Multi-modal Entity Alignment (MMEA) aims to identify equivalent entities across diverse knowledge graphs by leveraging structural, attribute, and visual information. However, real-world datasets frequently suffer from missing modalities, necessitating feature imputation. A critical yet underexplored issue is that not all imputed modalities are inherently trustworthy. Ignoring the aleatoric uncertainty of such modalities introduces severe noise which propagates through the fusion process and degrades alignment performance. To address this challenge, we propose a novel MMEA framework, namely SURE, to Suppress Uncertainty for tRustworthy Entity alignment. Specifically, SURE introduces an uncertainty-aware variational imputation module to estimate the aleatoric uncertainty of generated features. Crucially, rather than using these imputation features blindly, SURE leverages the estimated uncertainty to suppress noise propagation via a confidence-gated multi-modal fusion and an adaptive contrastive learning objective. Extensive experiments on DBP15K datasets demonstrate that SURE significantly outperforms state-of-the-art baselines, exhibiting exceptional robustness particularly in scenarios with high modality missing rates.

In recent years, large language models (LLMs) have emerged as powerful tools for link prediction in knowledge graphs (KGs) due to their strong capabilities in understanding and generation. However, many LLM-based methods still heavily rely on textual descriptions of KGs, limiting their ability to capture structural information and to model complex relational patterns. Although some methods integrate structural embeddings into LLMs, their ability to harness the complementary strengths of both modalities and dynamically prioritize candidate entities based on query context remains limited. In this paper, we propose ST-KGLP, a novel framework that improves link prediction by aligning structural knowledge with textual knowledge and employing query-aware adaptive weighting for candidate selection. Specifically, our proposed ST-KGLP employs a knowledge aligner to bridge the information gap between structural and textual knowledge, and then utilizes a query-aware adaptive weighting strategy that dynamically computes attention weights between query representations and candidate entities, enabling contextually relevant candidate re-ranking for more accurate prediction. Extensive experiments on various datasets show that our ST-KGLP outperforms state-of-the-art approaches, achieving average improvements of 3.81%, 11.52%, 2.22%, and 1.55% across four evaluation metrics. Our code and datasets are available at https://github.com/shijielaw/ST-KGLP.

To achieve multi-hop question answering over knowledge graphs (KGQA), many studies have explored converting retrieved subgraphs into textual form and feeding them into large language models (LLMs) to leverage their reasoning capabilities. However, due to the linear and discrete nature of text sequences, model performance may degrade when handling complex questions. To this end, we propose a novel structure-text knowledge synergistic method, BrikQA, which bridges the knowledge gap between knowledge graphs (KGs) and LLMs for multi-hop KGQA. LLMs and KGs complement each other by leveraging explicit topological patterns and implicit knowledge mining to enhance knowledge understanding and address sparsity issues. Experimental results on various datasets demonstrate that BrikQA outperforms state-of-the-art baselines. Our source code is available at https://github.com/shijielaw/BrikQA.

Numerical simulation is important in real-world engineering systems, such as solid mechanics and aero-dynamics. Hierarchical GNNs can learn engineering simulation with low simulation time and acceptable accuracy, but fail to represent complex interactions in simulation systems. In this paper, we propose a novel multilevel Transformer on learnable clusters, namely SimFormer. The key novelty of SimFormer is to interweave the learning of a learnable soft-cluster assignment algorithm and the inter-cluster/cluster-to-node attention. In form of a closed-loop, SimFormer learns the soft cluster assignment possibility by the feedback signals provided by the attention, and the attention can leverage the learnable clusters to better represent long-range interactions. In this way, the learnable clusters can adaptively match actual simulation results, and the multilevel attention modules can also effectively represent node embeddings. Experiments on four datasets demonstrate the superiority of SimFormer over seven baseline approaches. For example, on the real dataset, ours outperforms the recent work Eagle by 17.36% lower RMSE and 27.03% smaller FLOPs. The code and datasets are available at: https://github.com/pro-orp/SimFormer.

Blood glucose levels, when analyzed as time series data, contain vital information about a patient's glucose fluctuations and trends, which are crucial for assessing pancreatic function and insulin sensitivity. Leveraging these insights through feature extraction and classification can help determine the optimal insulin dosage for patients. Traditional methods of manual feature selection and extraction are laborintensive and inefficient. In this paper, we propose a novel local-global context encoding architecture to extract deep features from blood glucose time series data. We employ a weighted k-nearest neighbors (KNN) classifier to provide dynamic and personalized treatment recommendations. Our experiments, conducted using the UVA/Padova type 1 diabetes simulator and the ShanghaiT2DM real-world diabetes dataset, demonstrate that deep features significantly outperform manually extracted features in recommending insulin dosages. Our approach offers a more accurate and personalized method for managing multiple daily insulin injections, potentially reducing the workload of medical practitioners and improving the efficiency and effectiveness of diabetes management. Code is available at https://github.com/EricWvi/ykw.

Recently, deep reinforcement learning (DRL) has been employed for intelligent traffic‐light control and demonstrated promising results. However, state‐of‐the‐art DRL‐based systems still rely on discrete decision‐making, which can lead to unsafe driving practices. Additionally, existing feature representations of the environment often fail to capture the complex dynamics of traffic flows, resulting in imprecise predictions of traffic conditions. To address these issues, we propose a novel DRL framework based on the multiagent deep deterministic policy gradient algorithm. Our method offers several key innovations: it suggests employing a transitional phase before changing the current phase for safer traffic management, integrates local road network topology into feature representation to enhance the accuracy of traffic flow predictions, and uses two‐layer regional features to improve coordination among agents within the region. Our extensive evaluations using simulation of urban mobility, a widely used multimodal traffic simulation package, demonstrated that the proposed method outperformed previous methods and reduced the number of emergency stops, queue lengths, and waiting times.

