Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. However, a significant area overlooked within these fields is solution transparency for the human-machine interaction space, as the sub-symbolic methods employed for map generation do not account for the explainability of the solutions generated. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap. Our method for environment-type classification observes ontological reasoning used to synthesise the context of an environment through the features found within. We achieve explainability within the model by presenting operators with environment-type classifications overlayed by a semantically labelled occupancy map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of the Canberra region, demonstrating method effectiveness. We assessed the system through both simulations and real-world trials.
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Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fields such as reinforcement learning, robotics, and computer vision. Their incorporation has been rapid and transformative, marking a significant paradigm shift in the field of machine learning research. However, the field of experimental design, grounded on black-box optimization, has been much less affected by such a paradigm shift, even though integrating LLMs with optimization presents a unique landscape ripe for exploration. In this position paper, we frame the field of black-box optimization around sequence-based foundation models and organize their relationship with previous literature. We discuss the most promising ways foundational language models can revolutionize optimization, which include harnessing the vast wealth of information encapsulated in free-form text to enrich task comprehension, utilizing highly flexible sequence models such as Transformers to engineer superior optimization strategies, and enhancing performance prediction over previously unseen search spaces.
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic complexities issues. Mamba, as an emerging state space model (SSM), possesses superior linear complexity and global receptive fields. Despite this, it can hardly be applied to DG to address distribution shifts, due to the hidden state issues and inappropriate scan mechanisms. In this paper, we propose a novel framework for DG, named DGMamba, that excels in strong generalizability toward unseen domains and meanwhile has the advantages of global receptive fields, and efficient linear complexity. Our DGMamba compromises two core components: Hidden State Suppressing~(HSS) and Semantic-aware Patch refining~(SPR). In particular, HSS is introduced to mitigate the influence of hidden states associated with domain-specific features during output prediction. SPR strives to encourage the model to concentrate more on objects rather than context, consisting of two designs: Prior-Free Scanning~(PFS), and Domain Context Interchange~(DCI). Concretely, PFS aims to shuffle the non-semantic patches within images, creating more flexible and effective sequences from images, and DCI is designed to regularize Mamba with the combination of mismatched non-semantic and semantic information by fusing patches among domains. Extensive experiments on four commonly used DG benchmarks demonstrate that the proposed DGMamba achieves remarkably superior results to state-of-the-art models. The code will be made publicly available.
A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and recent models for 3D semantic occupancy prediction have successfully addressed the challenge of describing real-world objects with varied shapes and classes. However, existing methods for 3D occupancy prediction heavily rely on surround-view camera images, making them susceptible to changes in lighting and weather conditions. This paper introduces OccFusion, a novel sensor fusion framework for predicting 3D occupancy. By integrating features from additional sensors, such as lidar and surround view radars, our framework enhances the accuracy and robustness of occupancy prediction, resulting in top-tier performance on the nuScenes benchmark. Furthermore, extensive experiments conducted on the nuScenes and semanticKITTI dataset, including challenging night and rainy scenarios, confirm the superior performance of our sensor fusion strategy across various perception ranges. The code for this framework will be made available at //github.com/DanielMing123/OccFusion.
Retrieval-augmented Generation (RAG) systems have been actively studied and deployed across various industries to query on domain-specific knowledge base. However, evaluating these systems presents unique challenges due to the scarcity of domain-specific queries and corresponding ground truths, as well as a lack of systematic approaches to diagnosing the cause of failure cases -- whether they stem from knowledge deficits or issues related to system robustness. To address these challenges, we introduce GRAMMAR (GRounded And Modular Methodology for Assessment of RAG), an evaluation framework comprising two key elements: 1) a data generation process that leverages relational databases and LLMs to efficiently produce scalable query-answer pairs. This method facilitates the separation of query logic from linguistic variations for enhanced debugging capabilities; and 2) an evaluation framework that differentiates knowledge gaps from robustness and enables the identification of defective modules. Our empirical results underscore the limitations of current reference-free evaluation approaches and the reliability of GRAMMAR to accurately identify model vulnerabilities.
