Automatic algorithm design is generally achieved by the Learning to Optimize (L2O) framework with two steps: the inner step solves a problem min xf(x) using an algorithm A, while the outer step optimizes A to obtain the best optimization performance Ef[f(xA*)] (xA* is the best solution found by A). However, solving the outer-step optimization problem is extremely challenging because the execution of algorithm A is often complex, making xA* non-differentiable w.r.t. f and A, necessitating the use of derivative-free methods with limited theoretical guarantees. To address this challenge, we propose Positive Momentum Natural Evolution Strategies (PM-NES), whose core idea is only retaining samples whose function value exceeds a baseline b for updating. Under standard assumptions: 1) L-Lipschitz continuity of the objective function and 2) the bounded decision variable and function value space, we theoretically establish the monotonic improvement property of PM-NES in the infinite-sample case and derive improvement bounds in the finite-sample case using Bernstein inequalities. We validate PM-NES on three L2O tasks: learning the optimal relaxation factor for the SOR, training an adaptive subspace selection policy for differential evolution (DE) and learning an adaptive search policy for Pareto local search (PLS). Empirical results show that PM-NES converges faster than NES and achieves performance comparable to SGD by improving the efficiency of updating. Moreover, PM-NES can learn better strategies for DE and PLS than handcrafted ones. These results demonstrate PM-NES is an effective and practical optimizer for the outer step in L2O.

Large code language models (LCLMs) have revolutionized code-related tasks, yet their deployment in real-world software engineering introduces critical security challenges. As LCLMs increasingly interact with adversarial environments, understanding their vulnerabilities and developing robust defenses becomes imperative. This review systematically examines emerging adversarial threats and countermeasures, aligning with the CIA triad—confidentiality, integrity, and availability. We categorize attacks into three key frontiers: poisoning attacks (compromising integrity availability), adversarial attacks (undermining integrity), and privacy attacks (breaching confidentiality). Our study synthesizes 100+ papers spanning AI, security, and software engineering, offering the most extensive analysis to date on LCLM adversarial risks. We dissect threat models, attack methodologies, and mitigation strategies while introducing novel insights on explainable AI (XAI) and the interplay between risk categories. Finally, we highlight unresolved challenges and future research directions to advance secure LCLM adoption. By bridging theoretical and practical security gaps, this work provides a foundational roadmap for developing resilient LCLMs in adversarial settings.

Buildings account for substantial global energy consumption, with heating, ventilation, and air conditioning (HVAC) systems as major contributors. We study the setpoint schedule optimization of HVAC systems that minimize both energy costs and occupant discomfort. Since building performance simulation (BPS) tools provide high-fidelity models of building dynamics, integrating simulation with optimization is expected to obtain an effective schedule for building energy management. Consequently, many simulation-based optimization methods that integrate BPS into optimization processes are proposed. However, these methods still face challenges due to non-analytical system dynamics, computational complexity, and the lack of theoretical convergence guarantees. To address these challenges, a Lagrangian relaxation-based simulation optimization (LRSO) method is developed in this paper. A dynamic linear surrogate model iteratively refines itself with simulation outputs, balancing tractability and accuracy. Within Lagrangian relaxation framework, the problem is decomposed into simulation and optimization subproblems, which can be solved in a coordinated and decomposed way. The surrogate subgradient method further ensures the convergence. Experimental results demonstrate its superior performance in minimizing energy cost and occupant discomfort across all test scenarios, with computational times suitable for real-time scheduling.

