by up to 7.6%.

Human-oriented and machine-generated data in cyber-physical-social systems are often complicated graph-structured. Graph-powered learning methods are conducive to discovering valuable knowledge from large-scale graph data and improving decision-making processes. However, due to the neglect of diverse relations among things, most existing knowledge reasoning studies are inherently flawed and inefficient in processing the heterogeneous graphs with high-order connectivity. Tensor, as a powerful and effective tool to model high-level semantic interactions between various things, can provide high-order Internet of things graph with new perspectives and possibilities. Therefore, this article innovatively proposes a collaborative artificial intelligence of things data analysis and application framework based on Boolean tensors, which supports the expression and fusion of heterogeneous graph and ultimately promotes the AI processing. In this context, we focus on developing an incremental Boolean tensor factorization (IBTF) approach for efficient knowledge reasoning to meet the requirements of real-time and high-level quality demands for intelligent services. To the best of our knowledge, we are the first to do this work. More concretely, we present factors update and binary features merge algorithms for the integrated graph tensors to avoid numerous repeated calculations of historical data. Experimental results on general synthetic datasets demonstrate that the IBTF approach proposed in this article guarantees nearly equal approximate accuracy while reducing execution time by dozens and even more of times. Furthermore, experimental evaluations and interpretability analysis on real-world datasets verify the practicality of the proposed framework and approach.

Cyber-physical-social big data generated from ubiquitous devices and diverse spaces generally are multi-source, heterogeneous, and deeply intertwined. To efficiently analyze and handle the ubiquitous cyber-physical-social big data, tensor is considered as an effective tool, but the curse of dimensionality is still the main bottleneck of tensor-based big data analysis. Tensor networks can considerably alleviate or overcome it through the tensor approximate theory. Therefore, this paper focuses on developing an efficient big data processing framework based on tensor networks and providing an incremental tensor train decomposition approach for the streaming big data. Concretely, this paper first presents a hierarchical cyber-physical-social big data processing framework composed of three planes, namely, data representation and decomposition, data storage and processing, and data analysis and service, in which tensor train (TT) and quantized TT decompositions are particularly introduced to remarkably overcome the curse of dimensionality. Besides, to efficiently handle the continuous streaming big data and avoid the repeated decomposition for the history data, an incremental tensor train decomposition (ITTD) approach is proposed and the complexities are further analyzed in detail. Experimental results demonstrate that ITTD demonstrably outperforms the nonincremental TT decomposition in execution time on the precise of guaranteeing the nearly equal approximation error.

Predicting the future traffic flows by applying deep learning methods has become an alternative way for transportation network management. Combining recurrent neural networks (RNNs) with tensor to implement accurate predictions has drawn intensive attention. However, traditional RNNs cannot deal with the high-order traffic flow data and capture their inherent structural relationship to provide accurate multi-modal prediction services. Therefore, this article focuses on proposing a series of tensor-based RNNs (T-RNNs) and a T-RNNs based multi-modal prediction approach (TMMP) to provide accurate prediction services. First, we propose three types of T-RNNs including tensor-based vanilla RNN, tensor-based long short-term memory (T-LSTM) and tensor-based gated recurrent unit (T-GRU), in which the input, output and weights are arbitrary high-order tensors. Then, to compress the weight parameters, we further propose two compact TT-based GRU (TT-GRU) and Tucker-based GRU (Tucker-GRU) models by applying tensor train (TT) and Tucker decompositions to T-GRU model. Afterwards, based on the high-order output tensor generated by T-RNNs, a TMMP approach is proposed to achieve the accurate predictions under various scenarios. Extensive experimental results on the metro traffic flow dataset demonstrate that the proposed TMMP approach can improve the traffic flow prediction accuracy by at most 25.29 percentage compared with the traditional MSE-based approaches. Meanwhile, compared with the T-GRU model, the TT-GRU model can compress the number of parameters by 200~780 times. The proposed T-RNNs and TMMP approach can adapt to different application scenarios and can be used to improve the efficiency of transportation management.

