OpenRaiser/Intern-Atlas huggingface dataset:
Acknowledging Good Java Code with Code Perfumes
Venue: arXiv cs.SE 2024
Year: 2024

Java remains one of the most popular programming languages in education. Although Java programming education is well supported by study materials, learners also need more immediate support on the problems they face in their own code. When this support cannot be offered by educators personally, learners can resort to automated program analysis tools such as linters, which provide feedback on potential bugs or code issues. This is constructive feedback, but it may nevertheless feel like criticism. This paper introduces code perfumes for Java, a simple program analysis technique similar to linting, but commending the correct application of good programming practices. We present a catalogue of 20 Java code perfumes related to common Java language constructs for beginner to immediate learners. Our evaluation shows that these code perfumes occur frequently in learners' code, and programs with more code perfume instances tend to have better functionality and readability. Moreover, students who incorporate more code perfumes tend to achieve higher grades. Thus, code perfumes serve as a valuable tool to acknowledge learners' successes, and as a means to inform instructors about their learners' progress.
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations
Venue: arXiv cs.LG 2024
Year: 2024

Tabular data is ubiquitous in real-world applications and abundant on the web, yet its annotation has traditionally required human labor, posing a significant scalability bottleneck for tabular machine learning. Our methodology can successfully annotate a large amount of tabular data and can be flexibly steered to generate various types of annotations based on specific research objectives, as we demonstrate with SQL annotation and input-target column annotation as examples. As a result, we release AnnotatedTables, a collection of 32,119 databases with LLM-generated annotations. The dataset includes 405,616 valid SQL programs, making it the largest SQL dataset with associated tabular data that supports query execution. To further demonstrate the value of our methodology and dataset, we perform two follow-up research studies. 1) We investigate whether LLMs can translate SQL programs to Rel programs, a database language previously unknown to LLMs, while obtaining the same execution results. Using our Incremental Prompt Engineering methods based on execution feedback, we show that LLMs can produce adequate translations with few-shot learning. 2) We evaluate the performance of TabPFN, a recent neural tabular classifier trained on Bayesian priors, on 2,720 tables with input-target columns identified and annotated by LLMs. On average, TabPFN performs on par with the baseline AutoML method, though the relative performance can vary significantly from one data table to another, making both models viable for practical applications depending on the situation. Our findings underscore the potential of LLMs in automating the annotation of large volumes of diverse tabular data.
Compact Model Parameter Extraction via Derivative-Free Optimization
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we address the problem of compact model parameter extraction to simultaneously extract tens of parameters via derivative-free optimization. Traditionally, parameter extraction is performed manually by dividing the complete set of parameters into smaller subsets, each targeting different operational regions of the device, a process that can take several days or weeks. Our approach streamlines this process by employing derivative-free optimization to identify a good parameter set that best fits the compact model without performing an exhaustive number of simulations. We further enhance the optimization process to address three critical issues in device modeling by carefully choosing a loss function that focuses on relative errors rather than absolute errors to ensure consistent performance across different orders of magnitude, prioritizes accuracy in key operational regions above a specific threshold, and reduces sensitivity to outliers. Furthermore, we utilize the concept of train-test split to assess the model fit and avoid overfitting. We demonstrate the effectiveness of our approach by successfully modeling a diamond Schottky diode with the SPICE diode model and a GaN-on-SiC HEMT with the ASM-HEMT model. For the latter, which involves extracting 35 parameters for the ASM-HEMT DC model, we identified the best set of parameters in under 6,000 trials. Additional examples using both devices are provided to demonstrate robustness to outliers, showing that an excellent fit is achieved even with over 25% of the data purposely corrupted. These examples demonstrate the practicality of our approach, highlighting the benefits of derivative-free optimization in device modeling.
Open-Source Tool Based Framework for Automated Performance Evaluation of an AD Function
Venue: arXiv cs.RO 2024
Year: 2024

As automation in the field of automated driving (AD) progresses, ensuring the safety and functionality of AD functions (ADFs) becomes crucial. Virtual scenario-based testing has emerged as a prevalent method for evaluating these systems, allowing for a wider range of testing environments and reproducibility of results. This approach involves AD-equipped test vehicles operating within predefined scenarios to achieve specific driving objectives. To comprehensively assess the impact of road network properties on the performance of an ADF, varying parameters such as intersection angle, curvature and lane width is essential. However, covering all potential scenarios is impractical, necessitating the identification of feasible parameter ranges and automated generation of corresponding road networks for simulation. Automating the workflow of road network generation, parameter variation, simulation, and evaluation leads to a comprehensive understanding of an ADF's behavior in diverse road network conditions. This paper aims to investigate the influence of road network parameters on the performance of a prototypical ADF through virtual scenario-based testing, ultimately advocating the importance of road topology in assuring safety and reliability of ADFs.
Machine Learning with Real-time and Small Footprint Anomaly Detection System for In-Vehicle Gateway
Venue: arXiv cs.CR 2024
Year: 2024

