In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quickly understand the situation and respond more effectively. This is challenging because the key information of the event is often scattered across multiple documents, involving complex event knowledge understanding and reasoning, which is under-explored in previous work. Therefore, we proposed the Event-Centric Multi-Document Summarization task, which aims to generate concise and comprehensive summaries of a given event based on multiple related news documents. Based on this, we constructed the EventSum dataset, which was constructed using Baidu Baike entries and underwent extensive human annotation, to facilitate relevant research. It is the first large-scale Chinese multi-document summarization dataset, containing 5,100 events and a total of 57,984 news documents, with an average of 11.4 input news documents and 13,471 characters per event. To ensure data quality and mitigate potential data leakage, we adopted a multi-stage annotation approach for manually labeling the test set. Given the complexity of event-related information, existing metrics struggle to comprehensively assess the quality of generated summaries. We designed specific metrics including Event Recall, Argument Recall, Causal Recall, and Temporal Recall along with corresponding calculation methods for evaluation. We conducted comprehensive experiments on EventSum to evaluate the performance of advanced long-context Large Language Models (LLMs) on this task. Our experimental results indicate that: 1) The event-centric multi-document summarization task remains challenging for existing long-context LLMs; 2) The recall metrics we designed are crucial for evaluating the comprehensiveness of the summary information.

Privacy-preserving data aggregation (PDA) enables an edge server to securely perform aggregation tasks on data generated by terminal devices in edge intelligence (EI) systems, revealing only the result without exposing individual inputs. However, most existing solutions, such as homomorphic encryption and federated learning, support only basic functions (e.g., SUM or AVG). They often fail to achieve privacy protection, fault tolerance, and lightweight terminal-side operations when the server colludes with compromised devices. In this work, we propose PrivEI, a practical PDA scheme for EI systems. It uses a proposed collusion-resistant symmetric masking scheme that enables an untrusted edge server to collect and decode masked inputs from$n$terminal devices while supporting arbitrary computations. The scheme allows the server to collude with$k \leq n - 2$terminal devices and has a lightweight mechanism to tolerate device dropouts during aggregation. PrivEI further leverages the Chinese Remainder Theorem to avoid frequent mask updates when aggregating multi-dimensional data, and ensures data integrity using a signer-efficient multiple-time elliptic curve signature algorithm. We formally prove that PrivEI ensures input privacy and achieves$(n-k)$-source anonymity. Both theoretical analysis and experimental results confirm that it offers superior functionality with performance comparable to existing approaches. We have open-sourced the implementation.

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://github.com/Mzzzhu/CMNEE. Keywords: Corpus,Information Extraction, Information Retrieval, Knowledge Discovery/Representation

Limited data availability poses a major obstacle in training deep learning models for financial applications. Synthesizing financial time series to augment real-world data is challenging due to the irregular and scale-invariant patterns uniquely associated with financial time series - temporal dynamics that repeat with varying duration and magnitude. Such dynamics cannot be captured by existing approaches, which often assume regularity and uniformity in the underlying data. We develop a novel generative framework called FTS-Diffusion to model irregular and scale-invariant patterns that consists of three modules. First, we develop a scale-invariant pattern recognition algorithm to extract recurring patterns that vary in duration and magnitude. Second, we construct a diffusion-based generative network to synthesize segments of patterns. Third, we model the temporal transition of patterns in order to aggregate the generated segments. Extensive experiments show that FTS-Diffusion generates synthetic financial time series highly resembling observed data, outperforming state-of-the-art alternatives. Two downstream experiments demonstrate that augmenting real-world data with synthetic data generated by FTS-Diffusion reduces the error of stock market prediction by up to 17.9%. To the best of our knowledge, this is the first work on generating intricate time series with irregular and scale-invariant patterns, addressing data limitation issues in finance.

Humanitarian relief operations are often accompanied by regional conflicts around the globe, at risk of deliberate, persistent and unpredictable attacks. However, the long-term channeling of aid resources into conflict areas may influence subsequent patterns of violence and expose local communities to new risks. In this paper, we quantitatively analyze the potential externalities associated with long-lasting humanitarian relief operations based on game-theoretical modeling and online planning approaches. Specifically, we first model the problem of long-lasting humanitarian relief operations in conflict areas as an online multi-stage rescuer-and-attacker interdiction game in which aid demands are revealed in an online fashion. Both models of single-source and multiple-source relief supply policy are established respectively, and two corresponding near-optimal online algorithms are proposed. In conjunction with a real case of anti-Ebola practice in conflict areas of DR Congo, we find that 1) long-lasting humanitarian relief operations aiming alleviation of crises in conflict areas can lead to indirect funding of local rebel groups; 2) the operations can activate the rebel groups to some extent, as evidenced by the scope expansion of their activities. Furthermore, the impacts of humanitarian aid intensity, frequency and supply policies on the above externalities are quantitatively analyzed, which will provide enlightening decision-making support for the implementation of related operations in the future.