As the number of vehicles grows in urban cities, planning vehicle routes to avoid congestion and decrease commuting time is important. In this paper, we study the shortest path problem (SPP) withmultiplesource-destination pairs, namely MSD-SPP, to minimize the average travel time of all routing paths. The asynchronous setting in MSD-SPP, i.e., vehicles may not simultaneously complete routing actions, makes it challenging for cooperative route planning among multiple agents and leads to ineffective route planning. To tackle this issue, in this paper, we propose a two-stage framework of inter-region and intra-region route planning by dividing an entire road network into multiple sub-graph regions. Next, the proposed asyn-MARL model allows efficient asynchronous multi-agent learning by three key techniques. Firstly, the model adopts a low-dimensional global state to implicitly represent the high-dimensional joint observations and actions of multi-agents. Secondly, by a novel trajectory collection mechanism, the model can decrease the redundancy in training trajectories. Additionally, with a novel actor network, the model facilitates the cooperation among vehicles towards the same or close destinations, and a reachability graph can prevent infinite loops in routing paths. On both synthetic and real road networks, the evaluation result demonstrates that asyn-MARL outperforms state-of-the-art planning approaches.

Using Graph Neural Networks (GNNs) to simulate complex physical systems is currently a popular field. Finite Element Analysis (FEA) is a numerical analysis solution in physical simulation and involves dividing a physical structure into finite elements. A mesh-based graph network is formed by interconnecting the nodes of the finite elements. Mesh-based simulations are central to the modeling of physical systems, and GNNs are used to overcome the high cost of the simulation. However, most existing GNNs-based methods lack the ability to generalize when small changes in structure occur. The cantilever beam, which is a fundamental component of many physical structures, is a classic study case for FEA stress analysis. In this paper, we propose a Mesh-based Graph Transfer Learning (MGTL) method for beams, in which the knowledge learned on a mesh-based graph network of one beam structure via contrastive learning, and then is transferred to another. Furthermore, an augmentation approach is proposed for the mesh-based graph in contrastive learning. Our experiments demonstrate the effectiveness of transfer learning in the MGTL method, as well as its generalization ability across different mesh sizes and structure types.

With the burgeoning prevalence of diabetes and the urgent imperative for effective dietary management, harnessing the power of image analysis and computer vision technology presents a promising solution to the automatic nutrition estimation challenge. However, constructing a comprehensive frame-work for this endeavor presents its own set of challenges, encompassing multiple intricate procedures, including data collection, image segmentation, food recognition, and volume estimation. In this paper, we introduce a sophisticated and automated framework for Chinese food nutrition estimation, leveraging food images sourced from diabetic patients. In addition to developing models for the various procedures, the framework necessitates label images for model training. These label images encompass ground truth masks delineating precise object boundaries for image segmentation, food type labels for food recognition, and comprehensive nutrition tables for each food item. Through rigorous experimentation and validation, our framework has demonstrated its efficacy, offering a convenient and practical tool for managing dietary requirements in diabetes care.

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

The constrained shortest path ($\mathtt {CSP}$) problem has wide applications in travel path planning, mobile video broadcasting and network routing. Existing works do not work well on large dynamic graphs and suffer from either ineffectiveness or low scalability issues. To overcome these issues, in this paper, we propose an efficient and effective solution framework, namely$\mathtt {CSP\_{G}S}$. The solution framework includes two key components: (1) the techniques to decompose a large$\mathtt {CSP}$instance into multiple small sub-instances and (2) the developed learning model$\mathtt {CSP\_{D}QN}$to solve small$\mathtt {CSP}$instances. The evaluation result on real road network graphs indicates that our approach$\mathtt {CSP\_{G}S}$performs well on large dynamic graphs by rather high quality and reasonable running time, and particularly adapt to significant graph changes even with broken edges. To the best of our knowledge, this is the first learning-based model to well solve the$\mathtt {CSP}$problem on large dynamic graphs.

In this paper, we study the collaborative detection problem in a multi-agent environment. By exploiting onboard range-bearing sensors, mobile agents make sequential control decisions such as moving directions to gather information of movable targets. To estimate target states, i.e., target location and velocity, the classic works such as Kalman Filter (KF) and Extended Kalman Filter (EKF) impractically assume that the underlying state space model is fully known, and some recent learning-based works, i.e., KalmanNet, estimate target states alone but without estimation uncertainty, and cannot make robust control decision. To tackle such issues, we first propose a neural network-based state estimator, namely TWo-phase KALmanFilter withUncertainty quanTification (WALNUT), to explicitly give both target states and estimation uncertainty. The developed multi-agent reinforcement learning (MARL) model then takes the learned target states and uncertainty as input and makes robust actions to track movable targets. Our extensive experiments demonstrate that our work outperforms the state-of-the-art by higher tracking ability and lower localization error.

Complex physical system simulation is important in many real world applications. We study the general simulation scenario to generate the response result when a physical object is applied by external factors. Traditional solvers on Partial Differential Equations (PDEs) suffer from significantly high computational cost. Many recent learning-based approaches focus on multivariate time series alike simulation prediction problem and do not work for our case. In this paper, we propose a novel two-level graph neural networks (GNNs) to learn the simulation result of a physical object applied by external factors. The key is a two-level graph structure where one fine mesh graph is mapped to multiple coarse one. Our preliminary evaluation on both synthetic and real datasets demonstrates that our work outperforms three state-of-the-arts by much lower errors.

