Computing trajectory similarity is a fundamental task in trajectory analysis. Traditional heuristic methods suffer from quadratic computational complexity, which limits their scalability to large datasets. Recently, Trajectory Representation Learning (TRL) has been extensively studied to address this limitation. However, most existing TRL algorithms face two key challenges. First, they prioritize spatial similarity while neglecting the intricate spatio-temporal dynamics of trajectories, particularly temporal regularities. Second, these methods are often constrained by predefined single spatial or temporal scales, which can significantly impact performance, since the measurement of trajectory similarity depends on spatial and temporal resolution. To address these issues, we propose MSST, a Multi-Scale Self-supervised Trajectory Representation Learning framework. MSST simultaneously processes spatial and temporal information by generating 3D spatial-temporal tokens, thereby capturing spatio-temporal characteristics of trajectories more effectively. Further, MSST explore the multi-scale characteristics of trajectories. Finally, self-supervised contrastive learning is employed to enhance the consistency between the trajectory representations from different views. Experimental results on three real-world datasets for similarity trajectory computation provide insight into the design properties of our approach and demonstrate the superiority of our approach over existing TRL methods. MSST significantly surpasses all state-of-the-art competitors in terms of effectiveness, efficiency, and robustness. We explore the multi-scale characteristics of trajectories. To the best of our knowledge, this is the first effort in the TRL literature. Compared to previous TRL research, the proposed method can balance the noise and the details of trajectories, enabling a more comprehensive analysis by accounting for the variability inherent in trajectory data across different scales.

Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories' temporal regularities, which are essential for mining fine-grained mobility patterns for applications including traveling group identification, transportation mode discovering, social security emergency, etc. To fill this gap, we propose ConDTC, a contrastive deep trajectory clustering method targeting for fine-grained mobility pattern mining. Specifically, we first design a spatial-temporal trajectory representation learning method which can capture both spatial and temporal regularities of trajectories synchronously. The proposed trajectory representation model can be used as a pre-trained model to serve various downstream trajectory mining tasks. Then, we construct a contrastive trajectory clustering module which optimizes trajectory representations and clustering performance simultaneously. Experimental results on three datasets validate that ConDTC can identify fine-grained mobility patterns by clustering trajectories with similar spatial-temporal mobility patterns together while separating those with different mobility patterns apart. Actually, ConDTC outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency and robustness.

Trajectory data often suffer from sparsity and a lack of ground truth data, presenting challenges for map-matching.Traditional map-matching approaches struggle with sparse data, and deep learning methods are hindered by data scarcity.To address these issues, we propose a two-stage map-matching method.In the first stage, we use synthetic trajectory data to pre-train a deep learning model, overcoming data scarcity.We introduce a token mixing scheme during pre-training to clarify the interrelations between GPS-based and segment-based trajectory data.In the second stage, we fine-tune the model with limited ground truth data using a Transformer architecture.Empirical evaluations show that our model achieves 3.69%, 2.21% average precision and recall improvements over the best baseline model with various sparseness.

In the realm of human mobility data analysis, a multitude of constraints result in the publication of sparse, non-uniform implicit trajectories without explicit location information, such as coordinates. Researchers have dedicated substantial efforts towards trajectory recovery, aiming to densify trajectories and gain a more comprehensive understanding of human mobility. However, existing trajectory recovery methods focus on explicit trajectories, and require extensive historical data to capture users' mobility patterns. Nevertheless, implicit trajectories are usually more sparse than explicit trajectories. Addressing these challenges, we propose TrajBERT, an innovative BERT-based trajectory recovery method with spatial-temporal refinement. TrajBERT employs the Transformer encoder to learn mobility patterns bi-directionally and enhances the predictions by cross-stage temporal refinement. Subsequently, we design an output layer with global spatial refinement with a novel spatial-temporal aware loss function. To evaluate the performance of TrajBERT, we conduct a series of experiments on real-world datasets. Remarkably,TrajBERT yields at least 8.2% performance improvement compared to the state-of-the-art trajectory recovery approachs. Furthermore, TrajBERT successfully mitigates the cold start problem commonly experienced with new users lacking historical trajectories. It also shows superior robustness when faced with extremely sparse trajectories, thus demonstrating its potential as a practical tool in the field of human mobility analysis.

Trajectory clustering is a cornerstone task in trajectory mining. Sparse and noisy trajectories like Call Detail Records (CDR) have become popular with the rapid development of mobile applications. However, existing trajectory clustering methods' performance is limited on these trajectories. Therefore, we propose a dual Self-supervised Deep Trajectory Clustering (SDTC) method, to optimize trajectory representation and clustering jointly. First, we leverage the BERT model to learn spatial-temporal mobility patterns and incorporate them into the embeddings of location IDs. Second, we fine-tune the BERT model to learn cluster-friendly representations of trajectories by designing a dual self-supervised cluster layer, which improves the intra-cluster similarities and inter-cluster dissimilarities. Third, we conduct extensive experiments with two real-world datasets. Results show that SDTC improves the clustering accuracy by 12.1% (on a noisy and sparse dataset) and 3.8% (on a very sparse dataset) compared with SOTA deep clustering methods.

Mobile positioning plays an essential role in smart city services. This paper proposes a fingerprint positioning framework based on massive Minimization of Drive Test (MDT) data to provide accurate and efficient city-scale positioning without additional equipment and measures. First, a multi-level fingerprint construction method is proposed using the Timing Advance (TA), Reference Signal Receiving Power (RSRP), and Reference Signal Receiving Quality (RSRQ) of the serving cell and neighboring cell. Then, an adaptive online fingerprint matching method is employed to extract and match online data fingerprints. Experiments show that the median positioning error is 29.97 meters with city-scale MDT data. It outperforms the reported accuracy of the state-of-the-art fingerprint positioning method.

A large number of research studies have been focused on detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers and evaluate the performance of different detection techniques. Although several Android malware benchmarks have been widely used in our research community, these benchmarks face several major limitations. First, most of the existing datasets are outdated and cannot reflect current malware evolution trends. Second, most of them only rely on VirusTotal to label the ground truth of malware, while some anti-virus engines on VirusTotal may not always report reliable results. Third, all of them only contain the apps themselves (apks), while other important app information (e.g., app description, user rating, and app installs) is missing, which greatly limits the usage scenarios of these datasets. In this paper, we have created a reliable Android malware dataset based on Google Play's app maintenance results over several years. We first created four snapshots of Google Play in 2014, 2015, 2017 and 2018 respectively. Then we use VirusTotal to label apps with possible sensitive behaviors, and monitor these apps on Google Play to see whether Google has removed them or not. Based on this approach, we have created a malware dataset containing 9,133 samples that belong to 56 malware families with high confidence. We believe this dataset will boost a series of research studies including Android malware detection and classification, mining apps for anomalies, and app store mining, etc.

In this paper, we propose a new frame level rate control algorithm for the high efficiency video coding (HEVC) based 3D video (3DV) compression standard. In the proposed scheme, a new initial quantization parameter (QP) decision scheme is provided, and the bit allocation for each view is investigated to smooth the bitrate fluctuation and reach accurate rate control. Meanwhile, a simplified complexity estimation method for the extended view is introduced to reduce the computational complexity while improves the coding performance. The experimental results on 3DV test sequences demonstrate that the proposed algorithm can achieve better R-D performance and more accurate rate control compared to the benchmark algorithms in HTM10.0. The maximum performance improvement can be up to 12.4% and the average BD-rate gain for each view is 5.2%, 6.5% and 6.6% respectively.

In this work, a CTU level rate control algorithm is proposed for High Efficiency Video Coding (HEVC). On top of the CTU level rate control algorithm, an efficient bit allocation method considering the HEVC hierarchical coding structure is specifically designed. Instead of directly applying the rate distortion model at the CTU level, it's proposed to derive the CTU level quantization parameter (QP) based on the frame level QP with feedback of the coding status of CTUs. To further improve the coding performance of the CTU level rate control, a QP adjustment strategy is proposed and incorporated into the rate control algorithm. Experimental results show that the proposed CTU level rate control algorithm can achieve better coding performance than the state-of-the-art rate control scheme for HEVC (1.9% for RA, 3.2% for LD). Moreover, the coding quality become much smoother with the proposed algorithm.

This paper proposes a coding tree unit (CTU) level rate control for HEVC based on the Laplace distribution modeling of the transformed residuals. Firstly, we give a study on the relationship model among the optimal quantization step, the Laplace parameter and the Lagrange multiplier. Based on the relationship model, the quantization parameter for each CTU can be dynamically adjusted according to the distribution of the transformed residual. Secondly, a CTU level rate control scheme is proposed to achieve accurate rate control as well as high coding performance. Experimental results show that the proposed rate control scheme achieves better coding performance than the state-of-the-art rate control schemes for HEVC in terms of both objective and subjective quality.

This paper gives a study on the rate distortion modeling for High Efficiency Video Coding (HEVC) and new rate distortion models are proposed by considering the coding framework change of HEVC. Although HEVC is also characterized by the traditional prediction/transform hybrid coding framework with more improved coding tools, e.g. large size coding unit (CU), prediction unit (PU) and transform unit (TU), two new coding modules are added as sample adaptive offset(SAO) and adaptive loop filter(ALF). SAO and ALF are added after the motion compensation reconstruction, which would reduce the distortion of the reconstructed image. With considerations on these changes, new rate distortion models are proposed. For rate modeling, this paper proposes a separate model for header bits and coefficient bits respectively. For distortion modeling, the quantization distortion and the distortion reduction induced by SAO/ALF are modeled jointly. The experimental results show that the coding rate and distortion can be estimated by the proposed model accurately. These models can be used for rate distortion cost estimation for mode decision or rate control in the future.

A frame level adaptive rate control scheme for the emerging High Efficiency Video Coding (HEVC) standard is proposed in this paper, where both rate model and distortion model are provided. For rate modeling, a new rate model is proposed based on the weighted complexity estimation of the previously encoded frames. For distortion modeling, the distortion is modeled as an exponential function of the sum of absolute transformed difference (SATD) and the quantization parameter of the current frame. Moreover, a quality smoothing method based on the distortion model is proposed to reduce the quality fluctuation. The proposed rate control algorithm is implemented into HM5.0. The proposed scheme can control the bitrate accurately with smoothing quality, and the coding gain compared with state-of-the-art technique is up to 0.64dB for LB HE & LB LC, 0.33dB, 0.31dB, 0.42dB, 0.44dB for LP HE, LP LC, RA HE and RA LC respectively.