ABSTRACT Recommendation systems are rapidly evolving from static interaction‐driven models to dynamic, knowledge‐augmented architectures. A key challenge in this evolution is accurately capturing users' fine‐grained preferences from unstructured review text, which directly impacts the explainability and personalization of recommendations. As an essential enabling technology, Aspect‐Based Sentiment Analysis (ABSA) extracts aspect‐level sentiment elements that can be explicitly mapped to user preference vectors or product attribute ratings. With the integration of semantic and syntactic information, current works have significantly enhanced the performance of ABSA. However, existing graph‐based approaches that rely on dependency‐tree structures often converge to suboptimal solutions when handling implicit sentiment in natural language. To address this gap, we propose a graph fusion network that leverages augmented semantic and syntactic graphs. Specifically, we explicitly model word‐dependency correlations via contextual augmentation, and incorporate selected part‐of‐speech (POS) features to refine semantic graph construction. Concurrently, a syntactic graph is constructed by pruning the nodes based on the distance to the aspect term. The resulting semantic and syntactic representations are then fused through a dual graph convolutional network block, whereas the gating mechanism is used to regulate information flow during graph construction. Experiments on seven benchmarks demonstrate that our approach outperforms baselines by up to and in Macro‐F1 scores, establishing new state‐of‐the‐art results and providing a more reliable sentiment extraction module for downstream recommendation tasks.

Modern power analysis attacks (PAAs) pose a significant threat to hardware security, and reliably securing integrated systems against advanced PAAs has become an essential design target. The fundamental principle of PAA detection lies in identifying voltage drops induced by malicious probe insertion. However, conventional detection methods often suffer from reduced accuracy when voltage information is obscured by noise. To address this limitation, a real-time PAA detection technique is proposed to achieve high detection accuracy even in environments with significant voltage noise. The voltages of power grid nodes are initially acquired through voltage sensors, and the voltage differential between power grid nodes is evaluated by performing multiple voltage comparisons within a time period, which effectively mitigates the noise effects. Then, these differential measurements are processed by a linear support vector machine (SVM) model to identify anomalous voltage drops. To further optimize hardware efficiency, a reinforcement learning-based method is developed to determine sensor deployment, minimizing power and area overheads while maintaining detection accuracy. Experimental validation of our method demonstrates a high detection accuracy of 87.13% for 0.2 Ω resistance insertions, even under severe noise conditions (20% ofVdd).

Modern power analysis attacks (PAAs) pose significant threats to hardware security, and reliably securing integrated systems against advanced PAAs has become a significant design target in integrated circuits. However, the detection accuracy of most countermeasures to PAAs significantly decreases when power side-channel information is mixed with voltage noise. In this paper, a real-time PAA detection technique is proposed to achieve high detection accuracy even with large voltage noise. The voltage drops of certain power grid (PG) nodes caused by PAA are evaluated by a number of voltage comparisons between PG nodes, which compensate for the effects of voltage noise. These voltage comparison results are analyzed by machine learning algorithms, and a linear support vector machine (SVM) model is selected as the PAA detection model. The PAAs on an IBM benchmarked microprocessor are applied to evaluate the detection accuracy, and our proposed PAA detection method achieves 93.11% accuracy in detecting a resistance of 1 Ω with noise equal to 20% of Vdd. Furthermore, the power and area overheads (evaluated using a 65-nm CMOS process) of this method are reduced by 68% and 75%, respectively, compared to those of the existing machine learning-based PAA detection techniques.

Hardware Trojans may cause security issues in cyber-physical systems (CPSs), and recently proposed mutual auditing frameworks have helped build trustworthy CPSs with untrustworthy devices by requiring neighboring devices from different vendors. However, this may cause severe multi-vendor integration challenges, such as expensive, hard-to-maintain, and insufficient vendors to purchase devices. In this work, we improve the mutual auditing framework by maintaining the security of the CPSs with fewer vendors. First, the vendor-constrained security auditing framework is introduced to enhance the security of the CPS network with limited vendors, where side auditing detects the hardware Trojan collusion between neighboring nodes and infected node isolation stops the spread of active HTs. Second, a multi-agent cooperative reinforcement learning-based method is proposed to assign devices with proper vendors in the context of security auditing, and it provides solutions with a minimized number of offline nodes due to the HT infection. The experimental results show that our proposed method reduces the number of vendors needed by 40.95%, and only causes an increment of 0.39% infected nodes.

Failure recovery is one of the most essential problems in Internet of Things (IoT) systems, and the conventional snapshot method is an effective way to solve this problem. However, snapshot methods lack specialized designs for heterogeneous IoT devices, and when implemented in edge devices, serious system interruptions occur and performance is impacted. To address these problems, a dynamic checkpointing strategy is proposed for IoT systems that consist of heterogeneous devices. Firstly, an anomaly detection network for snapshots (i.e., ADSnet) that combines long short-term memory networks with multilayer convolutional networks is used to learn the multidimensional features of system resource usage. Secondly, ADSnet is tuned during deployment to learn the behaviors of target devices, so that ADSnet can report the anomalies of target devices in the near future. Finally, a dynamic checkpointing strategy is proposed to dynamically create snapshots on the basis of the anomaly detection results. The experimental results show that the proposed ADSnet achieves 97.73% accuracy in detecting anomalies in the target device; furthermore, our proposed dynamic checkpointing strategy reduces 25.4% snapshots than that created by the recently proposed ResCheck.

