The Linux kernel has been applied in various security-sensitive fields, so ensuring its security is crucial. Vulnerabilities in the Linux kernel are usually caused by undefined behaviors of the C programming language, the most threatening of which are memory safety vulnerabilities. Both the software-based and hardware approaches to memory safety have disadvantages of poor performance, false positives, and poor compatibility. This paper explores the feasibility of using the safe programming language Rust to reconstruct a Linux kernel component and open-source the component's code. We leverage the Rust FFI mechanism to design a safe foreign interface layer to enable the reconstructed component to invoke other Linux functionalities, and then use Rust to reconstruct the component, during which we leverage Rust's type-safety and ownership mechanisms to improve its security, and finally export the C interface of the component to enable the invocation by the Linux kernel. The performance and memory overhead of the reconstructed component, referred to as “rOOM”, were evaluated, revealing a performance overhead of 8.9% in kernel mode, 5% in user mode, 3% in real time, and a memory overhead of 0.06%. These results suggest that it is possible to develop key components of the Linux kernel using Rust in terms of functionality, performance, and memory overhead.

The influence of the gate voltage or base pair ratio modulation on the λ-DNA FET performance was examined. The result of the gate voltage modulation indicated that the captured electrons in the guanine base of the λ-DNA molecules greatly influenced the Id-Vd characteristics, and that of the base pair ratio modulation indicated that the tendency of the conductivity was partly clarified by considering the activation energy of holes and electrons and the length and numbers of the serial AT or GC sequences over which the holes or electrons jumped. In addition, the influence of the dimensionality of the DNA molecule on the conductivity was discussed theoretically.

A T-junction orthomode transducer (OMT) is a waveguide component that separates two orthogonal linear polarizations in the same frequency band. It has a common circular waveguide short-circuited at one end and two branch rectangular waveguides arranged in opposite directions near the short circuit. One of the advantages of a T-junction OMT is its short axial length. However, the two rectangular ports, which need to be orthogonal, have different levels of performance because of asymmetry. We therefore propose a uniaxially symmetrical T-junction OMT, which is configured such that the two branch waveguides are tilted 45° to the short circuit. The uniaxially symmetrical configuration enables same levels of performance for the two ports, and its impedance matching is easier compared to that for the conventional configuration. The polarization separation principle can be explained using the principles of orthomode junction (OMJ) and turnstile OMT. Based on calculations, the proposed configuration demonstrated a return loss of 25dB, XPD of 30dB, isolation of 21dB between the two branch ports, and loss of 0.25dB, with a bandwidth of 15% in the K band. The OMT was then fabricated as a single piece via 3D printing and evaluated against the calculated performance indices.

This paper proposes a non-linear overloaded MIMO detector that outperforms the conventional soft-input maximum likelihood detector (MLD) with less computational complexity. We propose iterative log-likelihood ratio (LLR) estimation and multi stage LLR estimation for the proposed detector to achieve such superior performance. While the iterative LLR estimation achieves better BER performance, the multi stage LLR estimation makes the detector less complex than the conventional soft-input maximum likelihood detector (MLD). The computer simulation reveals that the proposed detector achieves about 0.6dB better BER performance than the soft-input MLD with about half of the soft-input MLD's complexity in a 6×3 overloaded MIMO OFDM system.

In this paper, we consider precoder design for wireless data aggregation in sensor networks. The precoder optimization problem can be formulated as minimization of mean squared error under transmit power and block diagonal constraints. We include statistical correlation of data into the optimization problem, which is appeared in typical applications but is ignored in conventional designing methods. We propose precoder optimization algorithms based on projected gradient descent with projection onto the constraint sets. The proposed method can achieve better performance than the conventional methods that do not incorporate data correlation, especially when data are highly correlated. We also extend the proposed approach to the context of over-the-air computation.

