Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains, with applications ranging from robotic operations to autonomous driving. With recent advancements in artificial intelligence (AI), learning-based components, especially AI controllers, have become essential in enhancing the functionality and efficiency of CPSs. However, the lack of interpretability in these AI controllers presents challenges to the safety and quality assurance of AI-enabled CPSs (AI-CPSs). Existing methods for improving the safety of AI controllers often involve neural network repair, which requires retraining with additional adversarial examples or access to detailed internal information of the neural network. Hence, these approaches have limited applicability for black-box policies, where only the inputs and outputs are accessible during operation. To overcome this, we propose MORTAR, a runtime action repair framework designed for AI-CPSs in this work. MORTAR begins by constructing a prediction model that forecasts the quality of actions proposed by the AI controller. If an unsafe action is detected, MORTAR then initiates a repair process to correct it. The generation of repaired actions is achieved through an optimization process guided by the safety estimates from the prediction model. We evaluate the effectiveness of MORTAR across various CPS tasks and AI controllers. The results demonstrate that MORTAR can efficiently improve task completion rates of AI controllers under specified safety specifications. Meanwhile, it also maintains minimal computational overhead, ensuring real-time operation of the AI-CPSs.

Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational data. This paper introduces subgraph queries as a new task for deep graph learning. Unlike traditional graph prediction tasks that focus on individual components like link prediction or node classification, subgraph queries jointly predict the components of a target subgraph based on evidence that is represented by an observed subgraph. For instance, a subgraph query can predict a set of target links and/or node labels. To answer subgraph queries, we utilize a probabilistic deep Graph Generative Model. Specifically, we inductively train a Variational Graph Auto-Encoder (VGAE) model, augmented to represent a joint distribution over links, node features and labels. Bayesian optimization is used to tune a weighting for the relative importance of links, node features and labels in a specific domain. We describe a deterministic and a sampling-based inference method for estimating subgraph probabilities from the VGAE generative graph distribution, without retraining, in zero-shot fashion. For evaluation, we apply the inference methods on a range of subgraph queries on six benchmark datasets. We find that inference from a model achieves superior predictive performance, surpassing independent prediction baselines with improvements in AUC scores ranging from 0.06 to 0.2 points, depending on the dataset.

Breast cancer is not preventable because of its unknown causes. However, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make much more precise and reliable decisions than before. K-nearest neighbors, support vector machine, decision tree, Naive Bayes, AdaBoost, linear discriminant analysis, random forest, logistic regression, and artificial neural network are some of the methods employed. This paper applied a distinctive integration of evaluation measures and ML algorithms using the wrapper feature selection based on ICA (WFSIC) and BA (WFSB) separately. We compared two proposed approaches for the performance of the classifiers. Also, we compared our best diagnostic model with previous works reported in the literature survey. Experimentations were performed on the Wisconsin diagnostic breast cancer dataset. Results reveal that the proposed framework that uses the BA with an accuracy of 99.12\%, surpasses the framework using the ICA and most previous works. Additionally, the RF classifier in the approach of FS based on BA emerges as the best model and outperforms others regarding its criteria. Besides, the results illustrate the role of our techniques in reducing the dataset dimensions up to 90\% and increasing the performance of diagnostic models by over 99\%. Moreover, the result demonstrates that there are more critical features than the optimum dataset obtained by proposed FS approaches that have been selected by most ML models.

There has been significant recent progress to reduce the computational effort of static IR drop analysis using neural networks, and modeling as an image-to-image translation task. A crucial issue is the lack of sufficient data from real industry designs to train these networks. Additionally, there is no methodology to explain a high-drop pixel in a predicted IR drop image to its specific root-causes. In this work, we first propose a U-Net neural network model with attention gates which is specifically tailored to achieve fast and accurate image-based static IR drop prediction. Attention gates allow selective emphasis on relevant parts of the input data without supervision which is desired because of the often sparse nature of the IR drop map. We propose a two-phase training process which utilizes a mix of artificially-generated data and a limited number of points from real designs. The results are, on-average, 18% (53%) better in MAE and 14% (113%) in F1 score compared to the winner of the ICCAD 2023 contest (and U-Net only) when tested on real designs. Second, we propose a fast method using saliency maps which can explain a predicted IR drop in terms of specific input pixels contributing the most to a drop. In our experiments, we show the number of high IR drop pixels can be reduced on-average by 18% by mimicking upsize of a tiny portion of PDN's resistive edges.

The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality. The recent advent of large language models (LLMs) provides disruptively new solution to this long-standing problem and latest researches have reported over 90% F1 in extracting correct conditions from MOFs literature. We argue in this paper that most existing synthesis extraction practices with LLMs stay with the primitive zero-shot learning, which could lead to downgraded extraction and application performance due to the lack of specialized knowledge. This work pioneers and optimizes the few-shot in-context learning paradigm for LLM extraction of material synthesis conditions. First, we propose a human-AI joint data curation process to secure high-quality ground-truth demonstrations for few-shot learning. Second, we apply a BM25 algorithm based on the retrieval-augmented generation (RAG) technique to adaptively select few-shot demonstrations for each MOF's extraction. Over a dataset randomly sampled from 84,898 well-defined MOFs, the proposed few-shot method achieves much higher average F1 performance (0.93 vs. 0.81, +14.8%) than the native zero-shot LLM using the same GPT-4 model, under fully automatic evaluation that are more objective than the previous human evaluation. The proposed method is further validated through real-world material experiments: compared with the baseline zero-shot LLM, the proposed few-shot approach increases the MOFs structural inference performance (R^2) by 29.4% in average.