In recent years, learning-based single image dehazing networks have been comprehensively developed. However, performance improvement is limited due to domain shift between trained synthetic hazy images and untrained real-world hazy images. To alleviate this issue, this paper proposes a real-world dehazing targeted training scheme which nearly realizes paired real-world data training. As a result, a Twofold Multi-scale Generative Adversarial Network (TMS-GAN) consisting of a Haze-generation GAN (HgGAN) and a Haze-removal GAN (HrGAN) is designed. HgGAN attributes real haze properties to synthetic images and HrGAN removes haze from both synthetic and generated fake realistic data under supervision. Thus, the proposed method can better adapt to real-world image dehazing using this cooperative training scheme. Meanwhile, several structural advances of TMS-GAN also improve dehazing performance. Specifically, a haze residual map based on atmospheric scattering model is deduced in HgGAN for fake realistic data generation. The dual-branch generator in HrGAN draws attention to detail restoration by one branch along with another color-branch. A plug-and-play Multi-attention Progressive Fusion Module (MAPFM) is proposed and inserted in both HgGAN and HrGAN. MAPFM incorporates multi-attention mechanism to guide multi-scale feature fusion in a progressive manner, in which Adjacency-attention Block (AAB) can capture contributing features of each level and Self-attention Block (SAB) can establish non-local dependency of feature fusion. Experiments on mainstream benchmarks show that the proposed framework is superior especially on real-world hazy images among single image dehazing methods.

Machine learning approaches have been widely used to tackle the problem of sensor array drift in E‐Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM_T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM_T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

In this paper, we focus on the low-power scheduling problem with multiple threshold and/or supply voltage technologies in high-level synthesis. We propose a unified scheduling approach which is applicable to various optimization problems, including: 1) dynamic power and resource usage co-optimization; 2) leakage power optimization; and 3) dynamic power and leakage power co-optimization. To deal with different objectives with high flexibility, three problems are divided into two common subproblems including delay assignment and resource density variance minimization, then a vertex potential-based mobility allocation model is proposed to solve two subproblems simultaneously. Experimental results show that, for dynamic power and resource co-optimization, our scheduling approach produces optimum solutions for all six benchmarks with 15 groups of data; for leakage power optimization it also greatly excels the latest existing work, by 20% leakage power reduction and 52 times speedup. Besides, for dynamic and leakage power co-optimization, the Pareto solutions are studied.

Data path interconnection on VLSI chips usually consumes a significant amount of both power and area. In this paper, we focus on the port assignment problem for binary commutative operators for interconnection complexity reduction. First, the port assignment problem is formulated on a constraint graph, and a practical method is proposed to find a valid and initial solution. For solution optimization, an elementary spanning-tree-transformation-based local search algorithm is proposed. To improve the efficiency of optimization, a matrix formulation, which meets the simplex tabuleau format, is proposed and thus the simplex method is adopted for optimization. Moreover, operation pivoting and successive pivoting are discussed for algorithm speedup. The experimental results show that on the randomly generated test cases, the matrix-based algorithm shows the highest solution optimality and is five times faster than the elementary transformation method. On the real high-level synthesis benchmarks, the matrix-based method reduced 14% interconnections, while the previous greedy algorithm reduced 8% on average.

The exponential increase in leakage power and the substantial power-saving opportunities provided by scheduling have made dual-threshold voltage (dual-Vth) an attractive choice for low-leakage-power designs. In this paper, we work under the assumption that functional units (FUs) are allocated after scheduling, and fully explore the solution space of scheduling with dual-Vthoperations to optimize the leakage power of the FUs. First, a binding conflict graph (BCG)-based scheduling method is presented to minimize the number of FUs. Second, the BCG-based method is extended to allow scheduling with dual-Vthoperation targeting the minimization of leakage power. In timing-constrained scheduling, each operation in the data flow is initialized with low-Vth. Then, starting from an operation schedule with the timing constraint satisfied, we scale the sets of low-Vthoperations in the off-critical paths with high-Vthso as to reduce the number of low-VthFUs without increasing the total delay. Finally, a scheduling method for minimizing the leakage power under both timing and resource constraints is presented. The results of benchmark tests show that the proposed algorithms can reduce the leakage power reported in previous works by 10.2% while maintaining high circuit performance.

In this paper, we address the problem of scheduling operations into control steps with dual threshold voltage (dual-Vth) technique under timing and resource constraints. Recently, some scheduling methods are proposed based on the mobility overlap removal, and it is a hard problem to remove the mobility overlap optimally. There might be no feasible solution with an improper mobility overlap removal. In this work, we implement the mobility overlap removal together with dual threshold voltage technique to minimize the total leakage power. A simulated-annealing based method is introduced to explore the optimal solution. For each mobility overlap removal, a probability-based method is proposed to schedule operations at appropriate control steps, and to assign them with proper threshold voltages. The experimental results show the effectiveness of the proposed method.