Excavating human's temporal and spatial regularities hidden in trajectory data and predicting users' mobility patterns are conducive to providing proactive smart services for people. Combining Markov transition and tensor theories to improve the prediction performance has proved to be effective. However, the existing state-of-the-art multivariate multiorder Markov model neglects the mutual influence among different users. In a practical trajectory system, people's mobility patterns are influenced by their social relationships. Therefore, this article focuses on proposing a novel multiuser multivariate multiorder Markov model and a multimodal user mobility pattern prediction approach. First, we construct two concrete Markov trajectory transition models based on the single-user multivariate multiorder Markov model. Then, we propose a multiuser multivariate multiorder Markov model, including the influence model of multiple users and the multiuser Markov trajectory transition model. Afterward, two unified product-based power methods are developed to calculate the stationary joint eigentensor (SJE) for single-user and multiuser multivariate multiorder Markov models. Furthermore, an SJE-based multimodal prediction approach is proposed to realize precise mobility pattern prediction. Finally, we conduct a series of experiments based on real-world GPS trajectory data set to verify the performance of the proposed approaches. Experimental results demonstrate that the proposed multiuser multivariate multiorder Markov-based multimodal prediction approach can improve the trajectory prediction accuracy by highest up to 31.10% points compared with the Z-eigen-based approach.

Tensor-based big data analysis approaches are effectively exploited to handle multisource and heterogeneous cyber-physical-social big data generated from diverse spaces. However, the curse of dimensionality seriously restricts their widespread exploitation, especially under edge/fog computing environments. To alleviate the dilemma, we attempt to present a set of tensor-train (TT)-based tensor operations with their scalable computations and then propose a novel TT-based big data processing framework under edge/fog computing environments. Specifically, in this article, we first summarize and present a set of TT-based tensor operations by converting the original high-order tensor operation to a series of low-order (second- or third-order) TTcore-based operations. Then, we propose a two-layer scalable TT-based computation architecture, including inter-TTcore and intra-TTcore scalable models. Afterward, according to various scalable models, a series of scalable TT-based tensor computations (STT-TCs) with their complexity analysis are proposed in detail. Finally, we propose a novel TT-based big data processing framework to adapt to edge/fog computing environments. We conduct extensive experiments based on both random data sets and real-world ubiquitous bus traffic data sets. Experimental results demonstrate that the proposed STT-TCs can significantly improve computation efficiency and are suitable for edge/fog computing environments.

Online education and e-learning have vigorously sprung up and produced massive educational data in a streaming way. It is very challenging to acquire the appropriate learning resources and the suitable learning partners from the streaming-updated educational big data. This article aims to provide sustainable smart educational services including precise personalized recommendation and adaptive clustering under different contexts by correlatively analyzing the global educational data from multiple dimensions via incremental tensor decomposition. First, a tensor-based recommendation and service framework for the streaming educational big data is developed. Then three local tensors concerning learners, resources, and learning records are constructed and further fused to an integrated global learner-resource tensor. Afterward, we present an incremental tensor-based correlative analysis and personalized recommendation (ITCA-PR) algorithm to recommend appropriate resources under various contexts. Besides, we also propose an incremental tensor-based adaptive clustering and community recommendation (ITAC-CR) algorithm to recommend suitable learning partners under various contexts and accordingly construct adaptive learning communities. Extensive experimental results demonstrate that the ITCA-PR algorithm outperforms state-of-the-art improved collaborative filtering algorithms by F-score measurement and the ITAC-CR algorithm has a better clustering performance by DVI measurement. These precise educational services will promote the development of sustainable smart education.

Predicting the future network traffic through big data analysis technologies has been one of the important preoccupations of network design and management. Combining Markov chains with tensors to implement predictions has received considerable attention in the era of big data. However, when dealing with multi-order Markov models, the existing approaches including the combination of states and Z-eigen decomposition still face some shortcomings. Therefore, this paper focuses on proposing a novel multivariate multi-order Markov transition to realize multi-modal accurate predictions. First, we put forward two new tensor operations including tensor join and unified product (UP). Then a general multivariate multi-order (2M) Markov model with its UP-based state transition is proposed. Afterwards, we develop a multi-step transition tensor for 2M Markov models to implement the multi-step state transition. Furthermore, an UP-based power method is proposed to calculate the stationary joint probability distribution tensor (i.e., stationary joint eigentensor, SJE) and realize SJE based multi-modal accurate predictions. Finally, a series of experiments under various Markov models on real-world network traffic datasets are conducted. Experimental results demonstrate that the proposed SJE based approach can improve the prediction accuracy for network traffic by highest up to 38.47 percentage points compared with the Z-eigen based approach.