The collaborative detection problem has been widely used in many applications. Existing works typically exploit a Kalman Filter or its variant to estimate target state with an impractical assumption that the state space and environment are fully known. To address this issue, we propose a novel multi-agent reinforcement learning (MARL) based collaborative detection framework. The key is (1) a Two-Phase Kalman Neural Network (TPKN) to estimate target state and (2) a reinforcement learning (RL) model by taking the target estimation state as input and generating an action to track targets. Our initial evaluation with 4 pursuer agents and 4 targets demonstrates that our framework outperforms the state-of-the-art by a much higher tracking ability and lower localization error.11Weixiong Rao and Feng Ye are joint corresponding authors of the paper.

Communication is an effective mechanism to coordinate the behavior of mobile multi-agents. We propose a general multi-agent communication framework. With help of graph neural networks and reinforcement learning, this framework can provide a cooperative detection method with more powerful capabilities. In this paper, we demonstrate the configurable co-detection simulation system. Moreover, the evaluation of the multi-agent communication framework validates that the agents can effectively cooperate with each other through decentralized communication, and effectively reduce computation overhead meanwhile with high scalability.

Communication is an effective mechanism to coordinate the behavior of mobile multi-agent systems. We propose a general mobile multi-agent cooperative detection framework, which provides a detection system with enhanced collaboration capabilities based on graph neural networks and reinforcement learning. Each agent gains information about the surrounding area by heuristic KNN, and then exchanges this part of subgraph information through communication so as to obtain global information in a decentralized way. At the same time, to avoid the computation overhead caused by the increase of the number of agents, attention mechanism is used to filter and optimize the communication process to improve scalability. We evaluate our method on large-scale detection tasks. Our approach is able to outperform the baselines, while making superior communication efficiency1.

Multivariate time series forecasting has wide applications such as traffic flow prediction, supermarket commodity demand forecasting and etc., and a large number of forecasting models have been developed. Given these models, a natural question has been raised: what theoretical limits of forecasting accuracy can these models achieve? Recent works of urban human mobility prediction have made progress on the maximum predictability that any algorithm can achieve. However, existing approaches on maximum predictability on the multivariate time series fully ignore the interrelationship between multiple variables. In this article, we propose a methodology to measure the upper limit of predictability for multivariate time series with multivariate constraint relations. The key of the proposed methodology is a novel entropy, named Multivariate Constraint Sample Entropy (McSE), to incorporate the multivariate constraint relations for better predictability. We conduct a systematic evaluation over eight datasets and compare existing methods with our proposed predictability and find that we get a higher predictability. We also find that the forecasting algorithms that capture the multivariate constraint relation information, such as GNN, can achieve higher accuracy, confirming the importance of multivariate constraint relations for predictability.

Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. To this end, we propose a multi-task learning-based deep neural network (DNN) framework, namely${{\sf PRNet}}$$^+$, to incorporate outdoor position recovery and transportation mode detection. To make sure that${{\sf PRNet}}$$^+$can work, we develop a feature extraction module to precisely learn local-, short- and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that${{\sf PRNet}}$${^{+}}$greatly outperforms state-of-the-arts by lower localization errors.

For multi-class food images, an excellent segmentation method has a great influence on accuracy of recognition result, and hence improve the effectiveness of dietary management for diabetics. For Chinese food images, there are some challenges during the processing, such as blurred outlines, rich colors, and varied appearances due to various cooking methods. To overcome these difficulties, we propose a ChineseFoodSeg approach to obtain accurate and efficient multi-class segmentation. A food recognition model, Two-Path Global Local Network (TPGLNet), is also introduced to jointly learn complementary global and local features of the bounding box and the segment. Experiments on the ChineseDiabetesFood187 dataset collected by us demonstrate that the new methods are competitive compared to existing segmentation and recognition methods.

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely${\sf RLoc}$, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work${\sf RLoc}$can greatly improve Telco location accuracy. For example,${\sf RLoc}$on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4 percent better than state-of-the-art.

Content-based Pub/Sub paradigm has been widely used in many distributed applications and existing approaches suffer from high redundancy subscription index structure and low matching efficiency. To tackle this issue, in this paper, we propose a learning framework to guide the construction of an efficient in-memory subscription index, namely PMIndex, via a multi-task learning framework. The key of PMIndex is to merge redundant subscriptions into an optimal number of partitions for less memory cost and faster matching time. Our initial experimental result on a synthetic dataset demonstrates that PMindex outperforms two state-of-the-arts by faster matching time and less memory cost.

Web users provide rich multi-modal and heterogeneous data. Taking use of the data for extracting the representation of web users is a vital prerequisite of further profiling tasks. In this paper, we study the web user profiling based on the user-entity interaction data integrating the taxonomy information of the entity. To overcome the challenges of the representation of a web user and the distance definition of two web users, we introduce TaxoVec, an interpretable method to represent a user’s interaction history by vectors with the help of the entities’ category information. The TaxoVec is employed in clustering-based web user profiling on their transaction data and geographical data, which provide the information on their interests and activity area. Experiments show that the TaxoVec performs well on representing characteristics of web users.

Now indoor Location-Based Services (LBSs) have numerous applications built on indoor navigation. However, current indoor navigation can only meet the needs of finding a certain point of interest (POI), it can not help to find a certain person. Therefore, we propose Location Paging and Answering Service (LPAS) for finding out someone in an indoor environment. However, it may face serious problems of privacy disclosure of the clients' indoor location data. To ensure security, privacy and convenience, we leverage blockchain to model the indoor environment, and build Blockchain-Based Indoor LPAS (BILPAS) by embedding our three-way-handshake Diffie-Hellman Key Agreement (DHKA) procedures. Our BILPAS can automatically establish a secure and privacy communication tunnel between any two users, greatly liberating participants from unnecessary self-determining interactions. We present the proof-of-concept prototype and implement it on Hyperledger Fabric. Then, we conduct the security and privacy analysis and evaluate the time overhead. The experiment results validate the high privacy, security and convenience of our BILPAS.

