The domain knowledge graph exhibits characteristics of incompleteness and semantic complexity, which lead to shortcomings in the extraction and selection of rules, thereby affecting its inferential capabilities. A rule extraction model that integrates relationship and structural encoding is proposed to address this issue. A multidimensional embedding approach is achieved by extracting relational and structural information from the target subgraph and conducting feature encoding. A self-attention mechanism is designed to integrate relational and structural information, enabling the model to capture dependency relationships and local structural information in the input sequence better. This enhancement improves the understanding and expressive capabilities of context of the model, thus addressing the challenges of rule extraction and selection in the complex semantic situations. The experimental results for actual industrial datasets of automotive component failures and public datasets demonstrate improvements in the proposed model for link prediction and rule quality evaluation tasks. When the rule length is 3, an average increase of 7.1 percentage points in the mean reciprocal rank (MRR) and an average increase of 8.6 percentage points in Hits@10 are observed. For a rule length of 2, an average increase of 7.4 percentage points in MRR and an average increase of 3.9 percentage points in Hits@10 are observed. This confirms the effectiveness of relational and structural information in rule extraction and inference.
Third-party software systems play a significant role in modern software development. Software developers build software based on requirements by retrieving appropriate dependency libraries from third-party software repositories, effectively avoiding repetitive wheel-building operations and thus speeding up the development process. However, retrieving third-party dependency libraries can be challenging. Typically, third-party software repositories provide preset tags (categories) for software developers to search. However, when a software’s preset tags are incorrectly labeled, software developers are unable to find the libraries required, and this inevitably affects the development process. This study proposes a software clustering model to address the aforementioned challenges. The model combines method vectors, method importance, and text vectors to categorize unknown categories of software into known categories. In addition, because no publicly available dataset exists for this problem, we built a dataset and made it publicly available. This clustering model was tested on a self-built dataset comprising 30 categories and software systems from the Maven repository. The accuracy of the prediction category was 70% for one candidate (top-1) and 90% for three candidates (top-3). The experimental results show that our model can help software developers find suitable software, can be useful for classifying software systems in open-source repositories, and can assist software developers in quickly locating third-party libraries.
Crop diseases are one of the main factors threatening crop growth. In this regard, machine-learning algorithms can efficiently detect large-scale crop diseases and are beneficial for timely processing and improving crop yield and quality. In large-scale agricultural scenarios, owing to limitations in power supply and other conditions, the power-supply requirements of high-computing-power devices such as servers cannot be fulfilled. Most existing deep-network models require high computing power and cannot be deployed easily on low-power embedded devices, thus hindering the accurate identification and application of large-scale crop diseases. Hence, this paper proposes a lightweight crop-disease-recognition algorithm based on knowledge distillation. A student model based on a residual structure and the attention mechanism is designed and knowledge distillation is applied to complete transfer learning from the ConvNeXt model, thus achieving the lightweight model while maintaining high-precision recognition. The experimental results show that the accuracy of image classification for 39 types of crop diseases is 98.72% under a model size of 2.28 MB, which satisfies the requirement for deployment in embedded devices and indicates a practical and efficient solution for crop-disease recognition.
Monocular three-dimensional (3D) object detection is a fundamental but challenging task in autonomous driving and robotic navigation. Directly predicting object depth from a single image is essentially an ill-posed problem. Geometry projection is a powerful depth estimation method that infers an object’s depth from its physical and projected heights in the image plane. However, height estimation errors are amplified by the depth error. In this study, the physical and projected heights of object surface points (rather than the height of the object itself) were estimated to obtain several depth candidates. In addition, the uncertainties in the heights were estimated and the final object depth was obtained by assembling the depth predictions according to the uncertainties. Experiments demonstrated the effectiveness of the depth estimation method, which achieved state-of-the-art (SOTA) results on a monocular 3D object detection task of the KITTI dataset.
To improve the ability to predict future events and effectively address uncertainty, we propose a network architecture based on graph augmentation and attention mechanisms for uncertainty forecasting in multivariate time series. By introducing an implicit graph structure and integrating graph neural network techniques, we capture the mutual dependencies among sequences to model the interactions between time series. We utilize attention mechanisms to capture temporal patterns within the same sequence for modeling the dynamic evolution patterns of time series. We utilize the Monte Carlo dropout method to approximate model parameters and model the predicted sequences as a stochastic distribution, thus achieving accurate uncertainty forecasting in time series. The experimental results indicate that this approach maintains a high level of prediction precision while providing reliable uncertainty estimation, thus providing confidence for use in decision-making tasks.
Based upon path query information, we propose a graph attention model that effectively integrates textual and graph structure information in knowledge graphs, thereby enhancing knowledge graph completion. For textual information, a dual-encoder based on pre-trained language models is utilized to separately obtain embedding representations of entities and path query information. Additionally, an attention mechanism is employed to aggregate path query information, which is used to capture graph structural information and update entity embeddings. The model was trained using contrastive learning and experiments were conducted on multiple knowledge graph datasets, with good results achieved in both transductive and inductive settings. These results demonstrate the advantage of combining pre-trained language models with graph neural networks to effectively capture both textual and graph structural information, thereby enhancing knowledge graph completion.