Mobile cloud computing (MCC) is extensively ubiquitous in the mobile Internet era and embraces complex environments because of the heterogeneity of devices and complexity of communications. Balancing the costs of different influencing objectives (e.g., energy consumption, system reliability, and quality of experience (QoE)) in MCC faces great challenges. This paper focuses on reasonably allocating computational tasks to suitable cores of mobile devices or cloud in MCC to minimize the total energy consumption, and maximize the system reliability and QoE. Concretely, this paper 1 proposes a holistic mobile cloud optimization model including energy consumption, system reliability, and QoE; 2 presents a DVFS-enabled and thermal-aware global energy consumption model which simultaneously considers the synergy of multiple factors concerning mobile devices, cloud, and networks; 3 constructs a tensor-based representation model to comprehensively reflect the complex relationship of multiple influencing factors and cope with their heterogeneity; and 4 proposes a customized optimization framework and two heuristic single-objective optimization (SOO) and triple-objective optimization (TOO) algorithms based on simulated annealing. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-art scheduling schemes in SOO and the Pareto front in TOO can provide appropriate solutions to satisfy different application requirements.

Big data has received considerable attentions in recent years because of massive data volumes in multifarious fields. Considering various “V” features, big data tasks are usually highly complex and computational intensive. These tasks are generally performed in parallel in data centers resulting in massive energy consumption and Green House Gases emissions. Therefore, efficient resource allocation considering the synergy of the performance and energy efficiency is one of the crucial challenges today. In this paper, we aim to achieve maximum energy efficiency by combining thermal-aware and dynamic voltage and frequency scaling (DVFS) techniques. This paper proposes: (a) a thermal-aware and power-aware hybrid energy consumption model synchronously considering the computing, cooling, and migration energy consumption; (b) a tensor-based task allocation and frequency assignment model for representing the relationship among different tasks, nodes, time slots, and frequencies; and (c) a big data Task Scheduling algorithm based on Thermal-aware and DVFS-enabled techniques (TSTD) to minimize the total energy consumption of data centers. The experimental results demonstrate that the proposed TSTD algorithm significantly outperforms the state-of-the-art energy efficient algorithms from total, computing, and cooling energy consumption perspectives, as well as cooling energy consumption proportion and total energy consumption savings.

Due to the rapid advances of information technologies, Big Data, recognized with 4Vs characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as many challenges. A major benefit of Big Data is to provide timely information and proactive services for humans. The primary purpose of this paper is to review the current state-of-the-art of Big Data from the aspects of organization and representation, cleaning and reduction, integration and processing, security and privacy, analytics and applications, then present a novel framework to provide high-quality so called Big Data-as-a-Service. The framework consists of three planes, namely sensing plane, cloud plane and application plane, to systemically address all challenges of the above aspects. Also, to clearly demonstrate the working process of the proposed framework, a tensor-based multiple clustering on bicycle renting and returning data is illustrated, which can provide several suggestions for rebalancing of the bicycle-sharing system. Finally, some challenges about the proposed framework are discussed.

The Internet of Things (IoT) opens up tremendous opportunities to location-based industrial applications that leverage both Internet-resident resources and phones' processing power and sensors to provide location information. Location-based service is one of the vital applications in commercial, economic, and public domains. In this paper, we propose a novel localization scheme called NextMe, which is based on cellular phone traces. We find that the mobile call patterns are strongly correlated with the co-locate patterns. We extract such correlation as social interplay from cellular calls, and use it for location prediction from temporal and spatial perspectives. NextMe consists of data preprocessing, call pattern recognition, and a hybrid predictor. To design the call pattern recognition module, we introduce the notions of critical calls and corresponding patterns. In addition, NextMe does not require that the cell tower addresses should be bounded with concrete coordinates, e.g., global positioning system (GPS) coordinates. We validate NextMe across MIT Reality Mining Dataset, involving 500 000 h of continuous behavior information and 112 508 cellular calls. Experimental results show that NextMe achieves fine-grained prediction accuracy at cell tower level in the forthcoming 1-6 h with 12% accuracy enhancement averagely from cellular calls.