The shortest path problem (SPP) in graph theory has wide applications in daily travels, transportation and network routing. Existing works do not work well on large dynamic graphs and suffer from either low scalability or gealization issue. To overcome these issues, in this paper, we propose an efficient and effective learning framework, namely SPP-GS, to solve the SPP problem on large dynamic graph. The key components of SPP- GS include the techniques to decompose a large SPP instance into multiple small instance and the developed GCN-DQN model to solve small SPP instances. The evaluation result on 7 real road network graphs indicates that our approach SPP-GS performs well on large dynamic graphs by rather high quality and reasonable running time.

The proliferation of telco networks and mobile terminals brings the accumulation of tremendous amounts of measure report(MR) data at a rapid pace. The MR data is generated by mobile objects while connecting to data services and is stored in backend data centers. To geo-tag or localize such MR data is believed to have a profound effect on the analytics and optimizations of telco and traffic networks. However, MR records are of noisy and partial observations regarding to mobile objects' geo-locations and hence pose challenges to accurate telco data localization. There have been quite a few attempts. Single-point localization methods map a MR record to a location, but come out with limited accuracies due to the ignorance of spatiotemporal coherence of successive MR records. Recent efforts on sequential localization techniques alleviate this by mapping a sequence of MR records to a trajectory. However, existing solutions are often with assumptions on specific models, e.g., mobility and signal strength distributions, or priori knowledge on topology space, e.g., road networks, limiting the deployment in practice. To this end, we propose a data-driven framework to tackle the challenges in sequential telco localization. We solely use raw MR records and a public third-party GPS dataset for the learning of the correlations between mobile objects' locations and MR records, requiring no model assumptions and priori knowledge. To handle the data-intensive workloads during the learning process, we use materialized views for efficient online localization and light-weighted indexing techniques for periodical parameters tuning, in order to improve the efficiency and scalability. Results on real data show that our solution achieves 58.8 percent improvement in median localization errors compared with state-of-art sequential localization techniques that require hypothesis models and priori knowledge, making our solution superior in terms of effectiveness, efficiency, and employability.

Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy. Specifically, TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, TLoc outperforms a non-transfer approach by 27.58 and 26.12 percent less median errors, and further leads to 47.77 and 49.22 percent less median errors than a recent fingerprinting approach NBL.

Recently deep reinforcement learning (DRL) has been used for intelligent traffic light control. Unfortunately, we find that state-of-the-art on DRL-based intelligent traffic light essentially adopts discrete decision making and would suffer from the issue of unsafe driving. Moreover, existing feature representation of environment may not capture dynamics of traffic flow and thus cannot precisely predict future traffic flows. To overcome these issues, in this paper, we propose a DDPG-based DRL framework to learn a continuous time duration of traffic signal phases by introducing 1) a transit phase before the change of current phase for better safety, and 2) vehicle moving speed into feature representation for more precise estimation of traffic flow in next phase. Our preliminary evaluation on a well-known simulator SUMO indicates that our work significantly outperforms a recent work by much smaller number of emergency stops, queue length and waiting time.

Understanding the fine-grained distribution of telecommunication (Telco) signals in terms of a three-dimensional (3D) space is important for Telco operators to manage, operate and optimize Telco networks. It is particularly true in nowadays urban cities with a large number of high buildings. One of the key tasks is to infer the location height of mobile devices, e.g., the floor within a high building where mobile devices are located. However, precise height estimation is challenging due to complex Telco signal propagation within an indoor 3D space, sparse cell tower deployment and scarce training samples. To tackle these issues, in this paper, we propose an indoor MR height estimation framework, namely IFLoc, via a machine learning model. IFLoc first builds a training MR database via a pre-processing step to comfortably tag raw MR samples by precisely inferred height from auxiliary data such as GPS and barometer readings. Next, IFLoc trains a regression model for height estimation by a set of developed techniques including 3D space division, post-processing techniques, feature augmentation and an improved SVR (Supported Vector Regression) model. Our evaluation on eight real datasets collected within five representative high buildings in Shanghai validates that IFLoc outperforms state-of-the-art counterparts in particularly with scarce training data.

Multivariate time series forecasting has wide applications such as traffic flow prediction, supermarket commodity demand forecasting and etc. In literature, Due to the complex temporal patterns and inter-dependencies among multivariate time series, a large number of forecasting models have been developed. However, one question still remains unclear: how these models perform on a certain forecasting task, and there is lack of comprehensive performance comparison of these models on different tasks. To this end, in this paper, we conduct a systematic evaluation of eight representative forecasting models over eight multivariate time series datasets, and have the following findings: 1) When the datasets exhibit strong periodic patterns, deep learning models perform best. Otherwise on the datasets in a non-periodic manner, the statistical models such as ARIMA perform best. 2) For the long term prediction involving a high horizon value, the direct prediction strategy could lead to lower errors than the recursive one, but at the cost of higher training time. 3) For the multivariate time series explicitly involving graphic inter-dependencies among the multivariates, e.g., the road network topology in the spatio-temporal time series of traffic volumes in multiple routes, the Graph Convolution Network can incorporate the graphic inter-dependencies into their forecasting models for smaller prediction errors.

Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco networks. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength, between mobile devices and Telco networks. MR data have been widely used to precisely recover outdoor locations of mobile devices for the applications e.g., human mobility, urban planning and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture the spatio-temporal locality in underlying mobility patterns for lower localization errors. Such HMM approaches typically assume stable mobility pattern of underlying mobile devices. Yet real MR datasets frequently exhibit heterogeneous mobility patterns due to mixed transportation modes of underlying mobile devices and uneven distribution of the positions associated with MR samples. To address this issue, we propose a deep neural network (DNN)-based position recovery framework, namely PRNet, which can ensemble the power of CNN, sequence model LSTM, and two attention mechanisms to learn local, short- and long-term spatio-temporal dependencies from input MR samples. Extensive evaluation on six datasets collected at three representative areas (core, urban, and suburban areas in Shanghai, China) indicates that PRNet greatly outperforms seven counterparts.

Nowadays, transaction data of users on the network platform has become important raw materials to analyze their purchase behaviors and consumption patterns. In fields such as advertising and marketing, it is important to identify certain groups of users to target. Therefore, an automatic user categorization method is meaningful. In this paper, we introduce a user categorization method on e-commerce transaction data based on the products that users are interested in. The categorization method introduces a novel distance metric for two users and an efficient clustering algorithm based on it. We conduct our experiments on the yelp website, and the dataset with 1.2 million users are tested. Compared with two commonly used clustering algorithms, the proposed method shows the best efficiency, which is suitable for large data sets. Furthermore, the clusters of users obtained are summarized by keywords which are extracted from their review comments.

Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.

The volume of trajectory data has become tremendously large in recent years. How to efficiently maintain and compute such trajectory data becomes a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road networks. By treating the mined patterns as dictionary items, we have the chance to encode a long trajectory by shorter paths, thus leading to smaller space cost. Meanwhile, we design an error-bounded temporal compression on top of the identified spatial patterns for much low space cost. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of both space saving and runtime of trajectory compression.

Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco radio and core equipment. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength at the mobile device, when mobile devices give phone calls or access data services. Telco historical data (e.g., MRs) have been widely analyzed to understand human mobility and optimize the applications such as urban planning and traffic forecasting. The key of these applications is to precisely localize outdoor mobile devices from these historical MR data. Previous works calculate the location of a mobile device based on each single MR sample, ignoring the sequential and temporal locality hidden in the consecutive MR samples. To address the issue, we propose a deep neural network (DNN)-based localization framework namely DeepLoc to ensemble a recently popular sequence learning model LSTM and a CNN. Without skillful feature design and post-processing steps, DeepLoc can generate a smooth trajectory consisting of accurately predicted locations. Extensive evaluation on 6 datasets collected at three representative areas (core business, urban and suburban areas in Shanghai, China) indicates that DeepLoc greatly outperforms 10 counterparts.

Map-matching is an important preprocessing task for many location-based services (LBS). It projects each GPS point in trajectory data onto digital maps. The state of art work typically employed the Hidden Markov model (HMM) by shortest path computation. Such shortest path computation may not work very well for very low sampling rate trajectory data, leading to low matching precision and high running time. To solve this problem, this paper, we first identify the frequent patterns from historical trajectory data and next perform the map matching for higher precision and faster running time. Since the identified frequent patterns indicate the mobility behaviours for the majority of trajectories, the map matching thus has chance to satisfy the matching precision with high confidence. Moreover, the proposed FP-forest structure can greatly speedup the lookup of frequent paths and lead to high computation efficiency. Our experiments on real world data set validate that the proposed FP-matching outperforms state of arts in terms of effectiveness and efficiency.

GPS sensors have been widely used to track people's everyday life trajectories, generating massive trajectory datasets. The trajectory data typically contains sparse GPS points, and completing trajectories is often necessary. State-of-the-art methods [3, 4] essentially complete the entire route by using a single metric, e.g., either the shortest distance or the fastest driving/walking time. Unfortunately, using a single metric may not always work in real life due to the diversity of mobility patterns. In this demo abstract, we propose a frequent pattern (FP)-based trajectory completion approach, and demonstrate a system prototype to showcase the advantages of our approach over four previous works, in terms of accuracy and running time.

When Telecommunication (Telco) networks provide phone call and data services for mobile users, measurement record (MR) data is generated by mobile devices during each call/session. MR data reports the connection states, e.g., signal strength, between mobile devices and nearby base stations. Given the MR data, the literature has proposed various Telco localization approaches, to localize mobile devices. Unfortunately, such approaches typically estimate the individual position independently, and could compromise the temporal and spatial locality in underlying mobility patterns. To address this issue, in this paper, we propose a deep neural network-based localization approach, namely RecuLSTM, to automatically extract contextual features and predict the positions of mobile devices from an input sequence of MR data. Our preliminary experiment validates that RecuLSTM greatly outperforms three recent works [1, 2, 4] which suffer from 3.2×, 1.91× and 3.56× median errors on the dataset in a 2G GSM suburban area, respectively.

Superseding HTTP/1.1, the dominating web protocol, HTTP/2 promises to make web applications faster and safer by introducing many new features, such as multiplexing, header compression, request priority, server push, etc. Although a few recent studies examined the adoption of HTTP/2 and evaluated its impacts, little is known about whether the popular HTTP/2 servers have correctly realized the new features and how the deployed servers use these features. To fill in the gap, in this paper, we conduct the first systematic investigation by inspecting six popular implementations of HTTP/2 servers (i.e., Nginx, Apache, H2O, Lightspeed, nghttpd and Tengine) and measuring the top 1 million Alexa web sites. In particular, we propose new methods and develop a tool named H2Scope to assess the new features in those servers. The results of the large-scale measurement on HTTP/2 web sites reveal new observations and insights. This study sheds light on the current status and the future research of HTTP/2.

