Integrating dynamics into graph neural networks (GNNs) provides deeper insights into the evolution of dynamic graphs, thereby enhancing the temporal representation in real-world dynamic network problems. Existing methods extracting critical information from dynamic graphs face two key challenges, either overlooking the negative impact of redundant information or struggling in addressing the distribution shifting issue in dynamic graphs. To address these challenges, we propose MaskDGNN, a novel dynamic GNN architecture that consists of two modules: First, self-supervised activeness-aware temporal masking mechanism selectively retains edges between highly active nodes while masking those with low activeness, effectively reducing redundancy. Second, adaptive frequency enhancing graph representation learner amplifies the frequency-domain features of nodes to capture intrinsic features under distribution shifting. Experiments on five real-world dynamic graph datasets demonstrate that MaskDGNN outperforms state-of-the-art methods, achieving an average improvement of 7.07% in accuracy and 13.87% in MRR for link prediction tasks.

Side-channel-based Deep Neural Network (DNN) model stealing has become a major concern with the advent of learning-based attacks. In respond to this threat, defence mechanisms have been presented to obfuscate the DNN execution, making it difficult to infer the correlation between side-channel information and DNN architecture. However, state-of-the-art (SOTA) DNN obfuscation is time-consuming, requires expert-level changes in existing DNN compilers (e.g., Tensor Virtual Machine (TVM)), and often relies on prior knowledge of the attack models. In this work, we study the impact of various obfuscation levels on the defence effectiveness, and present a streamlined DNN obfuscation process that is extremely fast and is agnostic to any attack models. Our study reveals that by just modifying the scheduling of DNN operations on the GPU, we can achieve comparable defense performance as the SOTA in an attack agnostic manner. We also propose a simple algorithm that determines an effective scheduling configuration for mitigating DNN model stealing at a fraction of a time required by SOTA obfuscation methods. Our method can be easily integrated into existing DNN compilers as a security feature, even by non-experts, to protect their DNN against side-channel attacks.

Deep neural network (DNN) intellectual property (IP) models must be kept undisclosed to avoid revealing trade secrets. Recent works have devised machine learning techniques that leverage on side-channel information leakage of the target platform to reverse engineer DNN architectures. However, these works fail to perform successful attacks on DNNs that have undergone performance optimizations (i.e., operator fusion) using DNN compilers, e.g., Apache tensor virtual machine (TVM). We propose a two-phase attack framework to infer the layer sequences of optimized DNNs through side-channel information leakage. In the first phase, we use a recurrent network with multihead attention components to learn the intra and interlayer fusion patterns from GPU traces of TVM-optimized DNNs, in order to accurately predict the operation distribution. The second phase uses a model to learn the run-time temporal correlations between operations and layers, which enables the prediction of layer sequence. An encoding strategy is proposed to overcome the convergence issues faced by existing learning-based methods when inferring the layer sequences of optimized DNNs. Extensive experiments show that our learning-based framework outperforms state-of-the-art DNN model extraction techniques. Our framework is also the first to effectively reverse engineer both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) using side-channel leakage.

Multimodal public transport networks (MMPTNs) in modern cities are becoming increasingly complex. This makes finding optimal journey routes challenging due to a large number of transfer options that need to be properly considered. Furthermore, the complexity of the problem is compounded when multiple conflicting travel criteria are considered (e.g., travel time, walking distance, travel fare, etc.). This paper proposes a transfer graph (TG) model to explore the transfer opportunities of the MMPTN to support efficient journey route planning. TG considers all possible transfer opportunities, while employing a representative mechanism to optimize the TG structure that supports efficient route planning algorithms. Based on the proposed TG, we develop two exact algorithms to search the Pareto-optimal solutions for multi-criteria journey planning (MCJP) over the MMPTN. The first algorithm runs faster by eliminating many partial solutions at an early stage, which is more suited for lowering computation time at the expense of marginal degradation in output quality. In contrast, the second algorithm provides a more dependable solution by incorporating accurate journey time prediction that caters to the evolving traffic conditions. We also develop techniques to accelerate the TEDE and TEAE algorithms. Experiments on real-world public transport networks and traffic data demonstrate the effectiveness of our approach for MCJP. Experiment results also reveal interesting insights on the impact of the TOs, number of transfers, and number of travel criteria on MCJP algorithms, which can contribute to better public transportation planning.

Transport mode identification (TMI), which infers the travel modes of user trajectories, is essential to facilitate an understanding of urban mobility patterns and passengers’ choice behaviors with the goal of improving urban transportation systems. To achieve higher accuracy, existing TMI methods usually rely on mobility features obtained from densely sampled GPS trajectory points (e.g. 1 second per GPS point) or data measurements of additional inertial measurement unit (IMU) sensors (e.g. accelerometer, gyroscope, rotation vector). However, these lead to high energy consumption of the users’ mobile devices. In this paper, we propose a novel deep learning framework, Multi-Scale Attributes Attention (MSAA) model, to extract discriminating trajectory features from GPS data only, without the need to increase its sampling rate. The proposed model first partitions the trajectories into different scales and extract the latent representation of local attributes at each scale. The MSAA model relies on Convolutional Neural Network (CNN) to capture the spatial correlation of different trajectory segments, and utilizes attention mechanism to select the most suitable local attributes on the different trajectory scales that can effectively characterize the various transport modes. Since the learned latent local attributes are significantly different from the global features (e.g. average/min/max travel speeds which are measurable quantities), an ensemble model based on Neural Decision Forest (NDF) is employed to fuse the heterogeneous features consisting of both measurable quantities and non-measurable elements for determining the transport mode. Experiments on real-world datasets demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines, with average improvements in accuracy ranging from 0.76% to 6.4%. In addition, the proposed multi-scale local attributes well complement the global features. Our results show that by incorporating the local attributes, the detection performance improved by 2.3% on average compared to using only global features.

Traffic congestion has become a global concern due to continuous increase in traffic demand and limited road capacity. The ability to predict traffic congestion propagation, which depicts the spatiotemporal evolution of the congestion scenario, is essential for developing smart traffic management systems and enabling road users to make informed route choices. In this work, we study the behavior of congestion propagation at the road segment level, and leverage this to develop a novel machine learning framework that characterizes and predicts the congestion evolution among different road segments in the traffic network. In particular, our framework can infer the likelihood of congestion propagation between any pair of road segments through single or multiple propagation paths. The proposed framework relies on a network embedding module to learn a representation for each road segment, and a propagation model which calculates the congestion propagation likelihood based on the learned representations. Specifically, an asymmetric embedding of local proximity and global tendency (AE-LPGT) is relied upon for learning low dimension embeddings of the road segments which incorporate various realistic properties of congestion propagations, such as the local proximity property, global propagation tendency, and asymmetric transitivity of congestion propagations. Experimental results with Singapore traffic data show that our method significantly outperforms the state-of-the-art, and the congestion propagation properties in our embeddings have significant impact on the prediction performance.

Many efforts are devoted to predicting congestion evolution using propagation patterns that are mined from historical traffic data. However, the prediction quality is limited to the intrinsic properties that are present in the mined patterns. In addition, these mined patterns frequently fail to sufficiently capture many realistic characteristics of true congestion evolution (e.g., asymmetric transitivity, local proximity). In this paper, we propose a representation learning framework to characterize and predict congestion evolution between any pair of road segments (connected via single or multiple paths). Specifically, we build dynamic attributed networks (DAN) to incorporate both dynamic and static impact factors while preserving dynamic topological structures. We propose a Deep Meta Learning Model (DMLM) for learning representations of road segments which support accurate prediction of congestion evolution. DMLM relies on matrix factorization techniques and meta-LSTM modules to exploit temporal correlations at multiple scales, and employ meta-Attention modules to merge heterogeneous features while learning the time-varying impacts of both dynamic and static features. Compared to all state-of-the-art methods, our framework achieves significantly better prediction performance on two congestion evolution behaviors (propagation and decay) when evaluated using real-world dataset.

The first-mile transportation provides a transit service using ridesharing-based vehicles, e.g., feeder buses, for passengers to travel from their homes, workplaces, or public institutions to the nearest public transportation depots (rapid-transit metro or appropriated bus stations) which are located beyond comfortable walking distance. This paper studies the vehicle routing problem (VRP) for the first-mile transportation, which aims at finding the optimal travel routes for a vehicle fleet to deliver passengers from their doorstep to the depots, where the passengers can continue their journeys using fixed-route buses or trains. We focus on the Peak-Hour VRP (PHVRP) for a limited vehicle fleet capacity to serve a large volume of travel requests, with the aim of maximizing the number of served passengers. The PHVRP generalizes the VRP with time window by considering multiple alternative depots for each travel request, such that a request is satisfied if the passenger is taken to one of his/her nearest depots. We formally formulate the PHVRP with constraints on vehicle capacity, pickup time windows, and quality of service regarding riding time, where a novel trip-based constraint model is used. We proposed an ant-colony optimization algorithm for the PHVRP, which is initialized with pheromone information that jointly considers the temporal-spatial distance as well as depot similarity among different travel requests. We introduced a novel scheme (called trip-by-trip scheme) to construct the travel routes by repeatedly forming a single trip for the vehicle with earliest end time until no vehicle can accept any more trips. In constructing a single trip, the algorithm intelligently decides whether or not to end the trip instead of taking more passengers. The effectiveness of the proposed methods is evaluated by comparing with optimal solutions on small size instances and with heuristic solutions on large-size instances, using road network in Singapore and synthetic travel requests that are generated based on real bus travel demands.

Accurate bus travel speed prediction can lead to improved urban mobility by enabling passengers to reliably plan their trips in advance and traffic administrators to manage the bus operations more effectively. However, the increasing complexity of public transportation networks pose a significant challenge to existing prediction methods as the bus operations are affected by numerous factors such as varying traffic conditions, tight bus operation schedules, wide-ranging travel demands, frequent accelerations/decelerations at bus stops, delays at intersections, etc. This paper aims to achieve accurate bus speed prediction by identifying important intrinsic and extrinsic features that impact the bus speed, and their significance in specific situations. We propose to jointly incorporate multiple feature components that provide discriminating information to train the prediction model by exploring the spatial correlation, temporal correlation, as well as contextual information (e.g. road characteristics and weather conditions). In particular, we introduce an attribute-driven attention network model to integrate the feature components, which considers the heterogeneous influence of different feature components on bus speed and dynamically assigns weights to the learned latent features based on specific traffic situations. Extensive experiments using real bus travel data involving 42 bus services show that our proposed method outperforms six well-known methods.

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey usually consists of multiple bus trips. In this paper, we investigate the problem of travel-time prediction for bus journeys that takes into account a passenger's riding time on multiple bus trips, and also his/her waiting time at transfer points (interchange stations or bus stops). A novel framework is proposed to separately predict the riding and waiting time of a given journey from multiple datasets (i.e., historical bus trajectories, bus route, and road network), and combining the results to form the final travel-time prediction. We empirically determine the impact factors of bus riding times and develop a long short-term memory model that can accurately predict the riding time of each segment of the bus lines/routes. We also demonstrate that the waiting time at transfer points significantly impacts the total journey travel time, and estimating the waiting time is non-trivial as we cannot assume a fixed distribution waiting time. In order to accurately predict the waiting time, we introduce a novel interval-based historical average method that can efficiently address the correlation and sensitivity issues in waiting time prediction. Experiments on real-world data show that the proposed method notably outperforms six baseline approaches for all the scenarios considered.

Large scale multiprocessor array suffers from frequent hardware defects or soft faults due to overheating, overload or occupancy by other running applications. To obtain fault-free logical array, reconfiguration techniques are proposed to reuse the fault-free PEs by changing the interconnection among PEs. Previous research has worked on this topic but assume that switches and links are fault-free. In this paper, we consider faults not only on the processing elements (PEs) but also on the switches and links, and develop efficient algorithms to construct as large as possible logical arrays with optimized networks length. To deal with the faults on switches and links, an efficient pre-processing procedure is designed, in which switch faults are transformed into link faults, and then faulty links are classified into several categories to handle. Then, we propose an efficient algorithm, A-MLA, to produce as many as possible logical columns which are then combined to form a two dimensional processor array. After that, we propose an algorithm A-TMLA to reduce the interconnection length of the logical array obtained by algorithm A-MLA, as short interconnect leads to small communication latency and power consumption. Extensive experimental results show that, even with switch faults and link faults, our approach can produce larger logical fault-free arrays with shorter interconnection length, compared to the state-of-the-art.