The objective of flood forecasting is to accurately predict the peak value and timing of flood crests. However, the highly nonlinear temporal dynamics, intricate spatial relationships, and distributional shifts present significant challenges to precise flood prediction. To address these challenges, we propose a multi-scale hierarchical feature learning framework based on discrete wavelet transform (WTHFL). WTHFL comprises three core modules: multi-scale decomposition (MSD), coarse- to fine-grained hierarchical feature learning (CF-HFL), and contribution analysis (CA). Specifically, MSD first utilizes discrete wavelet transform (DWT) to perform multi-level decomposition of time series, effectively disentangling intricate spatio-temporal patterns. Following this, CF-HFL extracts spatio-temporal features from coarse to fine scales, while employing multi-cutoff frequency low-pass filtering to minimize noise interference. Finally, CA dynamically analyzes the influence of spatio-temporal rainfall distribution on flow, contributing to the prediction of final discharge. To mitigate the issue of distributional shifts and enhance the generalization capability of the model, we align spatio-temporal features across various scales using Sinkhorn divergence. Additionally, we introduce a peak loss that prioritizes high-flow portions aligning the model with flood characteristics. Comprehensive experiments on two real-world datasets demonstrate the superior performance of WTHFL.

Insufficient dynamic adaptability and lagging response to sparse events in sluice gate scheduling due to sudden rainfall in urban water-sheds is a critical topic, which raises significant importance and practical interest. This study proposes a scheduling framework based on Deep Q-Network (DQN) integrated with Support Vector Regression (SVR) and enhanced by Prioritized Experience Replay (PER). By modeling sluice gate scheduling as a sequential decision-making problem within a Markov Decision Process (MDP) model, the scheduling strategy is effectively optimized using Reinforcement Learning (RL). To address the modeling challenges of traditional DQN in high-dimensional state spaces, we incorporate a SVR model to predict the trend of water level changes. In addition, the PER is introduced to dynamically adjust the priority of experience replay, with a focus on improving the learning efficiency for sparse high-risk events. Experiments on the Nanjing Wuding men sluice gate show that the proposed scheduling framework achieves a maximum reduction of approximately 90.9 % in dispatch frequency under one of the three water level control criteria, while maintaining stable water level control across all scenarios.

Accurate flood prediction is crucial for disaster prevention and mitigation. Hydrological data exhibit highly nonlinear temporal patterns and encompass complex spatial relationships between rainfall and flow. Existing flood prediction models struggle to capture these intricate temporal features and spatial dependencies. This paper presents an adaptive periodic and spatial self-attention method based on LSTM (APS-LSTM) to address these challenges. The APS-LSTM learns temporal features from a multi-periodicity perspective and captures diverse spatial dependencies from different period divisions. The APS-LSTM consists of three main stages, (i) Multi-Period Division, that utilizes Fast Fourier Transform (FFT) to divide various periodic patterns; (ii) Spatio-Temporal Information Extraction, that performs periodic and spatial self-attention focusing on intra- and inter-periodic temporal patterns and spatial dependencies; (iii) Adaptive Aggregation, that relies on amplitude strength to aggregate the computational results from each periodic division. The abundant experiments on two real-world datasets demonstrate the superiority of APS-LSTM. The code is available: https://github.com/oopcmd/APS-LSTM.

Accurate flood forecast is crucial to ensure economic and ecological environment safety. Due to the complex factors affecting flood runoff in the small and medium-sized river basins, the traditional model cannot yield satisfactory prediction results. In this paper, we propose a novel Graph Convolution based spatial-temporal Attention LSTM(AGCLSTM) network to tackle the time series prediction problem in the flood forecasting domain. To be specific, our model contains two major modules: 1) the spatial-temporal GCN module with the dropedge mechanism which adequately captures the spatial and temporal characteristics of topological river graphs; 2) the spatial-temporal LSTM module to effectively extract temporal and spatial dynamic correlation in time series hydrological data. Experiments show that our model has excellent performance in flood peak prediction and flow calibration compared with the existing machine learning methods.

The topic models can effectively reduce the dimensionality of document representation. In recent years, Generative Adversarial Networks (GANs) have begun to be applied to topic models, but most researches do not fully consider the documents’ context information, leading to the fact that the generated results of the encoder networks cannot represent the real data distribution. To this end, we propose a novel topic model named Semantic-based Bidirectional Adversarial Neural Topic Model (SNTM), which introduces semantic information into Bidirectional Generative Adversarial Networks (BiGAN) by adding the word embedding and BiLSTM-Attention mechanism. This improvement makes the encoder network in our model generate a distribution closer to the real document-topic distribution. Furthermore, we also propose a topic difference evaluation indicator, which more comprehensively evaluates the quality of the generated topics. The experimental results on different datasets show that SNTM performs better than the baseline topic model.