Telecommunication (Telco) localization is a technique to accurately locate mobile devices (MDs) using measurement report (MR) data, and has been widely used in Telco industry. Many techniques have been proposed, including measurement-based statistical algorithms, fingerprinting algorithms and different machine learning-based algorithms. However, it has not been well studied yet on how these algorithms perform on various Telco MR data sets. In this paper, we conduct a comprehensive experimental study of five state-of-art algorithms for Telco localization. Based on real data sets from two Telco networks, we study the localization performance of such algorithms. We find that a Random Forest-based machine learning algorithm performs best in most experiments due to high localization accuracy and insensitivity to data volume. The experimental result and observation in this paper may inspire and enhance future research in Telco localization.

Telecommunication (Telco) localization is a technique to localize mobile devices (MDs) outdoor by using measurement report (MR) data. Unfortunately, existing Telco localization approaches (with localization error > 50 meters) cannot achieve comparable localization accuracy as GPS (with localization error ±10 meters). In particular, due to signal interference and attenuation caused by high buildings in urban cities, it is hard to achieve high localization accuracy if MR records contain unstable signal data. To this end, we propose to first detect those MR records incurring high localization errors and next repair the predicted location (with high errors). Our experiments on two real MR data sets from 2G GSM and 4G LTE Telco networks verify that a Telco localization approach, enhanced by the proposed detection and repair algorithms, can greatly improve localization accuracy. For example, the enhancement algorithm on 2G and 4G data sets can achieve 29.5 and 13.2 meters of median errors, around 160.68% and 201.51% better than previous results. Such result indicates that our work can achieve nearly comparable localization accuracy as GPS.

Recent years witnessed popular use of various mobile devices, e.g., smart phones, vehicle networks and wearable watches. Such mobile devices generate massive trajectory data, and literature have proposed various algorithms to leverage the trajectory data for map inference. Unfortunately, such algorithms are hard to achieve both high map quality and computation efficiency. In this paper, we propose a solution framework to infer road network maps with high quality and efficiency. The key of our map inference is to divide map extent into smaller cells and maintain a binary cell-trajectory matrix. The binary matrix determines whether or not a trajectory passes a cell. We infer the importance of each cell from the matrix using a popular topic model (e.g., LDA [13] and pLSA [8]). Based on such computed importance, we next infer representative points and road segments to derive a road network map. Our extensive experiments on real data sets verify that the proposed inference algorithm can achieve higher map quality and meanwhile 1.5 ×, 6.8 × and 280 × shorter running time, when compared with three state of the arts including three representative work [4], [7], [14].

Inference algorithms of latent Dirichlet allocation (LDA), either for small or big data, can be broadly categorized into expectation-maximization (EM), variational Bayes (VB) and collapsed Gibbs sampling (GS). Looking for a unified understanding of these different inference algorithms is currently an important open problem. In this paper, we revisit these three algorithms from the entropy perspective, and show that EM can achieve the best predictive perplexity (a standard performance metric for LDA accuracy) by minimizing directly the cross entropy between the observed word distribution and LDA's predictive distribution. Moreover, EM can change the entropy of LDA's predictive distribution through tuning priors of LDA, such as the Dirichlet hyperparameters and the number of topics, to minimize the cross entropy with the observed word distribution. Finally, we propose the adaptive EM (AEM) algorithm that converges faster and more accurate than the current state-of-the-art SparseLDA [20] and AliasLDA [12] from small to big data and LDA models. The core idea is that the number of active topics, measured by the residuals between E-steps at successive iterations, decreases significantly, leading to the amortized σ(1) time complexity in terms of the number of topics. The open source code of AEM is available at GitHub.

It is still challenging in telecommunication (telco) industry to accurately locate mobile devices (MDs) at city-scale using the measurement report (MR) data, which measure parameters of radio signal strengths when MDs connect with base stations (BSs) in telco networks for making/receiving calls or mobile broadband (MBB) services. In this paper, we find that the widely-used location based services (LBSs) have accumulated lots of over-the-top (OTT) global positioning system (GPS) data in telco networks, which can be automatically used as training labels for learning accurate MR-based positioning systems. Benefiting from these telco big data, we deploy a context-aware coarse-to-fine regression (CCR) model in Spark/Hadoop-based telco big data platform for city-scale localization of MDs with two novel contributions. First, we design map-matching and interpolation algorithms to encode contextual information of road networks. Second, we build a two-layer regression model to capture coarse-to-fine contextual features in a short time window for improved localization performance. In our experiments, we collect 108 GPS-associated MR records in the centroid of Shanghai city with 12 x 11 square kilometers for 30 days, and measure four important properties of real-world MR data related to localization errors: stability, sensitivity, uncertainty and missing values. The proposed CCR works well under different properties of MR data and achieves a mean error of 110m and a median error of $80m$, outperforming the state-of-art range-based and fingerprinting localization methods.

Nowadays large enterprises maintain a huge amount of data in multiple backend systems including traditional database systems and recently popular big data systems. In an example of telecom providers, the key business data (e.g., billing information) is maintained in database systems whereas the huge signaling log data is on HDFS with Hive. How to integrate such data and provide a consolidate query and analytic becomes a challenging task. Neither traditional database warehouse nor recent Big Data system (e.g. Apache Spark and Hadoop) can fully leverage the power of each backend system. In this paper, we build a hybrid data processing engine, called Octopus, to fully integrate backend systems. Given the backend systems, data is distributed at multiple locations. Octopus focuses on the optimization of the amount of data movement. To this end, Octopus proposes a technique of query pushdown for such optimization. A proof-of-concept prototype of Octopus successfully verifies that Octopus can achieve much faster running time than Spark. For example, Octopus outperforms the recent Spark version 1.4.0 by 5.25 X faster running time to process an aggregation query.

