Stateful P4 network functions introduce operational failures that emerge only under temporally ordered packet sequences and control-plane states. Existing temporal verifiers (e.g., P4TV) stop at logical verdicts, while dynamic testers (P4Testgen, CHIMERA) execute packets without temporal specifications, and both require operators to hand-author formal specifications. We present IntentP4, a formal-methods-aided pipeline that closes this loop: it translates an operator's natural-language intent into a P4LTL specification and then into a replayable multi-packet test case (packet sequence, control-plane rules, external operations, oracles), grounded throughout in compiler artifacts via a tool-queryable ProgramContext and gated by deterministic per-stage validators. On five stateful P4 programs spanning access control, monitoring, heavy-hitter detection, failure recovery, and load balancing, the P4LTL-to-test generator produces 10 scenarios, 89 packets, and 110 unified execution operations that pass eight consistency checks; on 11 specification subtasks, 4 strictly pass and 3 are semantically close; and an integrated BMv2/Mininet loop exposes runtime failures including a firewall policy-bypass manifestation and a missing multi-table control-plane entry under controller convergence.

With the increasing adoption of Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, ensuring the adequacy of retrieved documents has become critically important for generation quality. Traditional reranking approaches face three significant challenges: substantial computational overhead that scales with document length, dependency on plain text that limits application in sensitive scenarios, and insufficient assessment of document value beyond simple relevance metrics. We propose EAReranker, an efficient embedding-based adequacy assessment framework that evaluates document utility for RAG systems without requiring access to original text content. The framework quantifies document adequacy through a comprehensive scoring methodology considering verifiability, coverage, completeness and structural aspects, providing interpretable adequacy classifications for downstream applications. EAReranker employs a Decoder-Only Transformer architecture that introduces embedding dimension expansion method and bin-aware weighted loss, designed specifically to predict adequacy directly from embedding vectors. Our comprehensive evaluation across four public benchmarks demonstrates that EAReranker achieves competitive performance with state-of-the-art plaintext rerankers while maintaining constant memory usage ($\sim$550MB) regardless of input length and processing 2-3x faster than traditional approaches. The semantic bin adequacy prediction accuracy of 92.85\% LACC@10 and 86.12\% LACC@25 demonstrates its capability to effectively filter out inadequate documents that could potentially mislead or adversely impact RAG system performance, thereby ensuring only high-utility information serves as generation context. These results establish EAReranker as an efficient and practical solution for enhancing RAG system performance through improved context selection while addressing the computational and privacy challenges of existing methods.

Abstract The proliferation of high-performance personal devices and the widespread deployment of machine learning (ML) applications have led to two consequences: the volume of private data from individuals or groups has exploded over the past few years; and the traditional central servers for training ML models have experienced communication and performance bottlenecks in the face of massive amounts of data. However, this reality also provides the possibility of keeping data local for ML training and fusing models on a broader scale. As a new branch of ML application, Federated Learning (FL) aims to solve the problem of multi-party joint learning on the premise of protecting personal data privacy. However, due to the heterogeneity of devices, including network connection, network bandwidth, computing resources, etc., it is unrealistic to train, update and aggregate models in all devices in parallel, while personal data is often not independent and identically distributed (Non-IID) due to multiple reasons. This reality poses a challenge to the speed and convergence of FL. In this paper, we propose the pFedCAM algorithm, which aims to improve the robustness of the FL system to device heterogeneity and Non-IID data, while achieving some degree of federation model personalization. pFedCAM is based on the idea of clustering and model interpolation by classifying heterogeneous clients and performing FedAvg algorithm in parallel, and then combining them into personalized federated global models by inter-cluster model interpolation. Experiments show that the accuracy of pFedCAM improves 10.3% on Fashion-MNIST and 11.3% on CIFAR-10 compared to the benchmark in the case of Non-IID data. In the end, we applied pFedCAM in HomeProtect, a smart home privacy protection framework we designed, and achieved good practical results in the case of flame recognition.

With the proliferation of smart home, the research on the attack and protection methods of smart home privacy has gradually increased. Research has shown that adversaries can infer users' privacy information by analyzing smart home traffic traces. To address this problem, we investigated and summarized the existing smart home traffic obfuscation schemes. Firstly, the current situation that smart home privacy is easy to be leaked through traffic is explained. The necessity of smart home privacy data protection and challenges faced are expounded. Then, the smart home traffic obfuscation technologies are classified and summarized, including data packet filling, traffic shaping, false traffic injection, user simulation and adversarial learning. The limitations and application scenarios are analyzed. Finally, the paper holds the view that the smart home traffic obfuscation technology based on user simulation and adversarial learning is the direction with great development potential.