Anomaly Detection System (ADS) is an essential part of a modern gateway Electronic Control Unit (ECU) to detect abnormal behaviors and attacks in vehicles. Among the existing attacks, ``one-time`` attack is the most challenging to be detected, together with the strict gateway ECU constraints of both microsecond or even nanosecond level real-time budget and limited footprint of code. To address the challenges, we propose to use the self-information theory to generate values for training and testing models, aiming to achieve real-time detection performance for the ``one-time`` attack that has not been well studied in the past. Second, the generation of self-information is based on logarithm calculation, which leads to the smallest footprint to reduce the cost in Gateway. Finally, our proposed method uses an unsupervised model without the need of training data for anomalies or attacks. We have compared different machine learning methods ranging from typical machine learning models to deep learning models, e.g., Hidden Markov Model (HMM), Support Vector Data Description (SVDD), and Long Short Term Memory (LSTM). Experimental results show that our proposed method achieves 8.7 times lower False Positive Rate (FPR), 1.77 times faster testing time, and 4.88 times smaller footprint.
Counting Polyominoes in a Rectangle b x h
Venue: arXiv cs.DM 2024
Year: 2024

In this paper, we provide methods to automatically obtain automata that generate polyominoes inscribed in a rectangle of fixed width and increasing height. We use them to obtain the generating function of those sequences for small widths.
Local Limit Theorems for $q$-Multinomial and Multiple Heine Distributions
Venue: arXiv cs.DM 2024
Year: 2024

In this work we establish local limit theorems for q-multinomial and multiple Heine distributions. Specifically, the pointwise convergence of the q-multinomial distribution of the first kind, as well as for its discrete limit, the multiple Heine distribution, to a multivariate Stieltjes-Wigert type distribution, are provided.
Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge. To alleviate the annotation burden, existing methods generate pseudo masks for model training using point labels. However, the generated masks are inevitably different from the ground truth, and these dissimilarities are not handled reasonably during the network training, resulting in the subpar performance of the segmentation model. To tackle this issue, we propose a framework named DoNuSeg, enabling \textbf{D}ynamic pseudo label \textbf{O}ptimization in point-supervised \textbf{Nu}clei \textbf{Seg}mentation. Specifically, DoNuSeg takes advantage of class activation maps (CAMs) to adaptively capture regions with semantics similar to annotated points. To leverage semantic diversity in the hierarchical feature levels, we design a dynamic selection module to choose the optimal one among CAMs from different encoder blocks as pseudo masks. Meanwhile, a CAM-guided contrastive module is proposed to further enhance the accuracy of pseudo masks. In addition to exploiting the semantic information provided by CAMs, we consider location priors inherent to point labels, developing a task-decoupled structure for effectively differentiating nuclei. Extensive experiments demonstrate that DoNuSeg outperforms state-of-the-art point-supervised methods. The code is available at https://github.com/shinning0821/MICCAI24-DoNuSeg.
EmoLLM: Multimodal Emotional Understanding Meets Large Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks, but their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. Thus, it impedes their ability to effectively understand and react to the intricate emotions expressed by humans through multimodal media. To bridge this gap, we introduce EmoBench, the first comprehensive benchmark designed specifically to evaluate the emotional capabilities of MLLMs across five popular emotional tasks, using a diverse dataset of 287k images and videos paired with corresponding textual instructions. Meanwhile, we propose EmoLLM, a novel model for multimodal emotional understanding, incorporating with two core techniques. 1) Multi-perspective Visual Projection, it captures diverse emotional cues from visual data from multiple perspectives. 2) EmoPrompt, it guides MLLMs to reason about emotions in the correct direction. Experimental results demonstrate that EmoLLM significantly elevates multimodal emotional understanding performance, with an average improvement of 12.1% across multiple foundation models on EmoBench. Our work contributes to the advancement of MLLMs by facilitating a deeper and more nuanced comprehension of intricate human emotions, paving the way for the development of artificial emotional intelligence capabilities with wide-ranging applications in areas such as human-computer interaction, mental health support, and empathetic AI systems. Code, data, and model will be released.
Bijective BWT based compression schemes
Venue: arXiv cs.DS 2024
Year: 2024