Many applications, such as product promotion advertisement and traffic congestion notification, benefit from opportunistic content exchange in Delay Tolerant Networks (DTNs). An important requirement of such applications is timely delivery. However, the intermittent connectivity of DTNs may significantly delay content exchange, and cannot guarantee timely delivery. The state-of-the-arts capture mobility patterns or social properties of mobile devices. Such solutions do not capture patterns of delivered content in order to optimize content delivery. Without such optimization, the content demanded by a large number of subscribers could follow the same forwarding path as the content by only one subscriber, leading to traffic congestion and packet drop. To address the challenge, in this paper, we develop a solution framework, namely Ameba, for timely delivery. In detail, we first leverage content properties to derive an optimal routing hop count of each content to maximize the number of needed nodes. Next, we develop node utilities to capture interests, capacity and locations of mobile devices. Finally, the distributed forwarding scheme leverages the optimal routing hop count and node utilities to deliver content towards the needed nodes in a timely manner. Illustrative results verify that Ameba achieves comparable delivery ratio as Epidemic but with much lower overhead.

Beyond offering the widely used keyword search function, many peer-to-peer systems nowadays support the subscription function. For example, Vuze allows users to create subscription filters based on the keyword search. Given the subscription, episodic or related content will be delivered to the users whenever new episodes are available. Unfortunately, these applications suffer from the downsides, for example, high network traffic in the nodes maintaining popular terms. In this paper, we propose the MTAF mechanism to overcome the issues. The key of MTAF is to carefully select a subset of terms without incurring false negatives and to forward the content item toward the home nodes of such selected terms for low content forwarding cost. Experimental results based on real datasets indicate that the proposed solutions are efficient compared to existing approaches. In particular, the similarity-based replication of filters is shown to mitigate the effect of hot spots that arise due to the fact that some document terms are substantially more popular than the others.

In recent years, content-based publish/subscribe (pub/sub) has become a popular paradigm to decouple content producers and consumers for Internet-scale content services. Many real applications show that the content workloads frequently exhibit very skewed distribution, and incur unbalanced workloads. To balance the workloads, the literature of content-based pub/sub adopted a migration scheme (Mis) to move (a subset of) subscription filters from overloaded brokers to underloaded brokers. In this way, the publications that successfully match the moved filters are then overloaded, leading to balanced workloads. Unfortunately, the scheme cannot reduce the overall matching workloads. In the worst case, suppose that all brokers suffer from heavy workloads. cannot find available brokers to offload the heavy workloads of those overloaded brokers, and fails to balance the workloads. To overcome the issue, the contribution of this paper is to develop a set of novel load balancing algorithms, namely a similarity-based replication scheme (Sir). The novelty of is that it not only balances the workloads of brokers but also reduces the overall workloads. Based on both simulation and emulation results, the extensive experiments verify that can achieve much better performance than, in terms of 43.10% higher entropy value (i.e., more balanced workloads) and 46.39% lower workloads.

Nowadays Web search engines are experiencing significant performance challenges caused by a huge amount of Web pages and increasingly larger number of Web users. The key issue for addressing these challenges is to design a compact structure which can index Web documents with low space and meanwhile process keyword search very fast. Unfortunately, the current solutions typically separate the space optimization from the search improvement. As a result, such solutions either save space yet with search inefficiency, or allow fast keyword search but with huge space requirement. In this paper, to address the challenges, we propose a novel structure bitlist with both low space requirement and supporting fast keyword search. Specifically, based on a simple and yet very efficient encoding scheme, bitlist uses a single number to encode a set of integer document IDs for low space, and adopts fast bitwise operations for very efficient boolean-based keyword search. Our extensive experimental results on real and synthetic data sets verify that bitlist outperforms the recent proposed solution, inverted list compression [23, 22] by spending 36.71% less space and 61.91% faster processing time, and achieves comparable running time as [8] but with significantly lower space.

In recent years, the content-based publish/subscribe [12], [22] has become a popular paradigm to decouple information producers and consumers with the help of brokers. Unfortunately, when users register their personal interests to the brokers, the privacy pertaining to filters defined by honest subscribers could be easily exposed by untrusted brokers, and this situation is further aggravated by the collusion attack between untrusted brokers and compromised subscribers. To protect the filter privacy, we introduce an anonymizer engine to separate the roles of brokers into two parts, and adapt the k-anonymity and `-diversity models to the contentbased pub/sub. When the anonymization model is applied to protect the filter privacy, there is an inherent tradeoff between the anonymization level and the publication redundancy. By leveraging partial-order-based generalization of filters to track filters satisfying k-anonymity and ℓ-diversity, we design algorithms to minimize the publication redundancy. Our experiments show the proposed scheme, when compared with studied counterparts, has smaller forwarding cost while achieving comparable attack resilience.

The Web 2.0 era is characterized by the emergence of a very large amount of live content. A real time and fine grained content filtering approach can precisely keep users up-to-date the information that they are interested. The key of the approach is to offer a scalable match algorithm. One might treat the content match as a special kind of content search, and resort to the classic algorithm [5]. However, due to blind flooding, [5] cannot be simply adapted for scalable content match. To increase the throughput of scalable match, we propose an adaptive approach to allocate (i.e, replicate and partition) filters. The allocation is based on our observation on real datasets: most users prefer to use short queries, consisting of around 2-3 terms per query, and web content typically contains tens and even thousands of terms per article. Thus, by reducing the number of processed documents, we can reduce the latency of matching large articles with filters, and have chance to achieve higher throughput. We implement our approach on an open source project, Apache Cassandra. The experiment with real datasets shows that our approach can achieve around folds of better throughput than two counterpart state-of-the-arts solutions.

The privacy protection of graph data has become more and more important in recent years. Many works have been proposed to publish a privacy preserving graph. All these works prefer publishing a graph, which guarantees the protection of certain privacy with the smallest change to the original graph. However, there is no guarantee on how the utilities are preserved in the published graph. In this paper, we propose a general fine-grained adjusting framework to publish a privacy protected and utility preserved graph. With this framework, the data publisher can get a trade-off between the privacy and utility according to his customized preferences. We used the protection of a weighted graph as an example to demonstrate the implementation of this framework.