This paper proposes a network design model, considering data consistency for a delay-sensitive distributed processing system. The data consistency is determined by collating the own state and the states of slave servers. If the state is mismatched with other servers, the rollback process is initiated to modify the state to guarantee data consistency. In the proposed model, the selected servers and the master-slave server pairs are determined to minimize the end-to-end delay and the delay for data consistency. We formulate the proposed model as an integer linear programming problem. We evaluate the delay performance and computation time. We evaluate the proposed model in two network models with two, three, and four slave servers. The proposed model reduces the delay for data consistency by up to 31 percent compared to that of a typical model that collates the status of all servers at one master server. The computation time is a few seconds, which is an acceptable time for network design before service launch. These results indicate that the proposed model is effective for delay-sensitive applications.

This paper investigates the effects of backdoor attacks on graph neural networks (GNNs) trained through simple data augmentation by modifying the edges of the graph in graph classification. The numerical results show that GNNs trained with data augmentation remain vulnerable to backdoor attacks and may even be more vulnerable to such attacks than GNNs without data augmentation.

In this letter, we present a new lower bound for the treewidth of a graph in terms of the second smallest eigenvalue of its Laplacian matrix. Our bound slightly improves the lower bound given by Chandran and Subramanian [Inf. Process. Lett., 87 (2003)].

Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.

With the growing complexity and scale of software, detecting and repairing errant behaviors at an early stage are critical to reduce the cost of software development. In the practice of fault localization, a typical process usually includes three steps: execution of input domain test cases, construction of model domain test vectors and suspiciousness evaluation. The effectiveness of model domain test vectors is significant for locating the faulty code. However, test vectors with failing labels usually account for a small portion, which inevitably degrades the effectiveness of fault localization. In this paper, we propose a data augmentation method PVaug by using fault propagation context and variational autoencoder (VAE). Our empirical results on 14 programs illustrate that PVaug has promoted the effectiveness of fault localization.

This letter presents a technique that observes system call mapping behavior of the proxy kernel layer of secure container runtimes. We applied it to file system operations of a secure container runtime, gVisor. We found that gVisor's operations can become more expensive than the native by 48× more syscalls for open, and 6× for read and write.

We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.

Text classification is a fundamental task in natural language processing, which finds extensive applications in various domains, such as spam detection and sentiment analysis. Syntactic information can be effectively utilized to improve the performance of neural network models in understanding the semantics of text. The Chinese text exhibits a high degree of syntactic complexity, with individual words often possessing multiple parts of speech. In this paper, we propose BRsyn-caps, a capsule network-based Chinese text classification model that leverages both Bert and dependency syntax. Our proposed approach integrates semantic information through Bert pre-training model for obtaining word representations, extracts contextual information through Long Short-term memory neural network (LSTM), encodes syntactic dependency trees through graph attention neural network, and utilizes capsule network to effectively integrate features for text classification. Additionally, we propose a character-level syntactic dependency tree adjacency matrix construction algorithm, which can introduce syntactic information into character-level representation. Experiments on five datasets demonstrate that BRsyn-caps can effectively integrate semantic, sequential, and syntactic information in text, proving the effectiveness of our proposed method for Chinese text classification.

Representation learning is a crucial and complex task for multivariate time series data analysis, with a wide range of applications including trend analysis, time series data search, and forecasting. In practice, unsupervised learning is strongly preferred owing to sparse labeling. However, most existing studies focus on the representation of individual subseries without considering relationships between different subseries. In certain scenarios, this may lead to downstream task failures. Here, an unsupervised representation learning model is proposed for multivariate time series that considers the semantic relationship among subseries of time series. Specifically, the covariance calculated by the Gaussian process (GP) is introduced to the self-attention mechanism, capturing relationship features of the subseries. Additionally, a novel unsupervised method is designed to learn the representation of multivariate time series. To address the challenges of variable lengths of input subseries, a temporal pyramid pooling (TPP) method is applied to construct input vectors with equal length. The experimental results show that our model has substantial advantages compared with other representation learning models. We conducted experiments on the proposed algorithm and baseline algorithms in two downstream tasks: classification and retrieval. In classification task, the proposed model demonstrated the best performance on seven of ten datasets, achieving an average accuracy of 76%. In retrieval task, the proposed algorithm achieved the best performance under different datasets and hidden sizes. The result of ablation study also demonstrates significance of semantic relationship in multivariate time series representation learning.