Clone attack is considered as a severely destructive threat in Internet of Things (IoT), because: 1) the attack may be easily launched due to the deficiency of hardware architecture and the limited resources against physical capture and compromise and 2) it may trigger a large variety of insider and outsider attacks. Different from traditional clone attack detection approaches that ground on a large amount of data traversing the network (e.g., locations and identities), this article tackles this problem by answering the following fundamental questions: do we really need so much raw information? whether there is an alternative for local event detection by a node far away from that event? when acquiring/tracing global knowledge of a system/network, do we really need a global collection effort? These questions are of much importance in a large variety of networks. Specifically, this article provides a collective memory design for global topology and identity tracing (GTI Tracing), via a localized computing paradigm within neighborhood. This localized paradigm computationally builds a connection of identity and topology from time and space domain to a new computation domain. Such a computation domain retains four properties: 1) transitivity; 2) global convergence; 3) determinacy; and 4) causality. With byte-size information at an arbitrary device, it can recover and keep tracing global topology and identity information, and thus providing deterministic detection of clones. Both theoretical analysis and experimental study have shown the advantages of the proposed design in both detection accuracy and privacy protection, at a cost of light communication, storage, and computation overhead at each device.

In social activities, people are interested in some statistical data, such as purchase records, monthly consumption, and health data, which are usually utilized in recommendation systems. And it is seductive for them to acquire the ranking of these data among friends or other communities. In the meantime, they want their privacy data to be confidential. Therefore, a strategy is presented to allow users to obtain the result of calculating their privacy data while preserving these data. In this method, firstly a polynomial approximation function model is set up for each user. Afterwards, “fragment” the coefficients of each model into pieces. Eventually “blend” all scraps to build the global model of all users. Users can use the global model to gain their corresponding ranking results after a special computing. Security analyses of three aspects elaborate the validity of proposed privacy method, even if some spiteful attackers try to steal private data of users, no matter who they are (users or someone outside the community). Experiments results manifest that the global model competently fits all users data and all privacy data are protected.

Though there exist ready-made DSRC/WiFi/3G/4G cellular systems for roadway communications, there are common defects in these systems for roadway safety oriented applications and the corresponding challenges remain unsolved for years, i.e., WiFi cannot work well in vehicular networks due to the high probability of packet loss caused by burst communications, which is a common phenomenon in roadway networks; 3G/4G cannot well support real-time communications due to the nature of their designs; DSRC lacks the support to roadway safety oriented applications with hard realtime and reliability requirements [1]. To solve the conflict between the capability limitations of existing systems and the ever-growing demands of roadway safety oriented communication applications, we propose a novel system design and implementation for realtime reliable roadway communications, aiming at providing safety messages to users in a realtime and reliable manner. In our extensive experimental study, the latency is well controlled within the hard realtime requirement (100ms) for roadway safety applications given by NHTSA [2], and the reliability is proved to be improved by two orders of magnitude compared with existing experimental results [1]. Our experiments show that the proposed system for roadway safety communications can provide guaranteed highly reliable packet delivery ratio (PDR) of 99% within the hard realtime requirement 100ms under various scenarios, e.g., highways, city areas, rural areas, tunnels, bridges. Our design can be widely applied for roadway communications and facilitate the current research in both hardware and software design and further provide an opportunity to consolidate the existing work on a practical and easy-configurable low-cost roadway communication platform.

In this paper we propose and analyze a localized backbone renovating algorithm (LBR) to renovate a broken backbone in the network. This research is motivated by the problem of virtual backbone maintenance in wireless ad hoc and sensor networks, where the coverage area of nodes are disks with identical radii. According to our theoretical analysis, the proposed algorithm has the ability to renovate the backbone in a purely localized manner with a guaranteed connectivity of the network, while keeping the backbone size within a constant factor from that of the minimum CDS. Both the communication overhead and computation overhead of the LBR algorithm are O(k), where k is the number of nodes broken or added. We also conduct extensive simulation study on connectivity, backbone size, and the communication/computation overhead. The simulation results show that the proposed algorithm can always keep the renovated backbone being connected at low communication/computation overhead with a relatively small backbone, compared with other existing schemes. Furthermore, the LBR algorithm has the ability to deal with arbitrary number of node failures and additions in the network.