Graph-based Retrieval-Augmented Generation (RAG) has achieved remarkable success in refining the outputs of Large Language Models (LLMs), enabling them to integrate relational and multi-hop knowledge into context-aware responses by constructing a knowledge graph from an external database. In this paper, we focus on the underexplored security risks arising from the external database, and propose the first backdoor attacks against the graph-based RAG of LLMs. Specifically, attackers insert the backdoor into the knowledge graph as entities by poisoning a carefully crafted corpus into the external database, thereby causing LLMs to output attacker-desired answers for trigger-containing queries while preserving correct answers for others. The attacks are formulated as a minimax problem, whose solution is a poison corpus. Powered by the chain-of-thought reasoning capabilities of LLMs, we propose a new strategy to solve the minimax problem. We craft retrieval text to insert triggers into the knowledge graph as entities, exploit hijacking text to redirect LLMs’ attention toward attacker-desired answers, and finally link the hijacking text to the triggers so that it serves as context only for trigger-containing queries. In addition, our attacks involve three types of triggers, including word-level, topic-level, and semantic-level, with progressively increasing stealthiness. Empirical results across multiple knowledge databases and language models indicate that the proposed attacks achieve the desired attack performance. Our findings highlight the substantial risks in LLM applications (e.g., chatbots and agents) built on graph-based RAG systems.

The importance of group communication in the context of the Internet of Things (IoT) is growing, yet the security and stability of this communication are facing significant challenges. The prevailing distributed group key management (GKM) schemes are ill-suited to resource-constrained devices. Furthermore, those that rely on servers are vulnerable to single-point failures and Byzantine risks. The distributed, immutable, and automatic execution of smart contracts on blockchains may offer a potential solution to these problems. This article puts forth a multilayer blockchain-based IoT GKM scheme with Byzantine fault tolerance (BFT). The scheme oversees the management of IoT device subgroups through the deployment of blockchain and smart contracts on edge servers while overseeing the entire device group in a hierarchical structure. A redundant selection mechanism based on hash mapping has been designed to guarantee reliable communication between disparate blockchains and devices. Concurrently, the scheme incorporates a server detection mechanism for Byzantine behavior, thereby ensuring the stability of the blockchain. The results of the experimental analysis demonstrate that the scheme exhibits enhanced security and fault tolerance.

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms.

The ever-increasing requests of users in emergency communication scenarios lead to high data traffic and transmission delay, posing challenges for resource-limited space-air-ground integrated networks (SAGINs). To address this issue, this paper proposes a joint caching optimization and user selection (JCOUS) problem that leverages unmanned aerial vehicle (UAV) caching to maximize the residual energy of the satellite, considering the limited resources of UAVs. To address the complex time-coupling optimization problem with discrete variables, we propose a primal decomposition method to decouple the problem, and design an energy-efficient user selection algorithm with dynamic caching. Furthermore, to reduce computational complexity and cost, we consider a statistical scenario and maximize the statistical residual energy in the JCOUS problem. Simulation results verify that the proposed scheme can achieve a higher residual energy and fast optimization, thus realizing energy saving and quick decision making especially in large-scale computation-intensive SAGINs.

Recently, the lightweight single image super-resolution (SISR) model based on information distillation has attracted the attention of many researchers due to its ability to recover high-resolution images quickly. We reassess and delve into the advantages and disadvantages of information distillation structures, and propose an information scaling distillation network (ISDN) for lightweight single image super-resolution, which can accurately and efficiently restore high-resolution images. By optimizing the distillation branch and feature branch of the information distillation, we meticulously designed the stacked block deep scaling distillation block (DSDB) to enlarge the receptive field and increase the network depth. We mainly optimize and design from two aspects. Firstly, we extract redundant information in the distillation branch and integrate it into multiple layers to form deep information transmission. Secondly, we design a blueprint deep scaling residual (BDSR) in the feature branch, which can extract advanced semantic image information, compress and expand feature channels. The qualitative and quantitative results on various benchmark datasets demonstrate the advantages of our model in terms of model parameters, multiply-accumulate operations, test efficiency, and image reconstruction quality. Code is available at https://github.com/ycLi-CV/ISDN-main.

More and more evidence has demonstrated that microbiota play important roles in the life processes of the human body. In recent years, various computational methods have been proposed for identifying potentially disease-associated microbes to save costs in traditional biological experiments. However, prediction performances of these methods are generally limited by outdated and incomplete datasets. And moreover, until now, there are limited studies that can provide visual predictive tools for inferring possible microbe-disease associations (MDAs) as well. Hence, in this manuscript, a novel webserver called MDADP will be proposed to identify latent MDAs, in which, a new MDA database together with interactive prediction tools for MDAs studies will be designed simultaneously. Especially, in the newly constructed MDA database, 2019 known MDAs between 58 diseases and 703 microbes have been manually collected first. And then, through adopting the average ranking method and the co-confidence method respectively, eight representative computational models have been integrated together to identify potential disease-related microbes. As a result, MDADP can provide not only interactive features for users to access and capture MDAs entities, but alsoeffective tools for users to identify candidate microbes for different diseases. To our knowledge, MDADP is the first online platform that incorporates a new MDA database with comprehensive MDA prediction tools. Therefore, we believe that it will be a valuable source of information for researches in microbiology and disease-related fields. MDADP can be accessed at http://mdadp.leelab2997.cn.

