OpenRaiser/Intern-Atlas huggingface dataset:
Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts
Venue: arXiv cs.SE 2024
Year: 2024

Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains, including chatbots, healthcare, education, content generation, and automated support systems. However, developers encounter numerous challenges when implementing, fine-tuning, and integrating these models into real-world applications. This study investigates LLM developers' challenges by analyzing community interactions on Stack Overflow and OpenAI Developer Forum, employing BERTopic modeling to identify and categorize developer discussions. Our analysis yields nine challenges on Stack Overflow (e.g., LLM Ecosystem and Challenges, API Usage, LLM Training with Frameworks) and 17 on the OpenAI Developer Forum (e.g., API Usage and Error Handling, Fine-Tuning and Dataset Management). Results indicate that developers frequently turn to Stack Overflow for implementation guidance, while OpenAI's forum focuses on troubleshooting. Notably, API and functionality issues dominate discussions on the OpenAI forum, with many posts requiring multiple responses, reflecting the complexity of LLM-related problems. We find that LLM-related queries often exhibit great difficulty, with a substantial percentage of unresolved posts (e.g., 79.03\% on Stack Overflow) and prolonged response times, particularly for complex topics like 'Llama Indexing and GPU Utilization' and 'Agents and Tool Interactions'. In contrast, established fields like Mobile Development and Security enjoy quicker resolutions and stronger community engagement. These findings highlight the need for improved community support and targeted resources to assist LLM developers in overcoming the evolving challenges of this rapidly growing field. This study provides insights into areas of difficulty, paving the way for future research and tool development to better support the LLM developer community.
Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis
Venue: arXiv cs.CL 2024
Year: 2024

This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor-intensive, time-consuming, and susceptible to human error, highlighting the need for automated pipelines to streamline the process. Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction. We automate and optimize the meta-analysis process by integrating Retrieval Augmented Generation (RAG). Tailored through prompt engineering and a new loss metric, Inverse Cosine Distance (ICD), designed for fine-tuning on large contextual datasets, LLMs efficiently generate structured meta-analysis content. Human evaluation then assesses relevance and provides information on model performance in key metrics. This research demonstrates that fine-tuned models outperform non-fine-tuned models, with fine-tuned LLMs generating 87.6% relevant meta-analysis abstracts. The relevance of the context, based on human evaluation, shows a reduction in irrelevancy from 4.56% to 1.9%. These experiments were conducted in a low-resource environment, highlighting the study's contribution to enhancing the efficiency and reliability of meta-analysis automation.
Deep BI-RADS Network for Improved Cancer Detection from Mammograms
Venue: arXiv cs.CV 2024
Year: 2024

While state-of-the-art models for breast cancer detection leverage multi-view mammograms for enhanced diagnostic accuracy, they often focus solely on visual mammography data. However, radiologists document valuable lesion descriptors that contain additional information that can enhance mammography-based breast cancer screening. A key question is whether deep learning models can benefit from these expert-derived features. To address this question, we introduce a novel multi-modal approach that combines textual BI-RADS lesion descriptors with visual mammogram content. Our method employs iterative attention layers to effectively fuse these different modalities, significantly improving classification performance over image-only models. Experiments on the CBIS-DDSM dataset demonstrate substantial improvements across all metrics, demonstrating the contribution of handcrafted features to end-to-end.
Targeting Negative Flips in Active Learning using Validation Sets
Venue: arXiv cs.LG 2024
Year: 2024

The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by the accuracy of the model and the latter is captured in negative flips between rounds. Negative flips are samples that are correctly predicted when trained with the previous/smaller dataset and incorrectly predicted after additional samples are labeled. In this paper, we discuss improving the performance of active learning algorithms both in terms of prediction accuracy and negative flips. The first observation we make in this paper is that negative flips and overall error rates are decoupled and reducing one does not necessarily imply that the other is reduced. Our observation is important as current active learning algorithms do not consider negative flips directly and implicitly assume the opposite. The second observation is that performing targeted active learning on subsets of the unlabeled pool has a significant impact on the behavior of the active learning algorithm and influences both negative flips and prediction accuracy. We then develop ROSE - a plug-in algorithm that utilizes a small labeled validation set to restrict arbitrary active learning acquisition functions to negative flips within the unlabeled pool. We show that integrating a validation set results in a significant performance boost in terms of accuracy, negative flip rate reduction, or both.
Towards Accurate and Efficient Sub-8-Bit Integer Training
Venue: arXiv cs.LG 2024
Year: 2024

Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce quantization error, recent methods have developed new data formats and additional pre-processing operations on quantizers. However, it remains quite challenging to achieve high accuracy and efficiency simultaneously. In this paper, we explore sub-8-bit integer training from its essence of gradient descent optimization. Our integer training framework includes two components: ShiftQuant to realize accurate gradient estimation, and L1 normalization to smoothen the loss landscape. ShiftQuant attains performance that approaches the theoretical upper bound of group quantization. Furthermore, it liberates group quantization from inefficient memory rearrangement. The L1 normalization facilitates the implementation of fully quantized normalization layers with impressive convergence accuracy. Our method frees sub-8-bit integer training from pre-processing and supports general devices. This framework achieves negligible accuracy loss across various neural networks and tasks ($0.92\%$ on 4-bit ResNets, $0.61\%$ on 6-bit Transformers). The prototypical implementation of ShiftQuant achieves more than $1.85\times/15.3\%$ performance improvement on CPU/GPU compared to its FP16 counterparts, and $33.9\%$ resource consumption reduction on FPGA than the FP16 counterparts. The proposed fully-quantized L1 normalization layers achieve more than $35.54\%$ improvement in throughout on CPU compared to traditional L2 normalization layers. Moreover, theoretical analysis verifies the advancement of our method.
Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding
Venue: arXiv cs.CV 2024
Year: 2024

Reliable motion forecasting of surrounding agents is essential for ensuring the safe operation of autonomous vehicles. Many existing trajectory prediction methods rely heavily on high-definition (HD) maps as strong driving priors. However, the availability and accuracy of these priors are not guaranteed due to substantial costs to build, localization errors of vehicles, or ongoing road constructions. In this paper, we introduce MFTP, a Map-Free Trajectory Prediction method that offers several advantages. First, it eliminates the need for HD maps during inference while still benefiting from map priors during training via knowledge distillation. Second, we present a novel hierarchical encoder that effectively extracts spatial-temporal agent features and aggregates them into multiple trajectory queries. Additionally, we introduce an iterative decoder that sequentially decodes trajectory queries to generate the final predictions. Extensive experiments show that our approach achieves state-of-the-art performance on the Argoverse dataset under the map-free setting.
AppSign: Multi-level Approximate Computing for Real-Time Traffic Sign Recognition in Autonomous Vehicles
Venue: arXiv cs.AR 2024
Year: 2024

This paper presents a multi-level approximate computing approach for real-time traffic sign recognition in autonomous vehicles called AppSign. Since autonomous vehicles are real-time systems, they must gather environmental information and process them instantaneously to respond properly. However, due to the limited resources of these systems, executing computation-intensive algorithms such as deep-learning schemes that lead to precise output is impossible and takes a long time. To tackle this, imprecise computation schemes compromise the complexity and real-time operations. In this context, AppSign presents a multi-level approximate computing scheme to balance the accuracy and computation cost of the computation-intensive schemes and make them appropriate for real-time applications. AppSign is applied to the CNN-based traffic sign recognition unit by approximating the convolution operation of CNN which is the primal solution for image processing applications. In AppSign a novel approximate multiplication method called "TIRuD" is proposed that truncates the operations while keeping the accuracy acceptable. Moreover, it provides the adaptive approximation of the underlying CNN by involving various levels of computation and considering different approximation methods. The efficiency of the proposed AppSign, in real-time traffic sign recognition, is evaluated through several experiments. Based on these experiments, our proposed TIRuD reduces the accuracy by about $10\%$ while saving execution time about $64\%$ over the exact multiplication, averagely. Moreover, employing our proposed hierarchical approximation in various model layers outperforms the exact computation $27.78\%$ considering "AoC" that joins accuracy and computation cost in a parameter.
Time Step Generating: A Universal Synthesized Deepfake Image Detector
Venue: arXiv cs.CV 2024
Year: 2024

Currently, high-fidelity text-to-image models are developed in an accelerating pace. Among them, Diffusion Models have led to a remarkable improvement in the quality of image generation, making it vary challenging to distinguish between real and synthesized images. It simultaneously raises serious concerns regarding privacy and security. Some methods are proposed to distinguish the diffusion model generated images through reconstructing. However, the inversion and denoising processes are time-consuming and heavily reliant on the pre-trained generative model. Consequently, if the pre-trained generative model meet the problem of out-of-domain, the detection performance declines. To address this issue, we propose a universal synthetic image detector Time Step Generating (TSG), which does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms. Our method utilizes a pre-trained diffusion model's network as a feature extractor to capture fine-grained details, focusing on the subtle differences between real and synthetic images. By controlling the time step t of the network input, we can effectively extract these distinguishing detail features. Then, those features can be passed through a classifier (i.e. Resnet), which efficiently detects whether an image is synthetic or real. We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
Venue: arXiv cs.CV 2024
Year: 2024