Establishing efficient and robust covert channels is crucial for secure communication within insecure network environments. With its inherent benefits of decentralization and anonymization, blockchain has gained considerable attention in developing covert channels. To guarantee a highly secure covert channel, channel negotiation should be contactless before the communication, carrier transaction features must be indistinguishable from normal transactions during the communication, and communication identities must be untraceable after the communication. Such a full-lifecycle covert channel is indispensable to defend against a versatile adversary who intercepts two communicating parties comprehensively (e.g., on-chain and off-chain). Unfortunately, it has not been thoroughly investigated in the literature. We make the first effort to achieve a full-lifecycle covert channel, a novel blockchain-based covert channel named ABC-Channel. We tackle a series of challenges, such as off-chain contact dependency, increased masquerading difficulties as growing transaction volume, and time-evolving, communicable yet untraceable identities, to achieve contactless channel negotiation, indistinguishable transaction features, and untraceable communication identities, respectively. We develop a working prototype to validate ABC-Channel and conduct extensive tests on the Bitcoin testnet. The experimental results demonstrate that ABC-Channel achieves substantially secure covert capabilities. In comparison to existing methods, it also exhibits state-of-the-art transmission efficiency.

Power systems with high renewable penetration face significant challenges in generation expansion planning (GEP) because renewable output uncertainty is not only inherent but also decision-dependent: newly installed renewable capacity directly expands the feasible range of renewable generation, giving rise to decision‑dependent uncertainty (DDU). Conventional formulations may either neglect the decision dependence or require specialized solution techniques when DDU is embedded in robust optimization. To address this problem, this paper establishes a hybrid stochastic-robust GEP framework that explicitly incorporates DDU and proposes an equivalent model transformation that rigorously converts the DDU set into a decision‑independent one. This transformation renders the problem directly solvable by the classical column-and-constraint generation (CCG) algorithm without altering its convergence mechanism. Interday uncertainty is captured by K‑medoids clustering of historical days supplemented with extreme days, while intraday uncertainty is represented by scenario-specific uncertainty sets and solved using the CCG algorithm. Numerical tests on the IEEE 24‑bus and IEEE 118‑bus systems demonstrate that the proposed method yields cost‑effective and robust expansion plans with high feasibility under additional validation scenarios and competitive computational efficiency.

Range counting is a core primitive in geographic information systems. When data is distributed across multiple organizations, conducting range counting raises substantial privacy concerns. Existing privacy-preserving protocols focus on protecting organizations' datasets, but cannot simultaneously achieve efficiency, query privacy, and accuracy on overlapping data. Typical protocols process query range in plaintext for efficient point-in-range evaluation, since query-private designs rely on expensive secure comparisons. Moreover, most works assume non-overlapping datasets across organizations, which leads to huge errors in overlapping scenarios. In this paper, we propose PPRC, the first protocol that jointly satisfies all the privacy, efficiency, and accuracy requirements. PPRC makes two key technical contributions. First, we design the Private Range Predicate (PRP) technique that supports efficient point-in-range evaluation while protecting the query range. PRP reformulates range evaluation as encrypted membership tests, effectively replacing costly secure comparisons with faster secure multiplications. Second, we propose Oblivious Linear Counting (OLC), an aggregation scheme that efficiently and securely aggregates partial results from organizations with overlapping data. OLC involves only lightweight cryptographic operations and ensures that no information is leaked beyond the final range count. We theoretically analyze the accuracy, efficiency, and security of PPRC. Experiments on real-world and synthetic datasets show that PPRC achieves up to 55x smaller errors and 37x speedup compared to baseline protocols.

Ensuring resilience in multienergy systems (MESs) has become increasingly urgent and challenging due to the growing frequency and severity of extreme events, such as natural disasters, extreme weather, and cyber–physical attacks. Among the various approaches to enhancing MES resilience, hydrogen integration offers significant potential in cross-temporal, cross-spatial, and cross-sector flexibility, as well as black-start capability. Although considerable efforts have been devoted to this area, a systematic review of resilience enhancement in hydrogen-enabled MESs (HMESs) is still lacking. To address this gap, this article presents a comprehensive review of HMES resilience enhancement. First, advantages, vulnerabilities, and challenges related to HMES resilience enhancement are summarized. Next, a resilience enhancement framework for HMESs is proposed, based on which existing resilience metrics and event-oriented contingency models are reviewed and discussed. Planning measures are then classified according to the types of hydrogen-related facilities, together with uncertainty handling methods, scenario generation methods, and planning problem formulation frameworks. In addition, operational enhancement measures are categorized into three response stages: prevention, emergency response, and restoration. Finally, research gaps are identified, and future directions are discussed, including comprehensive resilience metric design, advanced extreme-event scenario generation, spatiotemporal cyber–physical contingency modeling under compound extreme events, coordinated planning and operation across multiple networks and timescales, low-carbon resilient planning and operation, and large language model (LLM)-assisted whole-process resilience enhancement.

As micro-architectures evolve with increasing complexity, the design process needs to account for a broad range of parameters, which leads to expansive design spaces. Design space exploration (DSE) for multiple metrics with limited simulation resources is a challenging task. In this paper, we focus on multi-objective (performance and area of processor) optimization, where one of the objectives (performance) is infeasible to model analytically and the other (area) is easy to model analytically. Most of the current black-box optimization methods are data-driven, which means that they require a large amount of simulation data to model the optimization metrics for high-dimensional and discrete problems. Based on the mixed integer linear programming (MILP), the Surrogate Objective-based Parallel Multi-objective Optimization Algorithm (SOP-MOOA) is proposed to reduce the data requirements by integrating mechanism constraints and human experience. The proposed method adopts the separability assumption to establish a surrogate objective function that is ordinal consistent with the real black-box objective for paralleling a single objective model. The white box objective is modeled as a series of arithmetic constraints, thus transforming the multi-objective optimization problem into a series of parallel single-objective optimization problems. The Pareto-optimal set is explored by iteratively constructing a series of parallel single-objective black-box optimization problems. The experimental results show that the proposed algorithm outperforms HEBO, a state-of-the-art Bayesian optimization (BO) method, combined with NSGA-II in terms of the quality of the Pareto-optimal set and the recommended points.

Photovoltaic (PV) systems serve as essential power generation units in DC microgrids. Therefore, ensuring their reliable and efficient operation remains a fundamental challenge. Nevertheless, the operation of PV system is susceptible to different types of disturbances, including internal nonlinear dynamics, environmental conditions, and operational states of either DC microgrids or main grids. Here, we propose an observer-based nonrecursive controller for PV-interfaced boost converters to achieve accurate and rapid regulation of PV array voltage even when large disturbances occur. First, the dynamics of the PV system are converted to a controllable canonical form via exact feedback linearization, by which the real-time PV power, parameter uncertainties, and unmodeled dynamics are packed into lumped disturbances. Subsequently, disturbance observation techniques are utilized to estimate these disturbances, and the estimated values are incorporated into the design of the feedforward compensation loops. Finally, a systematic coordinate transformation with feedback control techniques yields a compact controller that facilitates the practical implementation. On this basis, a rigorous stability analysis is conducted using Lyapunov stability theory. The effectiveness of the proposed controller is validated through Matlab/Simulink simulations, real-time simulation on the RT-LAB platform, and experimental results obtained from a laboratory-scale PV-based DC microgrid testbed. The evaluation includes typical operating scenarios as well as comparative studies with state-of-the-art control methods, which demonstrate the superior voltage tracking performance of the proposed controller.

The domain name system (DNS) is indispensable to nearly every Internet service. It has been extensively utilized for network activity characterization in passive and active approaches. Compared to the passive approach, active DNS cache probing is lightweight and non-cooperative, enabling world-wide characterization of remote network activities in different networks. Unfortunately, existing probing-based methods are too coarse-grained to characterize the time-varying features of network activities, substantially limiting their applications in time-sensitive tasks. In this paper, we advance DNSScope, a temporally fine-grained DNS cache probing framework by addressing three key challenges: cache entanglement, sample sparsity, and observational distortion. DNSScope introduces three novel probing strategies, extending active DNS cache probing from single-cache to heterogeneous recursive DNS (R-DNS) resolvers. It synthesizes statistical learning and transfer learning to achieve time-varying characterization of remote network activity. Extensive evaluations demonstrate DNSScope’s adaptability for R-DNS resolvers with diverse cache structures and its effectiveness in accurately estimating time-varying DNS query arrival rates, achieving an average mean absolute error of 0.124, as low as one-sixth that of the baseline methods. We also demonstrate DNSScope’s application to network anomaly detection.

Establishing efficient and robust covert channels is crucial for secure communication within insecure network environments. With its inherent benefits of decentralization and anonymization, blockchain has gained considerable attention in developing covert channels. To guarantee a highly secure covert channel, channel negotiation should be contactless before the communication, carrier transaction features must be indistinguishable from normal transactions during the communication, and communication identities must be untraceable after the communication. Such a full-lifecycle covert channel is indispensable to defend against a versatile adversary who intercepts two communicating parties comprehensively (e.g., on-chain and off-chain). Unfortunately, it has not been thoroughly investigated in the literature. We make the first effort to achieve a full-lifecycle covert channel, a novel blockchain-based covert channel named ABC-Channel. We tackle a series of challenges, such as off-chain contact dependency, increased masquerading difficulties as growing transaction volume, and time-evolving, communicable yet untraceable identities, to achieve contactless channel negotiation, indistinguishable transaction features, and untraceable communication identities, respectively. We develop a working prototype to validate ABC-Channel and conduct extensive tests on the Bitcoin testnet. The experimental results demonstrate that ABC-Channel achieves substantially secure covert capabilities. In comparison to existing methods, it also exhibits state-of-the-art transmission efficiency.

Thermal comfort prediction plays a critical role in intelligent building control, yet it remains challenging under data scarcity. While external data can potentially improve prediction performance, effectively utilizing heterogeneous distributed data under privacy constraints remains challenging because not all external knowledge is equally relevant to the target environment. To address this limitation, this study proposes Federated Learning with Evaluation Layer (FedEL), a host-centric framework that enables a target client to evaluate and selectively integrate model updates from multiple sources. By incorporating a local evaluation mechanism and a ranking-based weighting strategy, FedEL prioritizes updates that are most relevant to the host objective while maintaining robustness under limited data. Experiments conducted on a multi-source thermal comfort dataset demonstrate that FedEL improves personalized thermal comfort prediction under heterogeneous data conditions. The proposed framework achieves the greatest benefits when local data are limited, where effective utilization of external knowledge is particularly important, while remaining competitive with local training and transfer learning when sufficient local data are available. The proposed framework provides a practical mechanism for privacy-preserving knowledge sharing across distributed data sources, enabling data-limited participants to benefit from external information without compromising data ownership. This capability supports more effective and accessible thermal comfort modeling and offers strong potential for real-world intelligent building applications.

This article focuses on the challenging problems for robust load frequency control (RLFC) in multi-area power systems considering unknown parameter uncertainties in both load frequency control (LFC) operation conditions of nominal power systems and coupling inputs under dynamically changing reconfigurable communication networks by developing an autonomous gain scheduling scheme. We consider a class of coupled smart grids (e.g., multi-energy coupling microgrids as a modern multi-area multi-source power system that can realize multi-energy complementarity and comprehensive utilization improving energy efficiency) in which the process dynamics are composed of time-varying vector functions of scalar combinations of the states and dynamically changing networks. By incorporating the impact of generation-rate constraints (GRC), we propose a performance estimation index to evaluate and determine the optimal configuration of communication networks in multi-area power systems. In the event of link failures due to interference or when partial limits exceed predefined security redundancy, the automatic decision function is activated, scheduling reconfigurable communication modes and adjusting controller gains accordingly. Based on this, we first develop an RLFC strategy via a distributed framework that is driven by a dynamically reconfigurable communication network and a time-varying switching scheme. The proposed strategy can handle the system coupling dynamics to achieve global exponential stability for multi-area power systems with multiple aggregated uncertainties and GRC limiters. Furthermore, an RLFC strategy via an exponentially distributed adaptive framework is proposed to schedule the control gains and manage time-varying adaptive coupling dynamics for the multi-area power systems with multiple GRC limiters and aggregated system uncertainties. These uncertainties consist of aggregated parameter uncertainties, coexisting matched, and mismatched parameter uncertainties that can be unknown, where the continuous excitation conditions are equivalent to matrix inequality conditions to ensure exponential stability at the origin. The effectiveness of the proposed strategies is verified via a three-area power system with dynamically changing configurable communication networks.

Hydrogen-based multi-energy systems (HMES) have emerged as a promising low-carbon and energy-efficient solution, as it can enable the coordinated operation of electricity, heating and cooling supply and demand to enhance operational flexibility, improve overall energy efficiency, and increase the share of renewable integration. However, the optimal operation of HMES remains challenging due to the nonlinear and multi-physics coupled dynamics of hydrogen energy storage systems (HESS) (consisting of electrolyters, fuel cells and hydrogen tanks) as well as the presence of multiple uncertainties from supply and demand. To address these challenges, this paper develops a comprehensive operational model for HMES that fully captures the nonlinear dynamics and multi-physics process of HESS. Moreover, we propose an enhanced deep reinforcement learning (DRL) framework by integrating the emerging representation learning techniques, enabling substantially accelerated and improved policy optimization for spatially and temporally coupled complex networked systems, which is not provided by conventional DRL. Experimental studies based on real-world datasets show that the comprehensive model is crucial to ensure the safe and reliable of HESS. In addition, the proposed SR-DRL approaches demonstrate superior convergence rate and performance over conventional DRL counterparts in terms of reducing the operation cost of HMES and handling the system operating constraints. Finally, we provide some insights into the role of representation learning in DRL, speculating that it can reorganize the original state space into a well-structured and cluster-aware geometric representation, thereby smoothing and facilitating the learning process of DRL.

Hydrogen-based backup systems (HBSs) are a promising alternative to diesel generators (DGs) in Internet data centers (IDCs) because of their high efficiency, fast response, and near-zero on-site emissions. However, their performance in terms of reliability, cost, and carbon emissions is not yet fully understood. This paper proposes an HBS for IDCs, where medium-temperature fuel cells (MTFCs) produce electricity and waste heat, and absorption chillers (ACs) use this heat to provide cooling. A unified model is developed to describe the links between hydrogen-based energy supply, computing workloads, and indoor temperature control. To evaluate both planning and operation performance, a Sequential Monte Carlo Simulation (SMCS) method is used, with Gaussian copula–based scenario generation and Sobol sequences to improve sampling efficiency. A case study of a pilot IDC in Yulin, China, shows that at a hydrogen price of $1/kg, the HBS can reach an average reliability of service of 99.999%, cut yearly operating costs by $3,750, and lower carbon emissions by 23,333 kg per rack compared with DG-based systems. These results show that HBSs can deliver high-reliability and low-carbon backup power for sustainable data center operation.

The heating, ventilation and air-conditioning (HVAC) systems dominate building’s energy consumption and meanwhile exhibit substantial operational flexibility that can be exploited for providing grid services. However, this goal is largely hindered by the difficulty to characterize the system’s operating flexibility due to the complex building thermal dynamics, physical operating limits and human comfort constraints. To address this challenge, this paper develops a unified virtual battery (VB) modeling framework for characterizing the operating flexibility of both single-zone and multi-zone building HVAC systems, enabling responsive buildings to function like virtual batteries. Specifically, a physically meaningful representation state is first identified to represent building thermal conditions under thermal comfort constraints and then a VB model is established for characterizing the operating flexibility of single-zone HVAC systems. We subsequently extend the VB modeling framework to multi-zone HVAC systems and establish zone VB models to characterize the heterogeneous zonal operating flexibility. We further develop a systematic method to aggregate the zone VB models into a unified VB model. The proposed VB model enables a low-order storage-like representation of the operational flexibility of both single-zone and multi-zone buildings. Case studies demonstrate that the VB model can well capture the building thermal dynamics under varying HVAC control policies. In addition, the VB model is validated through demand response (DR) of buildings. The optimal DR strategies can be obtained from the low-order and low-complexity VB model, and the building’s committed DR can be efficiently decomposed to zone-level control inputs while maintaining human thermal comfort.

Initial alignment is a crucial stage in navigation because it directly determines the navigation accuracy of the global positioning system (GPS)-inertial navigation system (GINS) that consists of the GPS and the strapdown inertial navigation system. However, GPS outliers and their resultant initial velocity bias error, both caused by the GPS signal blockage and/or reflection, severely degrade the GINS initial alignment accuracy. In addition, the inertial measurement unit (IMU) bias can also cause the cumulative bias error to adversely affect the alignment accuracy. In order to simultaneously suppress these errors for the GINS initial alignment, we propose a factor graph optimization (FGO) method that models the initial velocity bias as a state. Specifically, the IMU bias and the initial velocity bias are estimated and then compensated to improve the alignment accuracy. The Huber norm is employed to suppress the GPS outliers and thus the stable alignment process can be achieved. A global observability analysis is conducted, showing that the newly augmented initial velocity bias, together with the other estimated states, is observable when the vehicle trajectory contains a constant-attitude straight-line motion interval during which the specific-force derivative vectors at two distinct instants are linearly independent. The car-mounted experiment results demonstrate that the proposed FGO method can effectively suppress the aforementioned errors.

The high penetration of volatile renewable power has created an urgent need for fast methods to solve large scale security-constrained unit commitment (SCUC) problems. In this paper, we propose a multi-timescale learn-to-optimize (MT L2O) method to efficiently solve large-scale stochastic SCUC. The coarse timescale serves as a learning-based presolving stage. At this scale, we propose a group-based multi-resolution formulation where machine learning (ML) techniques are used to predict integer variables and fix those with high confidence for finer timescales. Therefore, problems at finer timescales are reduced in size and can be solved more efficiently. Compared to existing works, when learning at the coarse scale, binary status constraints spanning multiple time periods become inactive. This not only significantly reduces data dimensionality but also eliminates the need for feasibility recovery measures targeting these constraints. At finer timescales, we develop constraint reduction methods for two types of constraints based on the on/off status determined at coarser timescales. Numerical experiments on IEEE 118-bus system, IEEE 300-bus system, and a 3266-bus system based on a practical provincial grid in China demonstrate that our methods obtain near-optimal solutions with an average performance gap not exceeding 0.4% and achieve speedups of 3.03 to 34.46 times compared to the commercial solver Gurobi.

Counting distinct elements (cardinality) across multiple data holders (DHs) privately is fundamental with broad applications, ranging from crowd counting to network monitoring. It is referred to as private distributed cardinality estimation (PDCE). Similarly, determining the intersection cardinality between the unions of two DH groups holds practical significance, a problem we call private distributed intersection cardinality estimation (PDICE). While many efficient methods (e.g., FM and LL sketches) exist, their differential privacy relies on secret hash functions. In PDCE, DHs must share the functions to enable sketch merging, which breaks this assumption and invalidates such guarantees. Although a recent protocol implements the FM sketch on a secret-sharing-based multiparty computation (MPC) framework for PDCE, we observe that it lacks differential privacy guarantees and is computationally expensive. To address these limitations, we propose DP-DICE-Bino, a novel protocol that is computationally efficient and differentially private for PDCE. DP-DICE-Bino is flexible in that it can compute the cardinality of any group of DHs without repeatedly interacting with the DHs. Furthermore, DP-DICE-Bino can also handle PDICE. Experiments show that DP-DICE-Bino achieves orders-of-magnitude speedups and reduces the estimation error by several times compared with the state of the art under the same security requirements.