Online social media are frequently used by people as a way of expressing their thoughts and feelings. Among the vast amounts of online posts, there may be more concerning ones expressing potential grievances and mental illnesses. Identifying these along with potential causes of mental health problems is an important task. Observing these posts, it is found that there is a label co-occurrence phenomenon in contexts, i.e., the semantics of multiple candidate labels appear in the context of one sample, which interferes with the modeling and prediction of label patterns. To mitigate the impact of this phenomenon, we propose a label-aware data augmentation method, which leverages large-scale pre-trained language models with excellent text comprehension capability to identify potential candidate labels, abates the noise from irrelevant co-occurring labels by estimating sample-independent label semantic strengths, and constructs well-performing classifiers with pre-trained language models. Extensive experiments validate the effectiveness of our model on the recent datasets Intent_SDCNL and SAD.
Diffusion models have revolutionized text-to-image synthesis, enabling users to generate high-quality and imaginative artworks from simple natural-language text prompts. Unfortunately, due to the large and unfiltered training dataset, inappropriate content such as nudity and violence can be generated from them. To deploy such models at a higher level of safety, we propose a novel method, directional contrastive language-image pre-training (CLIP) loss-based fine-tuning, dubbed as CLIF. This method utilizes directional CLIP loss to suppress the model’s inappropriate generation ability. CLIF is lightweight and immune to circumvention. To demonstrate the effectiveness of CLIF, we proposed a benchmark called categorized toxic prompts (CTP) to evaluate the ability to generate inappropriate content for text-to-image diffusion models. As shown by our experiments on CTP and common objects in context (COCO) datasets, CLIF is capable of significantly suppressing inappropriate generation while preserving the model’s ability to produce general content.
In response to the existing shortcomings of large computational complexity, poor real-time performance, and deployment difficulties in current algorithms, and to meet the high requirements of real-time performance and accuracy for infrared detection systems, proposes a lightweight algorithm deployed on domestically produced embedded chips, termed YOLOv5-TinyHisi. The YOLOv5-TinyHisi algorithm undertakes lightweight modifications to the backbone network structure based on the characteristics of infrared small targets. Additionally, it utilizes SIoU optimized loss function for boundary error, thereby enhancing the accuracy of infrared small target localization. The YOLOv5-TinyHisi algorithm model is deployed on Hi3531DV200, utilizing the chip-integrated neural network inference engine (NNIE) to accelerate network inference. Experimental results on public datasets demonstrate that the algorithm achieves a 1.52% improvement in average precision (mAP) compared to YOLOv5, while significantly reducing parameter count and model size. On the Hi3531DV200, the inference speed for a single image with a resolution of (1280 × 512) pixels reaches 35 frames per second (FPS), with a recall rate of 95%, meeting the real-time and accuracy requirements of the infrared detection system.
An and-inverter graph (AIG) is a representation of electrical circuits typically passed as input into a model checker. In this paper, we propose an AIG structural encoding that we use to extract the features of AIGs and construct a portfolio-based model checker called Liquid. The underlying concept of the proposed structural encoding is the enumeration of all possible AIG substructures, with the frequency of each substructure encoded as a feature vector for use in subsequent machine-learning processes. Because the performance of model-checking algorithms varies across different AIGs, Liquid combines multiple such algorithms and selects the algorithm appropriate for a given AIG via machine learning. In our experiments, Liquid outperformed all state-of-the-art model checkers in the portfolio, achieving a high prediction accuracy. We further studied the effectiveness of Liquid from several perspectives.
The operating system is the core and foundation of the entire computer system. Its reliability and safety are vital because faults or vulnerabilities in the operating system can lead to system crashes, data loss, privacy breaches, and security attacks. In safety-critical systems, any errors in the operating system can result in significant loss of life and property. Ensuring the safety and reliability of the operating system has always been a major challenge in industry and academia. Currently, methods for verifying the operating system’s safety include software testing, static analysis, and formal methods. Formal methods are the most promising in ensuring the operating system’s safety and trustworthiness. Mathematical models can be established using formal methods, and the system can be formally analyzed and verified to discover potential errors and vulnerabilities. In the operating system, formal methods can be used to verify the correctness and completeness of the operating system’s functions and system safety. A formal scheme for embedded operating systems is proposed herein on the basis of existing formal verification achievements for operating systems. This scheme uses VCC (verified C compiler), CBMC (C bounded model checker), and PAT (process analysis toolkit) tools to verify the operating system at the unit, module, and system levels, respectively. The schema, upon being successfully applied to a task scheduling architecture case of a certain operating system, exhibits a certain universality for analyzing and verifying embedded operating systems.
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