A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i.i.d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.

Wireless sensor networks (WSNs) are an effective and efficient method for collecting data from a target area, and prolonging the lifetime of WSNs has been a focus of scientific research due to the limited energy of sensor nodes. However, traditional studies of WSNs are based on the assumption that WSNs collect aggregated data with redundant sensor nodes in an ideal radio environment. These assumptions may not be acceptable in practical applications. To address this problem, this paper introduces a novel application scenario for WSNs in which raw data are collected by a multihop network without redundant sensor nodes, and a new hybrid tree-based and cluster-based routing protocol for raw data collection (HTC-RDC) is proposed to prolong the lifetime of WSNs that can work in novel application scenarios. The experimental results demonstrate that the proposed HTC-RDC enables the WSNs to achieve their expected functions and prolongs the network lifetime by an average of 11.4% compared to the existing protocols in the explored application scenario.

The evolutionary multi-objective optimization algorithm was used to optimize the clustering and splitting of nodes in the construction of inverted spatial objects index tree(ITSR) in this paper. Considering the objective factors including object's coverage, overlap, group center distance, directory rectangle perimeter and the word similarity between tree nodes, etc., a multi-objective optimization model for solving the optimal ITSR construction is established. To solve the model, this paper find a novel method of constructing high efficiency inverted text-spatial R-tree index. The experimental results show that the algorithm supports the multi-objective optimal clustering of ITSR with high efficiency and accuracy .

Participatory sensing is a crowdsourcing-based framework, where the platform executes the sensing requests with the help of many common peoples' handheld devices (typically smartphones). In this paper, we mainly address the online sensing request admission and smartphone selection problem to maximize the profit of the platform, taking into account the queue backlog, and the location of sensing requests and smartphones. First, we formulate this problem as a discrete time model and design a location aware online admission and selection control algorithm (LAAS) based on the Lyapunov optimization technique. The LAAS algorithm only depends on the currently available information and makes all the control decisions independently and simultaneously. Next, we utilize the recent advancement of the accurate prediction of smartphones' mobility and sensing request arrival information in the next few time slots and develop a predictive location aware admission and selection control algorithm (PLAAS). We further design a greedy predictive location aware admission and selection control algorithm (GPLAAS) to achieve the online implementation of PLAAS approximately and iteratively. Theoretical analysis shows that under any control parameter V > 0, both LAAS and PLAAS algorithm can achieve O(1/V)-optimal average profit, while the sensing request backlog is bounded by O(V). Extensive numerical results based on both synthetic and real trace show that LAAS outperforms the Greedy algorithm and Random algorithm and GPLAAS improves the profit-backlog tradeoff over LAAS.

Message in delay/disruption tolerant network (DTN) is often transmitted in the store/carry/forward mode. In this mode, the transmission process includes two important steps (beaconing and forwarding). Both processes consume much energy which is very precious for the wireless application. At present, most of the works considers just one step in the controlling process (some ignore the beaconing control and some ignore the forwarding control). In addition, state-of-the-art works often assume that there is only one destination for a specific message. In this paper, we consider the optimal forwarding and beaconing control problem at the same time in DTN, where multiple destinations exist. In particular, based on whether those destinations are willing to relay message for others, we consider the selfish and altruistic cases, respectively. Based on these settings, we mathematically characterize this problem as an optimal control problem. The controlling parameters are the forwarding rate and beaconing rate, respectively. Then, we solve the problem by Pontryagin's Maximum Principle, and mathematically prove that both the optimal forwarding and beaconing policies conform to the threshold form. Next, we check the accuracy of the theoretical models by simulations based on both synthetic and real motion traces. Finally, we check the result in a real scenario with 100 students, and show that the optimal policy can really improve the performance.

Routing in delay tolerant network (DTN) closely depends on the cooperation of nodes. At present, there are many works exploring the impact of the selfish behavior. However, most of these works assume that there is only one source-destination pair, and the source just generates one message. For this reason, this paper studies the impact of selfish behavior when many messages are transmitted at the same time. In particular, there are multiple source-destination pairs, and each source generates the messages according to the Poisson process. Due to the selfish behavior, the nodes may be more willing to forward their own messages. In addition, the nodes may have closer relation with certain nodes (friends). Due to selfishness, the nodes may be more willing to help their friends other than the strangers. In such complex scenario, this paper proposes the theoretical models to evaluate the performance of the two-hop routing algorithm with single-copy and redundancy, respectively. Simulations show the accuracy of the models. Numerical results show that the selfish behavior may significantly increase the average delivery delay. In addition, the selfishness may also decrease the maximal flow.

Abstract Message dissemination in delay tolerant networks (DTN) closely depends on the opportunistic contact between nodes. However, the contact is related to the beaconing rate of nodes. In particular, the node has more chances to contact with others if it has bigger beaconing rate, but this will need more energy. In this paper, we study the optimal beaconing policy in DTN with limited energy. In particular, we focus on the case with multiple destinations. According to whether the destinations forward the message to others, we explore the non‐replicative and replicative cases, respectively. First, we introduce a theoretical model for each case, which can be used to evaluate the performance under different beaconing rates. Then, based on these models, we present the corresponding optimization problems. Through Pontryagin's Maximal Principle, we obtain the optimal beaconing rate and prove that the optimal policies conform to the threshold form in both cases. Simulations show the accuracy of our theoretical models. Extensive numerical results show that the performance of the replicative case is better when the energy is limited. Copyright © 2014 John Wiley & Sons, Ltd.

Texture analysis plays an important role in image processing. In the texture analysis field, the regular texture has been studied a lot, but the natural texture with complex backgrounds is less studied. In this paper we bring texture analysis into the study of rice paper's classification. First of all we show the processing flow chart of rice paper classification. By comparing the different kinds of texture analysis methods we choose the LAWS texture method and uncertainty texture spectrum method to achieve the rice paper classification. When we use the two texture analysis methods separately, the classification accuracy of rice paper is lower. So we try to combine the two texture analysis methods. The experimental results show that the classification result when we combine the two texture analysis methods is better than the result when we only use one single texture analysis method. The classification accuracy of rice paper has been distinctly improved after the combination of the two texture analysis methods.

The store‐carry‐forward communication mode in delay tolerant networks closely depends on the opportunistic contacts between the nodes. In addition, there is a higher probability that a node can detect others if the beaconing rate is higher (although this consumes more energy). This study studies the optimal beaconing policy to obtain a trade‐off between the performance and the energy consumption. In particular, the authors consider the case with multiple destinations. According to whether the destinations forward the message to others, they study the problem in non‐replicative and replicative cases, respectively. First, a theoretical framework that can evaluate the trade‐off under different beaconing policies is introduced for each case. Then, they formulate an optimisation problem based on these frameworks. Through Pontryagin's maximal principle, they obtain the optimal policies and prove that the optimal policies conform to the threshold form in both cases. The imulations show the accuracy of the theoretical frameworks. The numerical results show that they can obtain a better trade‐off in the replicative case.

Routing algorithms in delay tolerant networks often need nodes serving as relays for the source to carry and forward message. In particular, nodes should relay the source's packets to others. However, because of the selfish nature, nodes may not relay others' packets to save energy after obtaining message. To make these nodes be cooperative, the source has to pay certain fees to them. Moreover, such fees may be varying with time. On the other hand, if the payment is too much, it may not be cost-effective for the source. Therefore, the total cost may be limited. The main objective of this paper is to explore efficient incentive policies for the source to use its limited cost maximal fees that the source can afford is limited to maximize the probability that the destination obtains the message before the deadline of the message. First, we present a theoretical framework, which can be used to evaluate the performance of different incentive policies. Then, we explore the optimal incentive policy through Pontryagin's maximal principle and prove that the optimal policy conforms to threshold form in certain cases. Simulation results show the accuracy of our theoretical framework. Extensive numerical results show that the optimal policy obtained in this paper is better than other policies. Copyright © 2013 John Wiley & Sons, Ltd.

Exposing digital audio forgeries in time domain is a significant research issue in the audio forensics community. In this paper, we develop an audio forensics method to detect and locate audio forgeries in time domain (including deletion, insertion, substitution and splicing) by analyzing singularity points of audio signals after performing discrete wavelet packet decomposition. Firstly, we observe and point out that a forgery operation in time domain will often generate a singularity point because the correlation property of those samples close to the tampering position has been degraded. Furthermore, we investigate and find that the singularity point resulted from a tampering operation often stays alone while those inherent singularity points in the original signal usually staying in the form of group. Finally, we propose an approach to expose audio forgeries in time domain by introducing Mallat et al.'s wavelet singularity analysis method and making a difference between a forged point and the inherent singularity points. Extensive experimental results have shown that the proposed scheme can better identify whether a given speech file has been tampered (e.g., part of the content deleted or replaced) previously and further locate the forged positions in time domain.