Inferring causal relationships is key to data science. Learning causal structures in the form of directed acyclic graphs (DAGs) has been widely adopted for uncovering causal relationships, nonetheless, it is a challenging task owing to its exponential search space. A recent approach formulates the structure learning problem as a continuous constrained optimization task that aims to learn causal relation matrix. Following it are various variants, such as nonlinear variants which uncover nonlinear causal relationships. However, the nonlinear variant which considers an ordinary sparsity constraint as part of its optimization objective may not effectively eliminate spurious edges, i.e., wrongly predicted edges. In this paper, we investigate the defect that the ordinary sparsity constraint inherently contains, that is, when the model predicts spurious edges, the sparsity constraint may be unable to make the relation matrix sparse to help eliminate such spurious edges, and based on the theoretical and empirical analysis of the defect, we propose the proportional sparse penalty constraint PSP which replaces the ordinary sparsity constraint with a normalized first-order matrix norm. We then compare our proposed model GAE-PSP with three models to show considerable performance improvement. We further conduct three comparative experiments and show that the PSP can make the relation matrix sparse to eliminate spurious edges.

Intelligent flood forecasting systems provide an effective means to forecast flood disaster. Accurate flood flow value prediction is a huge challenge since it's influenced by both spatial and temporal relationship among flood factors. Popular deep learning structures like Long Short-Term Memory (LSTM) network lacks abilities of modeling the spatial correlations of hydrological data, thus cannot yield satisfactory prediction results. Moreover, not all the temporal information is always valuable for flood forecasting. In this paper, we proposed a novel spatial and temporal aware Graph Convolution Network (ST-GCN) for flood prediction, which is capable to extract spatial-temporal information from raw flood data. Moreover, a temporal attention mechanism is introduced to weight the importance of different time steps, thus involving global temporal information to improve flood prediction accuracy. Compared with the existing methods, results on two self-collected datasets show that ST-GCN greatly improves the prediction performance.

The intelligent question answering over the knowledge graph aims to automatically answer natural language questions via locating the correct entities in the knowledge graph. Aside from the former progresses, it is still challenging to answer the multi-relation questions because of the variety and complexity of the natural language, as well as the combinatorial explosion on possible candidates. In this paper, we propose a novel embedding-based approach named SPE-QA to address these issues. It answers a question by identifying its most semantic like question-answer path from the candidate topic-entity-centric subgraph, and locating this path's tail entity as the final answer. In order to limit the scale of the candidate sub graph and thus reduce the neural network's training complexity, it is essential to filter the explicit noises as much as possible. Therefore, we employ a sub graph separation method to decide the scale of the sub graph, and remove the false question-answer paths with two different pruning policies. The experimental results on two widely used benchmarks approve that, our mechanism can not only reduce the subgraph's scale dramatically, but also obtain better performance on the multi-relation questions, compare to the stat-of-the-art approaches.

Efficient scheduling algorithms are key for attaining high performance in heterogeneous computing systems. In this article, we propose a new list scheduling algorithm for assigning task graphs to fully connected heterogeneous processors with an aim to minimize the scheduling length. The proposed algorithm, called Improved Predict Priority Task Scheduling (IPPTS) algorithm has two phases: task prioritization phase, which gives priority to tasks, and processor selection phase, which selects a processor for a task. The IPPTS algorithm has a quadratic time complexity as the related algorithms for the same goal, that is$O(t^{2} \times p)$, for$t$tasks and$p$processors. Our algorithm reduces the scheduling length significantly by looking ahead in both task prioritization phase and processor selection phase. In this way, the algorithm is looking ahead to schedule a task and its heaviest successor task to the optimistic processor, i.e., the processor that minimizes their computation and communication costs. The experiments based on both randomly generated graphs and graphs of real-world applications show that the IPPTS algorithm significantly outperforms previous list scheduling algorithms in terms of makespan, speedup, makespan standard deviation, efficiency, and frequency of best results.

As the scale of RDF data increases gradually, the requirements for efficient processing technologies on RDF data in the distributed environment has become a hot topic. Most existing methods have two main issues. First, massive storage redundancy often occurs to materialize semi-join results, but only a part is frequently accessed in most queries. Second, the queries are allocated to the whole cluster, causing a considerable duplication on intermediate results and hence increasing the communication cost. In this paper, we propose a new approach named FP-ExtVP, using a hybrid partitioning schema to reduce the storage requirement without damaging the query performance. In addition, we create an index named P-tree to allocate the queries only to the mentioned nodes, instead of the whole cluster, in order to reduce the shuffle cost. The experiments on several benchmarks demonstrate that, comparing to the native S2RDF, our method outperforms on Star, Snowflake and Complex queries, by reducing nearly a half of the storage requirements.