We investigate properties of the bijective Burrows-Wheeler transform (BBWT). We show that for any string $w$, a bidirectional macro scheme of size $O(r_B)$ can be induced from the BBWT of $w$, where $r_B$ is the number of maximal character runs in the BBWT. We also show that $r_B = O(z\log^2 n)$, where $n$ is the length of $w$ and $z$ is the number of Lempel-Ziv 77 factors of $w$. Then, we show a separation between BBWT and BWT by a family of strings with $r_B = Ω(\log n)$ but having only $r=2$ maximal character runs in the standard Burrows--Wheeler transform (BWT). However, we observe that the smallest $r_B$ among all cyclic rotations of $w$ is always at most $r$. While an $o(n^2)$ algorithm for computing an optimal rotation giving the smallest $r_B$ is still open, we show how to compute the Lyndon factorizations -- a component for computing BBWT -- of all cyclic rotations in $O(n)$ time. Furthermore, we conjecture that we can transform two strings having the same Parikh vector to each other by BBWT and rotation operations, and prove this conjecture for the case of binary alphabets and permutations.
Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing
Venue: arXiv cs.NE 2024
Year: 2024

Advances in neural computation have predominantly relied on the gradient backpropagation algorithm (BP). However, the recent shift towards non-stationary data modeling has highlighted the limitations of this heuristic, exposing that its adaptation capabilities are far from those seen in biological brains. Unlike BP, where weight updates are computed through a reverse error propagation path, Hebbian learning dynamics provide synaptic updates using only information within the layer itself. This has spurred interest in biologically plausible learning algorithms, hypothesized to overcome BP's shortcomings. In this context, Hinton recently introduced the Forward-Forward Algorithm (FFA), which employs local learning rules for each layer and has empirically proven its efficacy in multiple data modeling tasks. In this work we argue that when employing a squared Euclidean norm as a goodness function driving the local learning, the resulting FFA is equivalent to a neo-Hebbian Learning Rule. To verify this result, we compare the training behavior of FFA in analog networks with its Hebbian adaptation in spiking neural networks. Our experiments demonstrate that both versions of FFA produce similar accuracy and latent distributions. The findings herein reported provide empirical evidence linking biological learning rules with currently used training algorithms, thus paving the way towards extrapolating the positive outcomes from FFA to Hebbian learning rules. Simultaneously, our results imply that analog networks trained under FFA could be directly applied to neuromorphic computing, leading to reduced energy usage and increased computational speed.
C-LLM: Learn to Check Chinese Spelling Errors Character by Character
Venue: arXiv cs.CL 2024
Year: 2024

Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. Despite Large Language Models (LLMs) exhibit robust capabilities and are widely applied in various tasks, their performance on CSC is often unsatisfactory. We find that LLMs fail to meet the Chinese character-level constraints of the CSC task, namely equal length and phonetic similarity, leading to a performance bottleneck. Further analysis reveal that this issue stems from the granularity of tokenization, as current mixed character-word tokenization struggles to satisfy these character-level constraints. To address this issue, we propose C-LLM, a Large Language Model-based Chinese Spell Checking method that learns to check errors Character by Character. Character-level tokenization enables the model to learn character-level alignment, effectively mitigating issues related to character-level constraints. Furthermore, CSC is simplified to replication-dominated and substitution-supplemented tasks. Experiments on two CSC benchmarks demonstrate that C-LLM achieves an average improvement of 10% over existing methods. Specifically, it shows a 2.1% improvement in general scenarios and a significant 12% improvement in vertical domain scenarios, establishing state-of-the-art performance. The source code can be accessed at https://github.com/ktlKTL/C-LLM.
Homomorphisms and Embeddings of STRIPS Planning Models
Venue: arXiv cs.AI 2024
Year: 2024

Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance $P$ and a sub-instance of another instance $P_0$ . One application of such a mapping is to efficiently produce a compiled form containing all solutions to P from a compiled form containing all solutions to $P_0$. We also introduce the notion of embedding from an instance $P$ to another instance $P_0$, which allows us to deduce that $P_0$ has no solution-plan if $P$ is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI-complete, and can thus be solved, in theory, in quasi-polynomial time. While we prove the remaining problems to be NP-complete, we propose an algorithm to build an isomorphism, when possible. We report extensive experimental trials on benchmark problems which demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver.
Noisy Neighbors: Efficient membership inference attacks against LLMs
Venue: arXiv cs.CR 2024
Year: 2024

The potential of transformer-based LLMs risks being hindered by privacy concerns due to their reliance on extensive datasets, possibly including sensitive information. Regulatory measures like GDPR and CCPA call for using robust auditing tools to address potential privacy issues, with Membership Inference Attacks (MIA) being the primary method for assessing LLMs' privacy risks. Differently from traditional MIA approaches, often requiring computationally intensive training of additional models, this paper introduces an efficient methodology that generates \textit{noisy neighbors} for a target sample by adding stochastic noise in the embedding space, requiring operating the target model in inference mode only. Our findings demonstrate that this approach closely matches the effectiveness of employing shadow models, showing its usability in practical privacy auditing scenarios.
Personalized federated learning based on feature fusion
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning enables distributed clients to collaborate on training while storing their data locally to protect client privacy. However, due to the heterogeneity of data, models, and devices, the final global model may need to perform better for tasks on each client. Communication bottlenecks, data heterogeneity, and model heterogeneity have been common challenges in federated learning. In this work, we considered a label distribution skew problem, a type of data heterogeneity easily overlooked. In the context of classification, we propose a personalized federated learning approach called pFedPM. In our process, we replace traditional gradient uploading with feature uploading, which helps reduce communication costs and allows for heterogeneous client models. These feature representations play a role in preserving privacy to some extent. We use a hyperparameter $a$ to mix local and global features, which enables us to control the degree of personalization. We also introduced a relation network as an additional decision layer, which provides a non-linear learnable classifier to predict labels. Experimental results show that, with an appropriate setting of $a$, our scheme outperforms several recent FL methods on MNIST, FEMNIST, and CRIFAR10 datasets and achieves fewer communications.
Measuring the Recyclability of Electronic Components to Assist Automatic Disassembly and Sorting Waste Printed Circuit Boards
Venue: arXiv cs.CV 2024
Year: 2024

The waste of electrical and electronic equipment has been increased due to the fast evolution of technology products and competition of many IT sectors. Every year millions of tons of electronic waste are thrown into the environment which causes high consequences for human health. Therefore, it is crucial to control this waste flow using technology, especially using Artificial Intelligence but also reclamation of critical raw materials for new production processes. In this paper, we focused on the measurement of recyclability of waste electronic components (WECs) from waste printed circuit boards (WPCBs) using mathematical innovation model. This innovative approach evaluates both the recyclability and recycling difficulties of WECs, integrating an AI model for improved disassembly and sorting. Assessing the recyclability of individual electronic components present on WPCBs provides insight into the recovery potential of valuable materials and indicates the level of complexity involved in recycling in terms of economic worth and production utility. This novel measurement approach helps AI models in accurately determining the number of classes to be identified and sorted during the automated disassembly of discarded PCBs. It also facilitates the model in iterative training and validation of individual electronic components.
Process Algebra Based Tool Coordination Architectures in Raku and Go
Venue: arXiv cs.SE 2024
Year: 2024

This paper presents ongoing research in our project software engineering with process algebra. In this project we have developed among others a reimplementation of the simulator from the PSF Toolkit, a set of tools for the Process Specification formalism (PSF). This new simulator uses the ToolBus, a tool coordination architecture based on process algebra. We now developed new tool coordination architectures based on this ToolBus. We implement the primitives of the ToolBus in the programming languages Raku and Go. Both these languages have support for concurrency and communication between concurrent entities in the form of channels. We apply these tool coorination architectures on a small example. And we give implementations for the simulator in the PSF Toolkit based on the tool coordination architectures in Raku and Go.
MLAAN: Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network
Venue: arXiv cs.CV 2024
Year: 2024

Deep neural networks (DNNs) typically employ an end-to-end (E2E) training paradigm which presents several challenges, including high GPU memory consumption, inefficiency, and difficulties in model parallelization during training. Recent research has sought to address these issues, with one promising approach being local learning. This method involves partitioning the backbone network into gradient-isolated modules and manually designing auxiliary networks to train these local modules. Existing methods often neglect the interaction of information between local modules, leading to myopic issues and a performance gap compared to E2E training. To address these limitations, we propose the Multilaminar Leap Augmented Auxiliary Network (MLAAN). Specifically, MLAAN comprises Multilaminar Local Modules (MLM) and Leap Augmented Modules (LAM). MLM captures both local and global features through independent and cascaded auxiliary networks, alleviating performance issues caused by insufficient global features. However, overly simplistic auxiliary networks can impede MLM's ability to capture global information. To address this, we further design LAM, an enhanced auxiliary network that uses the Exponential Moving Average (EMA) method to facilitate information exchange between local modules, thereby mitigating the shortsightedness resulting from inadequate interaction. The synergy between MLM and LAM has demonstrated excellent performance. Our experiments on the CIFAR-10, STL-10, SVHN, and ImageNet datasets show that MLAAN can be seamlessly integrated into existing local learning frameworks, significantly enhancing their performance and even surpassing end-to-end (E2E) training methods, while also reducing GPU memory consumption.