As the significance of Software Engineering (SE) professionals continues to grow in the industry, the adoption of gamification techniques for training purposes has gained traction due to its potential to enhance class appeal through game-derived elements. This paper presents a tertiary study investigating the application of gamification in Software Engineering (SE) education. The study was conducted in response to recent systematic literature reviews and mappings on the topic. The findings reveal that the areas of SE most frequently gamified are Software Testing and Software Quality, with competition and cooperation being the most commonly utilized gamification elements. Additionally, the majority of studies focus on structural gamification, where game elements are employed to modify the learning environment without altering the content. The results demonstrate the potential of gamification to improve students' engagement and motivation throughout the SE learning process, while also impacting other aspects such as performance improvement, skill development, and fostering good SE practices. However, caution is advised as unplanned and incorrectly applied gamification measures may lead to significant declines in performance and motivation. (English Version of the paper in Portuguese available here: HTTP://doi.org/10.1145/3613372.3614193
We formally introduce a systematic (de/re)-composition approach, based on the algebraic formalism of "Multi-Dimensional Homomorphisms (MDHs)". Our approach is designed as general enough to be applicable to a wide range of data-parallel computations and for various kinds of target parallel architectures. To efficiently target the deep and complex memory and core hierarchies of contemporary architectures, we exploit our introduced (de/re)-composition approach for a correct-by-construction, parametrized cache blocking and parallelization strategy. We show that our approach is powerful enough to express, in the same formalism, the (de/re)-composition strategies of different classes of state-of-the-art approaches (scheduling-based, polyhedral, etc), and we demonstrate that the parameters of our strategies enable systematically generating code that can be fully automatically optimized (auto-tuned) for the particular target architecture and characteristics of the input and output data (e.g., their sizes and memory layouts). Particularly, our experiments confirm that via auto-tuning, we achieve higher performance than state-of-the-art approaches, including hand-optimized solutions provided by vendors (such as NVIDIA cuBLAS/cuDNN and Intel oneMKL/oneDNN), on real-world data sets and for a variety of data-parallel computations, including: linear algebra routines, stencil and quantum chemistry computations, data mining algorithms, and computations that recently gained high attention due to their relevance for deep learning.
ZKP systems have surged attention and held a fundamental role in contemporary cryptography. Zk-SNARK protocols dominate the ZKP usage, often implemented through arithmetic circuit programming paradigm. However, underconstrained or overconstrained circuits may lead to bugs. Underconstrained circuits refer to circuits that lack the necessary constraints, resulting in unexpected solutions in the circuit and causing the verifier to accept a bogus witness. Overconstrained circuits refer to circuits that are constrained excessively, resulting in the circuit lacking necessary solutions and causing the verifier to accept no witness, rendering the circuit meaningless. This paper introduces a novel approach for pinpointing two distinct types of bugs in ZKP circuits. The method involves encoding the arithmetic circuit constraints to polynomial equation systems and solving polynomial equation systems over a finite field by algebraic computation. The classification of verification results is refined, greatly enhancing the expressive power of the system. We proposed a tool, AC4, to represent the implementation of this method. Experiments demonstrate that AC4 represents a substantial 29% increase in the checked ratio compared to prior work. Within a solvable range, the checking time of AC4 has also exhibited noticeable improvement, demonstrating a magnitude increase compared to previous efforts.
The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.
An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing NAS algorithms often train and evaluate intermediate neural architectures on a small proxy dataset with limited training epochs. But it is difficult to expect an accurate performance estimation of an architecture in such a coarse evaluation way. This paper advocates a new neural architecture evaluation scheme, which aims to determine which architecture would perform better instead of accurately predict the absolute architecture performance. Therefore, we propose a \textbf{relativistic} architecture performance predictor in NAS (ReNAS). We encode neural architectures into feature tensors, and further refining the representations with the predictor. The proposed relativistic performance predictor can be deployed in discrete searching methods to search for the desired architectures without additional evaluation. Experimental results on NAS-Bench-101 dataset suggests that, sampling 424 ($0.1\%$ of the entire search space) neural architectures and their corresponding validation performance is already enough for learning an accurate architecture performance predictor. The accuracies of our searched neural architectures on NAS-Bench-101 and NAS-Bench-201 datasets are higher than that of the state-of-the-art methods and show the priority of the proposed method.
Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.
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