Efficient workflow scheduling algorithms are crucial for attaining high performance in large-scale heterogeneous distributed infrastructures, such as cloud computing. List scheduling algorithms are one of the most efficient heuristic methods for assigning task graphs to fully connected heterogeneous systems. However, most existing list-based scheduling algorithms do not consider the applications' security requirements and the security services offered by cloud providers. In this paper, we extend four list scheduling algorithms for security-aware workflow scheduling in the IaaS cloud. The idea of the extension is to consider the security overheads in both tasks prioritizing phase and virtual machine selection phase of the four original algorithms. Based on real-world applications, we evaluate the performance of the proposed algorithms in terms of scheduling length, speedup and efficiency.

With the development of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have emerged to wireless communication system, especially in cybersecurity. In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. Specifically, we firstly summarize fundamental problems of network security and attack detection and introduce several successful related applications using deep learning structure. On the basis of categorization on deep learning methods, we pay special attention to attack detection methods built on different kinds of architectures, such as autoencoders, generative adversarial network, recurrent neural network, and convolutional neural network. Afterwards, we present some benchmark datasets with descriptions and compare the performance of representing approaches to show the current working state of attack detection methods with deep learning structures. Finally, we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures.

To minimize the negative impacts brought by floods, researchers from pattern recognition community utilize artificial intelligence based methods to solve the problem of flood prediction. Inspired by the significant power of Long Short-Term Memory (LSTM) networks in modeling the dynamics and dependencies of sequential data, we intend to utilize LSTM networks to predict sequential flow rate values based on a set of collected flood factors. Since not all factors are informative for flood prediction and the irrelevant factors often bring a lot of noise, we need to pay more attention to the informative ones. However, original LSTM doesn't have strong attention capability. Hence we propose an context-aware attention LSTM (CA-LSTM) network for flood prediction, which is capable to selectively focus on informative factors. During training, the local context-aware attention model is constructed by learning probability distributions between flow rate and hidden output of each LSTM cell. During testing, the learned local attention model assign weights to adjust relations between input factors and predictions at all steps of LSTM network. We conduct experiments on a flood dataset with several comparative methods to demonstrate high accuracy of the proposed method and the effectiveness of the proposed context-aware attention model.

To minimize the negative impacts brought by floods, researchers from pattern recognition community pay special attention to the problem of flood prediction by involving technologies of machine learning. In this paper, we propose to construct hierarchical Bayesian network to predict floods for small rivers, which appropriately embed hydrology expert knowledge for high rationality and robustness. We present the construction of the hierarchical Bayesian network in two stages comprising local and global network construction. During the local network construction, we firstly divide the river watershed into small local regions. Following the idea of a famous hydrology model - the Xinanjiang model, we establish the entities and connections of the local Bayesian network to represent the variables and physical processes of the Xinanjiang model, respectively. During the global network construction, intermediate variables for local regions, computed by the local Bayesian network, are coupled to offer an estimation for time-varying values of flow rate by proper inferences of the global network. At last, we propose to improve the output of Bayesian network by utilizing former flow rate values. We demonstrate the accuracy and robustness of the proposed method by conducting experiments on a collected dataset with several comparative methods.

The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users’ product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of high‐quality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain‐specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non‐domain‐specific sentiment lexicons as well as state‐of‐the‐art machine‐learning and deep‐learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.

With the recent advancements in the information and communication technologies, large number of devices are connecting to the Internet, hence large volumes of data in different formats and from different sources are generating. Consequently, on one hand dynamic and heterogeneous data sharing and management, in the ecosystem of Internet of Things (IoT), where every smart object is connected to Internet, presents new research challenges. On the other hand, citizen privacy preserving is another challenge, because he/she has to send his/her information to a service provider, to obtain the required information. This information is sensitive since it can reveal information about an individual. An attacker or a malicious service provider can utilize this sensitive information for their own business or something else. This paper presents a Secure Framework for Future Smart City (SEFSCITY), for better city living and governance, based on Cloud Computing IoT and Distributed Computing. We first present the architecture of SEFSCITY, which is based on Multi-Cloud and Cloud Federation approach; then we propose a security protocol for our framework. In our security model, we use Zero-Knowledge Protocol based on Elliptic Curve Discrete Logarithm Problem. Finally, we validate our architecture by conducting several scenarios that we have implemented using Cloud Analyst tool. The results show that in all scenarios, the cost infrastructure remains the same for the cloud customer, and our approach is benefic for the cloud provider in term of revenues and data processing time

Traditionally the data retrieval is achieved by searching the metadata with keywords, though it is often difficult for ordinary users to express professional and precise query demands in the water industry. Regarding this issue, this paper introduces an exploratory retrieval method called faceted search by gradually recommending relevant to the users. Firstly, a unified modeling algorithm is proposed to construct the unified metadata model in XML for heterogeneous water metadata. Based on this model, candidate facet terms can be extracted and filtered, in order to retrieve the various water metadata uniformly. At last, a facet recommendation algorithm is proposed to help the users to sharpen their queries by prompting less but more accurate as the search gets deeper, after excluding those irrelevant facets and redundant facets. The experimental results demonstrate that our facet recommendation algorithm can significantly improve the retrieval precision on querying the water data.

Current algorithms that utilize water index to extract water information from high resolution remote sensing image are inadequate in that it is difficult to determine the optimal thresholds, the result of water boundary is not satisfactory and prone to error. We propose a new algorithm which combines image segmentation algorithm based on MRF model with Normalized Difference Water Index (NDWI) for extracting water information. We represent the pixels in image as random variables in a MRF model and introduce a hybrid feature space in the energy function on these variables, then minimize the energy function by iterative graph cut scheme to find the optimal water boundary. Water boundary is refined according to water index and color features of extracted main water body. The experiments on ShiLianghe Reservoir show that our approach can achieve a better accuracy and the boundary is precise, especially at the reservoirs that surrounding environments are complicated.

The spatial join is usually processed in two steps: filter and refinement. The first creates candidate pairs based on the objects' abstractions, like object pairs having overlapping minimum bounding rectangles (MBRs). The second step then further checks each candidate pair whether the objects fit the join condition with their actual shapes. This two-step strategy prevents the disk I/O and CPU overhead spent on retrieving and comparing the non-candidates, hence it is also adopted at the large scale processing, based on the Hadoop platform. However, in order to cope with different data distributions, the Hadoop-based spatial join often processes both above steps in the Reduce stage, causing considerable data migration overhead as all object pairs need to be shuffled over the cluster network. In this paper we propose two novel shuffling approaches for the distributed filter and refinement operations. They are able to retrieve and even transfer only the candidates' actual shapes data in the Reduce tasks, decreasing the migration overhead to the minimum. In our evaluations with a six-node cluster, both approaches outstandingly improve the process efficiency regardless of the join selectivity.

Aiming at optimizing Web service composition which satisfies user's multiple QoS constraints, an efficient graph-based Web service composition approach, named Skyline improved Multi Constraint Shortest Path-Relax (Sky-MCSP-R), is proposed. Firstly, the approach selects Skyline services from candidate service spaces, thus it can construct the model of Web service composition directly on these high quality candidate services, reducing the whole number of nodes of the model. Secondly, the approach uses MCSP-K algorithm which uses over constraint mechanism to compose basic services, and reduces the constraint intensity so to make algorithm MCSP-K produce as many feasible solutions as possible. Thirdly, the approach uses Relax algorithm to optimize the solutions. Experimental results show that the approach improves the efficiency of Web service composition and keeps a high optimization rate, reducing no solution phenomenons. The results also indicate that our approach is superior to previous approaches.

This paper proposes a new technique for hyperspectral band selection from the spectral similarity perspective. Through a newly defined measure for band subset discriminativeness, class-specific important bands are retained which can preserve the spectral similarity of the samples from the same class and narrow down candidate band subset for the following search procedure. Then optimal search is performed in the aggregated band subset from all classes. Experiments on the Indian Pine benchmark data set have proved the efficiency and effectiveness of the proposed method.

Aggregate information is important for data stream processing systems, especially, to get any user-specified time window queries and evolution analysis over data stream. To solve this problem, in this paper, we propose an integrated structure for managing summarized information of snapshots under geometry timeframe, which facilitates analyzing the temporal evolving of data stream. By using our method, the cost of update operations and the errors can be controlled within acceptable scope. Evaluation shows the our structure can respond to arbitrary time window aggregate queries within small errors, efficiently.

Route planning is one of the important tasks for transport systems. Appropriate policies for route selections improve not only profitability of transport companies but also convenience of customers. Traditional ways for establishing the policies depend on manual efforts based on statistical data of transports. Moreover, traditional route planning techniques are reactive, i.e., an optimization based on information provided in advance. It is difficult for the manual policies and the reactive planning techniques to adjust dynamic changes of transport trends for customers such as amount and direction of transport demands, i.e., drivers of transport vehicles must follow the policies provided in advance. Therefore, in this paper we show how the proactive route planning based on expected rewards for transport systems can be modeled as a reinforcement learning problem. And, we show how agents as transport vehicles acquire their policies for route selection autonomously and dynamically. The learning ability of transport trends enables transport vehicles to foresee the next destination which provides high rewards. Finally, we report simulation results and make the effectiveness of our proposal strategy clear