Advances in communication and sensing technologies have enabled low-cost air quality monitoring devices that are easy to deploy. Moreover, the diverse deployment of the devices, which share a huge amount of sensing data, may help monitor and predict air quality at a fine grain granularity. In such context, valuing the data and introducing micropayment may encourage more people to install monitoring devices and share their data. More specifically, the micropayment, a small amount of electronic currency, will be paid for each portion of shared sensing data. To this end, IOTA cryptocurrency shows potential due to its high-speed transactions without transaction fees. This paper introduces a novel IOTA-based micropayment system for air quality monitoring applications. Our system allows IoT devices (i.e., Raspberry Pi) running IOTA clients to exchange the data on the public IOTA network (i.e., the Tangle). We also implement IOTA nodes, which can join the public IOTA or form a private IOTA. Our system has been proven to work well with real air quality monitoring devices. We have also evaluated various system performance parameters, including latency, jitter, and throughput.

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, in the novel neural network model, a new activation function is designed to activate the hidden layer and the output layer based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting Gaussian Interaction Profile kernel (GIP) similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, different frameworks such as the Leave-One-Out Cross Validation (LOOCV) and k-Fold Cross Validation ( k-Fold CV) are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 ± 0.0009 and 0.8955 ± 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies of inflammatory bowel disease (IBD), asthma and obesity demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA-disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leave-one-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004, and 0.9292 for known lncRNA-disease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.

BACKGROUND: Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited. RESULTS: In this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies. CONCLUSIONS: According to the simulation results, DCSMDA can be a great addition to the biomedical research field.

The large number of mobile-sensing vehicles traveling in cities offer a novel solution to the collection of vast amounts of multimedia data packets. When a vehicle passes through the data center (DC), the collected multimedia data packets will be transmitted to the DC. Due to the mobile characteristic of vehicular sensor networks, the main challenge lies in how to improve the multimedia data delivery ratio and balance the data packet collections. In this paper, in consideration of delay and delivery factors, a novel routing method is proposed to optimize multimedia data collections in mobile sensing vehicles (DDSVs). This method targets at balancing multimedia data collections, improving the delivery ratio of the multimedia data, and reducing the delay ratio in Internet of Things (IoT) networks. In the DDSV scheme, two rules are designed for improving the collection of multimedia data in the IoT. These rules pertain to: 1) data and 2) vehicular priorities. First, different regions hold different priorities of data packet transmission, which can improve the delivery ratio in the suburban areas and reduce the delay ratio. Meanwhile, this scheme is capable of guaranteeing the balance of multimedia data collection. Second, the vehicular priority is proportional to the probability of a vehicle reaching a DC. Therefore, the data should be forwarded to vehicles with higher priorities, that is, the vehicles which are more likely to pass by the DC. By using these two rules, the DDSV scheme can improve the performances of the multimedia data delivery ratio, compared with the conventional optimal vehicular data forwarding scheme. In the simulation experiments, the DDSV scheme utilizes multidatasets of Beijing city, where the average delay for data collection can be decreased by 17.3% in general, and by 41.8% in the suburban areas; the average data delivery ratio can be improved by 16.9% in comparison to the previous studies.

Due to overshadow and channel fading, many mobile users are unable to receive the signal transmitted from satellite directly. Hence, some relay stations should be set to help this type of users to receive signals reliably. In this paper, we present a novel cognitive hybrid satellite-terrestrial model, where two cognitive relays forward their received signal for a mobile user successively. Furthermore, we address its achievable rate maximization. We first convert the co-channel interference threshold into transmit power constraints, and then formulate the maximization of the achievable rate as an optimization problem. Based on Karush-Kuhn-Tucker conditions, the optimization problem is decomposed into four cases, each of which is solved in closed form. Simulation study with different system settings is presented, and the efficiency of the proposed power allocation scheme is shown.

Recent growing interest for location‐based services has created a demand on object localisation approaches with low cost and high accuracy. In this study, the problem of distributed multi‐object localisation using fingerprints of received signal strength (RSS) is addressed by combining average consensus and compressed sensing. First, Bayesian compressed sensing is employed at each agent to recover the sparse index vector from RSS measurements, which are corrupted by noises. It relaxes the requirement on accurate prior position knowledge of beacon nodes and is applicable in non‐line‐of‐sight conditions. Then, average consensus is adopted to compel all agents to reach an agreement on the index vector, and in turn, on the location of objects. By using only one‐hop neighbours’ information, the proposed distributed localisation method is applicable to large‐scale networks. Moreover, the final location of each object is obtainable from each individual agent, which makes the proposed method flexible to the network administration. Experimental results are included to demonstrate the effectiveness of the proposed method.

Under a uniform framework, this paper develops a new family of adaptive filtering algorithms, where the wellknown normalized LMS algorithm and normalized constant modulus algorithm (CMA) are included. They all update the weight according to the gradient descent method, but this time a variable and relatively optimal step size is used instead of a constant one. Some application examples are also given to show their efficiencies.