User interest modeling plays a vital role in industrial recommendation systems (RSs). Existing generative recommendation (GR) methods rely on single-step direct inference, which falls short of deeply modeling complex and dynamically evolving user interest. Recent chain-of-thought (CoT)-based GR methods attempt to address this, but either suffer from information loss during semantic space transformation in explicit reasoning or yield uncontrollable, homogeneous reasoning chains in implicit reasoning.

IMS (IP Multimedia Subsystem) is vital for delivering IP-based multimedia services in mobile networks. Despite constant upgrades by 3GPP over the past two decades to support heterogeneous radio access networks (e.g., 4G LTE, 5G NR, and Wi-Fi) and enhance IMS security, the focus has primarily been on cellular infrastructure. Consequently, IMS security measures on mobile equipment (ME), such as smartphones, lag behind rapid technological advancements. Our study reveals that mandated IMS security measures on ME fail to keep pace, resulting in new vulnerabilities and attack vectors, including denial of service (DoS) across all networks, named SMS source spoofing, and covert communications over Video-over-IMS attacks. All vulnerabilities and proof-of-concept attacks have been experimentally validated in operational 5G/4G networks across various phone models and network operators. Finally, we propose and prototype standard-compliant remedies for these vulnerabilities.

Federated learning (FL) supports distributed training of a global machine learning model across multiple Internet of Things (IoT) devices with the help of a central server. However, data heterogeneity across different IoT devices leads to the client model drift issue and results in model performance degradation and poor model fairness. To address the issue, we design federated learning with global–local knowledge fusion (FedKF) scheme in this article. The key idea in FedKF is to let the server return the global knowledge to be fused with the local knowledge in each training round so that the local model can be regularized toward the global optima. Therefore, the client model drift issue can be mitigated. In FedKF, we first propose the active–inactive model aggregation technique that supports a precise global knowledge representation. Then, we propose a data-free knowledge distillation (KD) approach to enable each client model to learn the global knowledge (embedded in the global model) while each client model can still learn the local knowledge (embedded in the local data set) simultaneously, thereby realizing the global–local knowledge fusion process. The theoretical analysis and intensive experiments demonstrate the superiority of FedKF over previous solutions.

Cellular networks, vital for delivering emergency services, enable mobile users to dial emergency calls (e.g., 9–1-1 in the U.S.), which are forwarded to public safety answer points (PSAPs). Regulatory requirements allow anonymous user equipment (UE) without a SIM card or valid mobile subscription to access these services. However, supporting emergency services for anonymous UEs introduces different operations, expanding the attack surface of cellular infrastructure. In this study, we explore the insecurity of cellular emergency services, identifying six security vulnerabilities. These vulnerabilities can be exploited for free data service attacks against carriers and data DoS/overcharge and denial of cellular emergency service (DoCES) attacks against mobile users. Experimental validation in networks of three major U.S. carriers and two major Taiwan carriers demonstrates the global impact of our findings. Finally, we propose and prototype standard-compliant remedies to mitigate these vulnerabilities.

Nowadays, both Internet Application Service (IAS) providers and users face various security threats and legal issues. Due to the lack of reliable user information verification mechanisms, adversaries can abuse IASs to launch various cyberattacks, such as misinformation distributing and phishing, by using fake user accounts. IAS providers may thus inadvertently offer inappropriate content to restricted users, thereby suffering a serious risk of prosecution under local or international laws. Also, IAS users may suffer from nefarious ID theft attacks. In this paper, we proposed a novel security framework,${{\sf MPKIX}}$, designated as Mobile-assisted PKIX (Public-Key Infrastructure X.509).${{\sf MPKIX}}$secures both IAS providers and users by leveraging the broadly used PKIX services and mobile networked systems. It not only provides IAS providers with a reliable user verification mechanism while simultaneously enabling cross-IAS user privacy protection, but also largely mitigates the possibility of ID theft attacks and benefits other involved parties, such as cellular network operators and PKIX service providers. We further conduct a security analysis of${{\sf MPKIX}}$and implement an${{\sf MPKIX}}$prototype. The evaluation results based on the prototype confirm the effectiveness and efficiency of${{\sf MPKIX}}$with low overhead.

Cellular networks that offer ubiquitous connectivity have been the major medium for delivering emergency services. In the U.S., mobile users can dial an emergency call with 911 for emergency uses in cellular networks, and the call can be forwarded to public safety answer points (PSAPs), which deal with emergency service requests. According to regulatory authority requirements for the cellular emergency services, anonymous user equipment (UE), which does not have a SIM (Subscriber Identity Module) card or a valid mobile subscription, is allowed to access them. Such support of emergency services for anonymous UEs requires different operations from conventional cellular services, and can therefore increase the attack surface of the cellular infrastructure. In this work, we are thus motivated to study the insecurity of the cellular emergency services and then discover four security vulnerabilities from them. Threateningly, they can be exploited to launch not only free data service attacks against cellular carriers, but also data DoS/overcharge and denial of cellular emergency service (DoCES) attacks against mobile users. All vulnerabilities and attacks have been validated experimentally as practical security issues in the networks of three major U.S. carriers. We finally propose and prototype standard-compliant remedies to mitigate the vulnerabilities.

The Internet-of-Things (IoT) attracts great attention in the past few years. With millions of devices connected to the network, data are generated at an unprecedented speed and the data must be stored efficiently in the database to serve spatial queries. In existing spatial databases that use space-filling curves to organize the data, they store spatial data without considering on-road data distribution. This will introduce unnecessary computation and I/O cost in the service of users' queries about data on the roads. In this paper, we present a Road-Aware Spatial Mapping of data to the storage, or RASM for short, which can be applied in spatial databases for highly efficient storage and query services for moving objects. Usually, a space-filling curve, such as the Hilbert curve, is used to map data in a cell of a geographical area to a segment of linear storage space. However, in a road-network system where data are most distributed and queried along the roads, using a generic square cell as a mapping unit to aggregate data is in conflict with the data use pattern. In RASM, road segment, instead of the cell, is used as the unit of space mapping and data storage so that data requested in a road query can be stored together to enable efficient I/O. Furthermore, a substantial computation may be required to identify mapping units covered in a query in a geometric space. As RASM has grouped data in the road-segment units, one can efficiently found the units covered in a road query, which is usually concerned only about data on a few segments of roads. We implemented a prototype query-serving system using RASM to map data on road segments to a linear space enabled by LevelDB, a widely-used key-value store. Experiment results with real-world traffic data show that with RASM, the road query time can be reduced by up to 43%, and the I/O traffic can be reduced by up to 70%. In the meantime, other queries about geographical regions are well supported in RASM with minimal performance impacts.

BACKGROUND: Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs. RESULTS: In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods. CONCLUSIONS: WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

Bacterial small non-coding RNAs (sRNAs) play important roles in various physiological processes, and predicting sRNAs is an important task. In this paper, we develop a computational method for the sRNA prediction by using sRNA sequence-derived features. We investigate a variety of sRNA sequence-derived features, and evaluate the usefulness of features for the sRNA prediction. Then, we develop the sequence learning ensemble method, which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs, and the genetic algorithm is adopted to optimize the parameters in the ensemble system. In the computational experiments, we compile a balanced dataset and four imbalanced datasets, and evaluate our method on these datasets by using 5-fold cross validation. The sequence learning ensemble method can achieve AUC scores greater than 0.9, and outperforms existing state-of-the-art sRNA prediction methods. In conclusion, the proposed method has a great potential for sRNA prediction. The source codes, datasets and supplementary are available in http://www.bioinfotech.cn/BIBM2017/SLEM.