Bank credit risk is a significant challenge in modern financial transactions, and the ability to identify qualified credit card holders among a large number of applicants is crucial for the profitability of a bank'sbank's credit card business. In the past, screening applicants'applicants' conditions often required a significant amount of manual labor, which was time-consuming and labor-intensive. Although the accuracy and reliability of previously used ML models have been continuously improving, the pursuit of more reliable and powerful AI intelligent models is undoubtedly the unremitting pursuit by major banks in the financial industry. In this study, we used a dataset of over 40,000 records provided by a commercial bank as the research object. We compared various dimensionality reduction techniques such as PCA and T-SNE for preprocessing high-dimensional datasets and performed in-depth adaptation and tuning of distributed models such as LightGBM and XGBoost, as well as deep models like Tabnet. After a series of research and processing, we obtained excellent research results by combining SMOTEENN with these techniques. The experiments demonstrated that LightGBM combined with PCA and SMOTEENN techniques can assist banks in accurately predicting potential high-quality customers, showing relatively outstanding performance compared to other models.

Language Processing (NLP) tasks, including in the Our overall best submission to the task achieved assessment of textual entailment relations. However, a macro F1-score of 0.80 (1st place on the leaderboard), these models are heavily susceptible to shortcut a consistency score of 0.72 (15th), and a learning (Du et al., 2023), factual inconsistency faithfulness score of 0.83 (11th). Our method excels (Xie et al., 2023), and performance degradation in classification accuracy, but fails at being when exposed to data from specialized domains, robust to perturbations on the statements, i.e. predicting such as in the case of medical data.

Class imbalance refers to the significant difference in the number of samples from different classes within a dataset, making it challenging to identify minority class samples correctly. This issue is prevalent in real-world classification tasks, such as software defect prediction, medical diagnosis, and fraud detection. The synthetic minority oversampling technique (SMOTE) is widely used to address class imbalance issue, which is based on interpolation between randomly selected minority class samples and their neighbors. However, traditional SMOTE and most of its variants only interpolate between existing samples, which may be affected by noise samples in some cases and synthesize samples that lack diversity. To overcome these shortcomings, this paper proposes the Minimum Enclosing Ball SMOTE (MEB-SMOTE) method from a geometry perspective. Specifically, MEB is innovatively introduced into the oversampling method to construct a representative point. Then, high-quality samples are synthesized by interpolation between this representative point and the existing samples. The rationale behind constructing a representative point is discussed, demonstrating that the center of MEB is more suitable as the representative point. To exhibit the superiority of MEB-SMOTE, experiments are conducted on 15 real-world imbalanced datasets. The results indicate that MEB-SMOTE can effectively improve the classification performance on imbalanced datasets.

In the digital age, the emergence of deepfakes and synthetic media presents a significant threat to societal and political integrity. Deepfakes based on multi-modal manipulation, such as audio-visual, are more realistic and pose a greater threat. Current multi-modal deepfake detectors are often based on the attention-based fusion of heterogeneous data streams from multiple modalities. However, the heterogeneous nature of the data (such as audio and visual signals) creates a distributional modality gap and poses a significant challenge in effective fusion and hence multi-modal deepfake detection. In this paper, we propose a novel multi-modal attention framework based on recurrent neural networks (RNNs) that leverages contextual information for audio-visual deepfake detection. The proposed approach applies attention to multi-modal multi-sequence representations and learns the contributing features among them for deepfake detection and localization. Thorough experimental validations on audio-visual deepfake datasets, namely FakeAVCeleb, AV-Deepfake1M, TVIL, and LAV-DF datasets, demonstrate the efficacy of our approach. Cross-comparison with the published studies demonstrates superior performance of our approach with an improved accuracy and precision by 3.47% and 2.05% in deepfake detection and localization, respectively. Thus, obtaining state-of-the-art performance. To facilitate reproducibility, the code and the datasets information is available at https://github.com/vcbsl/audiovisual-deepfake/.

Object recognition technologies hold the potential to support blind and low-vision people in navigating the world around them. However, the gap between benchmark performances and practical usability remains a significant challenge. This paper presents a study aimed at understanding blind users' interaction with object recognition systems for identifying and avoiding errors. Leveraging a pre-existing object recognition system, URCam, fine-tuned for our experiment, we conducted a user study involving 12 blind and low-vision participants. Through in-depth interviews and hands-on error identification tasks, we gained insights into users' experiences, challenges, and strategies for identifying errors in camera-based assistive technologies and object recognition systems. During interviews, many participants preferred independent error review, while expressing apprehension toward misrecognitions. In the error identification task, participants varied viewpoints, backgrounds, and object sizes in their images to avoid and overcome errors. Even after repeating the task, participants identified only half of the errors, and the proportion of errors identified did not significantly differ from their first attempts. Based on these insights, we offer implications for designing accessible interfaces tailored to the needs of blind and low-vision users in identifying object recognition errors.