The misalignment of a coupler is a significant issue for capacitive wireless power transfer (WPT). This paper presents a capacitive WPT system specifically designed for underwater drones operating in flowing freshwater environments. The primary design features include a capacitive coupler with an opposite relative position between feeding and receiving points on the coupler electrode, two phase compensation circuits, and a load-independent inverter. A stable and energy-efficient power transmission is achieved by maintaining a 90° phase difference on the coupler electrode in dielectrics with a large unloaded quality factor (Q factor), such as in freshwater. Although a 622-mm coupler electrode is required at 13.56MHz, the phase compensation circuits can reduce to 250mm as one example, which is mountable to small underwater drones. Furthermore, the electricity waste is automatically reduced using the constant-current (CC) output inverter in the event of misalignment where efficiency drops occur. Finally, their functions are simulated and demonstrated at various receiver positions and transfer distances in tap water.

There are several benefits of the information that is invisible to the human eye. “Invisible” here means that it can be visualized or quantified when using instruments. For example, it can improve security without compromising product design. We have succeeded in making an invisible digital image on a metal substrate using periodic repeatability by thin-film interference of niobium oxides. Although this digital information is invisible in the visible light wavelength range of 400-800nm, but detectable in the infrared light that of 800-1150nm. This technology has a potential to be applied to anti-counterfeiting and traceability.

The novel patch element shapes with the interdigital and multi-via structures for mushroom-type metasurface reflectors are proposed for controlling the reflection phases. The interdigital structure provides a wide reflection phase range by changing the depth of the interdigital fingers. In addition, the multi-via structure provides the higher positive reflection phases such as near +180°. The sufficient reflection phase range of 360° and the low polarization dependent properties could be confirmed by the electromagnetic field simulation. The metasurface reflector for the normal incident plane wave was designed. The desired reflection angles and sharp far field patterns of the reflected beams could be confirmed in the simulation results. The prototype reflectors for the experiments should be designed in the same way as the primary reflector design of the reflector antenna. Specifically, the reflector design method based on the ray tracing method using the incident wave phase was proposed for the prototype. The experimental radiation pattern for the reflector antenna composed of the transmitting antenna (TX) and the prototype metasurface reflector was similar to the simulated radiation pattern. The effectiveness of the proposed structures and their design methods could be confirmed by these simulation and experiment results.

Aiming at the problem of “energy hole” caused by random distribution of nodes in large-scale wireless sensor networks (WSNs), this paper proposes an adaptive energy-efficient balanced uneven clustering routing protocol (AEBUC) for WSNs. The competition radius is adaptively adjusted based on the node density and the distance from candidate cluster head (CH) to base station (BS) to achieve scale-controlled adaptive optimal clustering; in candidate CHs, the energy relative density and candidate CH relative density are comprehensively considered to achieve dynamic CH selection. In the inter-cluster communication, based on the principle of energy balance, the relay communication cost function is established and combined with the minimum spanning tree method to realize the optimized inter-cluster multi-hop routing, forming an efficient communication routing tree. The experimental results show that the protocol effectively saves network energy, significantly extends network lifetime, and better solves the “energy hole” problem.

Due to the increase in the volume of data and intensified concurrent requests, distributed caching is commonly used to manage high-concurrency requests and alleviate pressure on databases. However, there is limited research on distributed record mapping caching, and traditional caching algorithms have suboptimal resolution performance for mapping records that typically follow a long-tail distribution. To address the aforementioned issue, in this paper, we propose a recommendation-based adaptive auxiliary caching method, AC-REC, which delivers the primary cache record along with a list of additional cache records. The method uses request correlations as a basis for recommendations, customizes the number of additional cache entries provided, and dynamically adjusts the time-to-live. We conducted evaluations to compare the performance of our method against various benchmark strategies. The results show that our proposed method, as compared to the conventional LCE method, increased the cache hit ratio by an average of 20%, Moreover, this improvement is achieved while effectively utilizing the cache space. We believe that our strategy will contribute an effective solution to the related studies in both traditional network architecture and caching in paradigms like ICN.

Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous researches, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods.