OpenRaiser/Intern-Atlas huggingface dataset:
A Real-World Benchmark for Evaluating Fine-Grained Issue Solving Capabilities of Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user experience. The process of resolving real-world issues encompasses tasks such as question-answering (QA), fault localization, and code editing. Existing benchmarks such as HumanEval fall short in their ability to assess LLMs' proficiency in solving issues within a codebase. Although benchmarks like SWE-Bench are designed to evaluate the LLMs' capability to handle real-world GitHub issues, the end-to-end evaluation method cannot provide granular insights on the performance of subtasks involved in issue solving. To address existing deficiencies in benchmarking LLMs for practical software engineering tasks, we introduce FAUN-Eval, a benchmark specifically designed to evaluate the Fine-grAined issUe solviNg capabilities of LLMs. FAUN-Eval systematically assesses LLMs across three distinct tasks: QA, fault localization, and code editing. This benchmark is constructed using a dataset curated from 30 well-known GitHub repositories. For each entry, issue and pull request (PR) pairs are meticulously compiled and validated using cross-referencing and keyword verification methods. FAUN-Eval includes 300 entries and employs both LLM and manual checks to ensure data quality. We evaluate ten LLMs with FAUN-Eval, including four closed-source and six open-source models. Our experimental results reveal several key findings. We find that the top-performing LLMs differ across the different tasks. Additionally, features in issues may lead LLMs to generate incorrect information. Moreover, models may vary in their proficiency with texts of different lengths.
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
Venue: arXiv cs.LG 2024
Year: 2024

Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a physics-informed machine learning-based framework to enhance grid's resiliency. Specifically, when encountering disruptive events, this paper designs remedial control actions to prevent blackouts. The proposed Physics-Guided Reinforcement Learning (PG-RL) framework determines effective real-time remedial line-switching actions, considering their impact on power balance, system security, and grid reliability. To identify an effective blackout mitigation policy, PG-RL leverages power-flow sensitivity factors to guide the RL exploration during agent training. Comprehensive evaluations using the Grid2Op platform demonstrate that incorporating physical signals into RL significantly improves resource utilization within electric grids and achieves better blackout mitigation policies - both of which are critical in addressing climate change.
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
Venue: arXiv cs.SE 2024
Year: 2024

Mobile apps are essential in daily life, yet they often employ dark patterns, such as visual tricks to highlight certain options or linguistic tactics to nag users into making purchases, to manipulate user behavior. Current research mainly uses manual methods to detect dark patterns, a process that is time-consuming and struggles to keep pace with continually updating and emerging apps. While some studies targeted at automated detection, they are constrained to static patterns and still necessitate manual app exploration. To bridge these gaps, we present AppRay, an innovative system that seamlessly blends task-oriented app exploration with automated dark pattern detection, reducing manual efforts. Our approach consists of two steps: First, we harness the commonsense knowledge of large language models for targeted app exploration, supplemented by traditional random exploration to capture a broader range of UI states. Second, we developed a static and dynamic dark pattern detector powered by a contrastive learning-based multi-label classifier and a rule-based refiner to perform detection. We contributed two datasets, AppRay-Dark and AppRay-Light, with 2,185 unique deceptive patterns (including 149 dynamic instances) across 18 types from 876 UIs and 871 benign UIs. These datasets cover both static and dynamic dark patterns while preserving UI relationships. Experimental results confirm that AppRay can efficiently explore the app and identify a wide range of dark patterns with great performance.
Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis
Venue: arXiv cs.CV 2024
Year: 2024

Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here we present a novel knowledge concept-based MIL framework, named ConcepPath to fill this gap. Specifically, ConcepPath utilizes GPT-4 to induce reliable diseasespecific human expert concepts from medical literature, and incorporate them with a group of purely learnable concepts to extract complementary knowledge from training data. In ConcepPath, WSIs are aligned to these linguistic knowledge concepts by utilizing pathology vision-language model as the basic building component. In the application of lung cancer subtyping, breast cancer HER2 scoring, and gastric cancer immunotherapy-sensitive subtyping task, ConcepPath significantly outperformed previous SOTA methods which lack the guidance of human expert knowledge.
DistinctAD: Distinctive Audio Description Generation in Contexts
Venue: arXiv cs.CV 2024
Year: 2024

Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment. Automatic generation of ADs remains challenging due to: i) the domain gap between movie-AD data and existing data used to train vision-language models, and ii) the issue of contextual redundancy arising from highly similar neighboring visual clips in a long movie. In this work, we propose DistinctAD, a novel two-stage framework for generating ADs that emphasize distinctiveness to produce better narratives. To address the domain gap, we introduce a CLIP-AD adaptation strategy that does not require additional AD corpora, enabling more effective alignment between movie and AD modalities at both global and fine-grained levels. In Stage-II, DistinctAD incorporates two key innovations: (i) a Contextual Expectation-Maximization Attention (EMA) module that reduces redundancy by extracting common bases from consecutive video clips, and (ii) an explicit distinctive word prediction loss that filters out repeated words in the context, ensuring the prediction of unique terms specific to the current AD. Comprehensive evaluations on MAD-Eval, CMD-AD, and TV-AD benchmarks demonstrate the superiority of DistinctAD, with the model consistently outperforming baselines, particularly in Recall@k/N, highlighting its effectiveness in producing high-quality, distinctive ADs.
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation
Venue: arXiv cs.LG 2024
Year: 2024

Recent learning-to-imitation methods have shown promising results in planning via imitating within the observation-action space. However, their ability in open environments remains constrained, particularly in long-horizon tasks. In contrast, traditional symbolic planning excels in long-horizon tasks through logical reasoning over human-defined symbolic spaces but struggles to handle observations beyond symbolic states, such as high-dimensional visual inputs encountered in real-world scenarios. In this work, we draw inspiration from abductive learning and introduce a novel framework \textbf{AB}ductive \textbf{I}mitation \textbf{L}earning (ABIL) that integrates the benefits of data-driven learning and symbolic-based reasoning, enabling long-horizon planning. Specifically, we employ abductive reasoning to understand the demonstrations in symbolic space and design the principles of sequential consistency to resolve the conflicts between perception and reasoning. ABIL generates predicate candidates to facilitate the perception from raw observations to symbolic space without laborious predicate annotations, providing a groundwork for symbolic planning. With the symbolic understanding, we further develop a policy ensemble whose base policies are built with different logical objectives and managed through symbolic reasoning. Experiments show that our proposal successfully understands the observations with the task-relevant symbolics to assist the imitation learning. Importantly, ABIL demonstrates significantly improved data efficiency and generalization across various long-horizon tasks, highlighting it as a promising solution for long-horizon planning. Project website: \url{https://www.lamda.nju.edu.cn/shaojj/KDD25_ABIL/}.
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification
Venue: arXiv cs.LG 2024
Year: 2024

Cardiovascular disease refers to any critical condition that impacts the heart. Because heart diseases can be life-threatening. Researchers are focusing on designing smart systems to accurately diagnose them based on electronic health data, with the aid of machine learning algorithms. Heart disease classification using machine learning (ML) algorithms such as Support Vector Machine(SVM), Naïve Bayes(NB), Decision Trees (DTs) and Random Forests (RFs) are often hindered by overfitting. These ML algorithms need extensive hyperparameter tuning. Random Search offers a faster, and, more efficient exploration of hyperparameter space, but, it may overlook optimal regions. Grid Search, though exhaustive, but, it is computationally expensive and inefficient, particularly with high-dimensional data. To address these limitations, Randomized-Grid Search, a novel hybrid optimization method is proposed that combines the global exploration strengths of Random Search with the focused, and, exhaustive search of Grid Search in the most promising regions. This hybrid approach efficiently balances exploration and exploitation. The proposed model optimizes the hyperparameter for Decision Tree model. The proposed model is applied to UCI heart disease dataset for classification. It enhances model performance, provides improved accuracy, generalization, and computational efficiency. Experimental results demonstrate that Randomized-Grid Search outperforms traditional methods by significant margins. The proposed model provides a more effective solution for machine learning applications in healthcare diagnosis.
A gentle push funziona benissimo: making instructed models in Italian via contrastive activation steering
Venue: arXiv cs.CL 2024
Year: 2024

Adapting models to a language that was only partially present in the pre-training data requires fine-tuning, which is expensive in terms of both data and computational resources. As an alternative to fine-tuning, we explore the potential of activation steering-based techniques to enhance model performance on Italian tasks. Through our experiments we show that Italian steering (i) can be successfully applied to different models, (ii) achieves performances comparable to, or even better than, fine-tuned models for Italian, and (iii) yields higher quality and consistency in Italian generations. We also discuss the utility of steering and fine-tuning in the contemporary LLM landscape where models are anyway getting high Italian performances even if not explicitly trained in this language.
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware
Venue: arXiv cs.ET 2024
Year: 2024

Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity. Herein, a novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated to meet this demand. This synapse features two distinct state variables: a neuron coupling weight and an "eligibility trace" that dictates synaptic weight updates. The coupling weight is encoded in the ReRAM conductance, while the "eligibility trace" is encoded in the local temperature of the ReRAM and is modulated by applying voltage pulses to a physically co-located resistive heating element. The utility of the proposed synapse has been investigated using two representative tasks: first, temporal signal classification using Recurrent Spiking Neural Networks (RSNNs) employing the e-prop algorithm, and second, Reinforcement Learning (RL) for path planning tasks in feedforward networks using a modified version of the same learning rule. System-level simulations, accounting for various device and system-level non-idealities, confirm that these synapses offer a robust solution for the fast, compact, and energy-efficient implementation of advanced learning rules in neuromorphic hardware.
Using Malware Detection Techniques for HPC Application Classification
Venue: arXiv cs.CR 2024
Year: 2024

HPC systems face security and compliance challenges, particularly in preventing waste and misuse of computational resources by unauthorized or malicious software that deviates from allocation purpose. Existing methods to classify applications based on job names or resource usage are often unreliable or fail to capture applications that have different behavior due to different inputs or system noise. This research proposes an approach that uses similarity-preserving fuzzy hashes to classify HPC application executables. By comparing the similarity of SSDeep fuzzy hashes, a Random Forest Classifier can accurately label applications executing on HPC systems including unknown samples. We evaluate the Fuzzy Hash Classifier on a dataset of 92 application classes and 5333 distinct application samples. The proposed method achieved a macro f1-score of 90% (micro f1-score: 89%, weighted f1-score: 90%). Our approach addresses the critical need for more effective application classification in HPC environments, minimizing resource waste, and enhancing security and compliance.
TransferFuzz: Fuzzing with Historical Trace for Verifying Propagated Vulnerability Code
Venue: arXiv cs.SE 2024
Year: 2024

Code reuse in software development frequently facilitates the spread of vulnerabilities, making the scope of affected software in CVE reports imprecise. Traditional methods primarily focus on identifying reused vulnerability code within target software, yet they cannot verify if these vulnerabilities can be triggered in new software contexts. This limitation often results in false positives. In this paper, we introduce TransferFuzz, a novel vulnerability verification framework, to verify whether vulnerabilities propagated through code reuse can be triggered in new software. Innovatively, we collected runtime information during the execution or fuzzing of the basic binary (the vulnerable binary detailed in CVE reports). This process allowed us to extract historical traces, which proved instrumental in guiding the fuzzing process for the target binary (the new binary that reused the vulnerable function). TransferFuzz introduces a unique Key Bytes Guided Mutation strategy and a Nested Simulated Annealing algorithm, which transfers these historical traces to implement trace-guided fuzzing on the target binary, facilitating the accurate and efficient verification of the propagated vulnerability. Our evaluation, conducted on widely recognized datasets, shows that TransferFuzz can quickly validate vulnerabilities previously unverifiable with existing techniques. Its verification speed is 2.5 to 26.2 times faster than existing methods. Moreover, TransferFuzz has proven its effectiveness by expanding the impacted software scope for 15 vulnerabilities listed in CVE reports, increasing the number of affected binaries from 15 to 53. The datasets and source code used in this article are available at https://github.com/Siyuan-Li201/TransferFuzz.
SoK: Privacy Personalised -- Mapping Personal Attributes \& Preferences of Privacy Mechanisms for Shoulder Surfing
Venue: arXiv cs.HC 2024
Year: 2024

Shoulder surfing is a byproduct of smartphone use that enables bystanders to access personal information (such as text and photos) by making screen observations without consent. To mitigate this, several protection mechanisms have been proposed to protect user privacy. However, the mechanisms that users prefer remain unexplored. This paper explores correlations between personal attributes and properties of shoulder surfing protection mechanisms. For this, we first conducted a structured literature review and identified ten protection mechanism categories against content-based shoulder surfing. We then surveyed N=192 users and explored correlations between personal attributes and properties of shoulder surfing protection mechanisms. Our results show that users agreed that the presented mechanisms assisted in protecting their privacy, but they preferred non-digital alternatives. Among the mechanisms, participants mainly preferred an icon overlay mechanism followed by a tangible mechanism. We also found that users who prioritized out-of-device privacy and a high tendency to interact with technology favoured the personalisation of protection mechanisms. On the contrary, age and smartphone OS did not impact users' preference for perceived usefulness and personalisation of mechanisms. Based on the results, we present key takeaways to support the design of future protection mechanisms.
Convolutional Neural Networks Do Work with Pre-Defined Filters
Venue: arXiv cs.CV 2024
Year: 2024

We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all nxn convolution kernels with n>1 are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In the channel-wise convolution part, the 1xnxn kernels are drawn from a fixed pool of only a few (16) different pre-defined kernels. In the 1x1 convolution part linear combinations of the pre-defined filter outputs are learned. Despite this harsh restriction, complex and discriminative features are learned. These findings provide a novel perspective on the way how information is processed within deep CNNs. We discuss various properties of PFCNNs and prove their effectiveness using the popular datasets Caltech101, CIFAR10, CUB-200-2011, FGVC-Aircraft, Flowers102, and Stanford Cars. Our implementation of PFCNNs is provided on Github https://github.com/Criscraft/PredefinedFilterNetworks
When does a bridge become an aeroplane?
Venue: arXiv cs.LG 2024
Year: 2024

Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. It has been proposed that heterogeneous transfer may be accomplished via intermediate structures that bridge the gap in information between the structures of interest. A key aspect of the technique is the idea that by varying parameters such as material properties and geometry, one structure can be continuously morphed into another. The current work demonstrates the development of these interpolating structures, via case studies involving the parameterisation of (and transfer between) a simple, simulated 'bridge' and 'aeroplane'. The facetious question 'When is a bridge not an aeroplane?' has been previously asked in the context of predicting positive transfer based on structural similarity. While the obvious answer to this question is 'Always,' the current work demonstrates that in some cases positive transfer can be achieved between highly-disparate systems.
Preserving Information: How does Topological Data Analysis improve Neural Network performance?
Venue: arXiv cs.NE 2024
Year: 2024

Artificial Neural Networks (ANNs) require significant amounts of data and computational resources to achieve high effectiveness in performing the tasks for which they are trained. To reduce resource demands, various techniques, such as Neuron Pruning, are applied. Due to the complex structure of ANNs, interpreting the behavior of hidden layers and the features they recognize in the data is challenging. A lack of comprehensive understanding of which information is utilized during inference can lead to inefficient use of available data, thereby lowering the overall performance of the models. In this paper, we introduce a method for integrating Topological Data Analysis (TDA) with Convolutional Neural Networks (CNN) in the context of image recognition. This method significantly enhances the performance of neural networks by leveraging a broader range of information present in the data, enabling the model to make more informed and accurate predictions. Our approach, further referred to as Vector Stitching, involves combining raw image data with additional topological information derived through TDA methods. This approach enables the neural network to train on an enriched dataset, incorporating topological features that might otherwise remain unexploited or not captured by the network's inherent mechanisms. The results of our experiments highlight the potential of incorporating results of additional data analysis into the network's inference process, resulting in enhanced performance in pattern recognition tasks in digital images, particularly when using limited datasets. This work contributes to the development of methods for integrating TDA with deep learning and explores how concepts from Information Theory can explain the performance of such hybrid methods in practical implementation environments.
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning
Venue: arXiv cs.LG 2024
Year: 2024

Selection bias poses a critical challenge for fairness in machine learning, as models trained on data that is less representative of the population might exhibit undesirable behavior for underrepresented profiles. Semi-supervised learning strategies like self-training can mitigate selection bias by incorporating unlabeled data into model training to gain further insight into the distribution of the population. However, conventional self-training seeks to include high-confidence data samples, which may reinforce existing model bias and compromise effectiveness. We propose Metric-DST, a diversity-guided self-training strategy that leverages metric learning and its implicit embedding space to counter confidence-based bias through the inclusion of more diverse samples. Metric-DST learned more robust models in the presence of selection bias for generated and real-world datasets with induced bias, as well as a molecular biology prediction task with intrinsic bias. The Metric-DST learning strategy offers a flexible and widely applicable solution to mitigate selection bias and enhance fairness of machine learning models.