Many applications, such as product promotion advertisement and traffic congestion notification, benefit from the opportunistic content exchange in Delay Tolerant Networks (DTNs). An important requirement of such applications is timely delivery. However, the intermittent connectivity of DTNs may significantly delay content exchange and cannot guarantee timely delivery. The state-of-the-arts capture the mobility patterns or social properties of mobile devices. However, there is little optimization in terms of the delivered content. Without such optimization, the content demanded by a large number of subscribers could follow the same forwarding path as the content by only one subscriber. To address the challenge, in this paper, we separate content routing from content forwarding. For content routing, we leverage content properties to derive an optimal routing hop count for each content in order to maximize the number of nodes which receive demanded content. Next, for timely forwarding, we develop node utilities to capture interests and mobility patterns of mobile devices for the selection of content carriers. The distributed greedy relay scheme, Ameba, leverages the optimal routing hop count and developed utilities to timely relay content to the needed nodes as fast as possible. Illustrative results show that Ameba is able to achieve comparable delivery ratio as the Epidemic but with much lower overhead.

Keyword-based content alert services, e.g., Google Alerts and Microsoft Live Alerts, empower the end users with the ability to automatically receive useful and most recent content. In this paper, we leverage the favorable properties of DHTs, such as scalability, and propose a design of a scalable keyword-based content alert service. The DHT-based architecture matches textual documents with queries based on document terms: For each term, the implementation assigns a home node that is responsible for handling documents and queries that contain the term. The main challenge of this keyword-based matching scheme is the high number of terms that appear in a typical document resulting in a high publication cost. Fortunately, a document can be forwarded to the home nodes of a carefully selected subset of terms without incurring false negatives. In this paper we focus on the MTAF problem of minimizing the number of selected terms to forward the published content. We show that the problem is NP-hardness, and consider centralized and DHT-based solutions. Experimental results based on real datasets indicate that the proposed solutions are efficient compared to existing approaches. In particular, the similarity-based replication of filters that is a key element of our solution is shown to mitigate the effect of hotspots that arise due to the fact that some document terms are substantially more popular than the others, both inside documents and queries.

Utilizing the skewed popularity distribution in P2P systems, common in Gnutella and KazaA like P2P applications, we propose an optimal resource (replica or link) placement strategy, which can optimally trade off the performance gain and paid cost. The proposed resource placement strategy, with better results than existing works, can be generally applied in randomized P2P systems (Symphony) and deterministic P2P systems (e.g., Chord, Pastry, Tapestry, etc.). We apply the proposed resource placement strategy, respectively, to two novel applications: PCache (a P2P-based caching scheme) and PRing (a P2P ring structure). The simulation results as well as a real deployment on Planetlab demonstrate the effectiveness of the proposed resource placement strategy in reducing the average search cost of the whole system.

Nowadays contents in Internet like weblogs, wikipedia and news sites become "live". How to notify and provide users with the relevant contents becomes a challenge. Unlike conventional Web search technology or the RSS feed, this paper envisions a personalized full-text content filtering and dissemination system in a highly distributed environment such as a Distributed Hash Table (DHT). Users can subscribe to their interested contents by specifying some terms and threshold values for filtering. Then, published contents will be disseminated to the associated subscribers. We propose a novel and simple framework of filter registration and content publication, STAIRS. By the new framework, we propose three algorithms (default forwarding, dynamic forwarding and adaptive forwarding) to reduce the forwarding cost and false dismissal rate; meanwhile, the subscriber can receive the desired contents with no duplicates. In particular, the adaptive forwarding utilizes the filter information to significantly reduce the forwarding cost. Experiments based on two real query logs and two real datasets show the effectiveness of our proposed framework.

The efficiency of matching structures is the key issue for content publish/subscribe systems. In this paper, we propose an efficient matching tree structure, named CobasTree, for a distributed environment. Particularly, we model a predicate in each subscription filter as an interval and published content value as a data point. The CobasTree is designed to index all subscription intervals and a matching algorithm is proposed to match the data points to these indexed intervals. Through a set of techniques including selective multicast by bounding intervals, cost model-based interval division, and CobasTree merging, CobasTree can match the published contents against subscription filters with a high efficiency. We call the whole framework including CobasTree and the associated techniques as COBAS. The performance evaluation in simulation environment and PlanetLab environment shows COBAS significantly outperforms two counterparts with low cost and fast forwarding.

As a promising new technology with the unique properties like high efficiency, scalability and fault tolerance, Peer-to-Peer (P2P) technology is used as the underlying network to build new Internet-scale applications. However, one of the well known issues in such an application (for example WWW) is that the distribution of data popularities is heavily tailed with a Zipf-like distribution. With consideration of the skewed popularity we adopt a proactive caching approach to handle the challenge, and focus on two key problems: where (i.e. the placement strategy: where to place the replicas) and how (i.e. the degree problem: how many replicas are assigned to one specific content)? For the where problem, we propose a novel approach which can be generally applied to structured P2P networks. Next, we solve two optimization objectives related to the how problem: MAX_PERF and MIN_COST. Our solution is called PoPCache, and we discover two interesting properties: (1) the number of replicas assigned to each content is proportional to its popularity; (2) the derived optimal solutions are related to the entropy of popularity. To our knowledge, none of the previous works has mentioned such results. Finally, we apply the results of PoPCache to propose a P2P base web caching, called as Web-PoPCache. By means of web cache trace driven simulation, our extensive evaluation results demonstrate the advantages of PoPCache and Web-PoPCache.