In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents significant challenges due to noisy data, unbalanced defect classes, and the complexity of failure modes. To address these challenges, this study proposes a novel method combining a self-encoder-based data augmentation technique with a convolutional neural network (CNN). By introducing noise into the latent space, the self-encoder enhances data diversity and mitigates class imbalance, thereby improving the model's generalization capabilities. The augmented dataset is subsequently used to train the CNN, enabling it to deliver precise classification of both common and rare defect patterns. Experimental results on the WM-811K dataset demonstrate that the proposed method achieves a classification accuracy of 98.56%, surpassing Random Forest, SVM, and Logistic Regression by 19%, 21%, and 27%, respectively. These findings highlight the robustness and effectiveness of the proposed approach, offering a reliable solution for wafer defect detection and classification.
Digital Twin for Advanced Network Planning: Tackling Interference
Venue: arXiv cs.NI 2024
Year: 2024

Operational data in next-generation networks offers a valuable resource for Mobile Network Operators to autonomously manage their systems and predict potential network issues. Machine Learning and Digital Twin can be applied to gain important insights for intelligent decision-making. This paper proposes a framework for Radio Frequency planning and failure detection using Digital Twin reducing the level of manual intervention. In this study, we propose a methodology for analyzing Radio Frequency issues as external interference employing clustering techniques in operational networks, and later incorporating this in the planning process. Simulation results demonstrate that the architecture proposed can improve planning operations through a data-aided anomaly detection strategy.
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data. The growing importance of the "right to be forgotten" calls for effective mechanisms to facilitate data removal upon request. In response, federated unlearning (FU) has been developed to efficiently eliminate the influence of specific data from the model. Current FU methods primarily rely on approximate unlearning strategies, which seek to balance data removal efficacy with computational and communication costs, but often fail to completely erase data influence. To address these limitations, we propose FedUHB, a novel exact unlearning approach that leverages the Polyak heavy ball optimization technique, a first-order method, to achieve rapid retraining. In addition, we introduce a dynamic stopping mechanism to optimize the termination of the unlearning process. Our extensive experiments show that FedUHB not only enhances unlearning efficiency but also preserves robust model performance after unlearning. Furthermore, the dynamic stopping mechanism effectively reduces the number of unlearning iterations, conserving both computational and communication resources. FedUHB can be proved as an effective and efficient solution for exact data removal in federated learning settings.
Knowledge-enhanced Transformer for Multivariate Long Sequence Time-series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Multivariate Long Sequence Time-series Forecasting (LSTF) has been a critical task across various real-world applications. Recent advancements focus on the application of transformer architectures attributable to their ability to capture temporal patterns effectively over extended periods. However, these approaches often overlook the inherent relationships and interactions between the input variables that could be drawn from their characteristic properties. In this paper, we aim to bridge this gap by integrating information-rich Knowledge Graph Embeddings (KGE) with state-of-the-art transformer-based architectures. We introduce a novel approach that encapsulates conceptual relationships among variables within a well-defined knowledge graph, forming dynamic and learnable KGEs for seamless integration into the transformer architecture. We investigate the influence of this integration into seminal architectures such as PatchTST, Autoformer, Informer, and Vanilla Transformer. Furthermore, we thoroughly investigate the performance of these knowledge-enhanced architectures along with their original implementations for long forecasting horizons and demonstrate significant improvement in the benchmark results. This enhancement empowers transformer-based architectures to address the inherent structural relation between variables. Our knowledge-enhanced approach improves the accuracy of multivariate LSTF by capturing complex temporal and relational dynamics across multiple domains. To substantiate the validity of our model, we conduct comprehensive experiments using Weather and Electric Transformer Temperature (ETT) datasets.
Exploring the Impact of Non-Verbal Virtual Agent Behavior on User Engagement in Argumentative Dialogues
Venue: arXiv cs.HC 2024
Year: 2024

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of great importance as user engagement plays a crucial role in determining the success or failure of cooperative argumentative discussions. One main goal is to maintain the user's motivation to participate in a reflective opinion-building process, even when addressing contradicting viewpoints. This work investigates how non-verbal agent behavior, specifically co-speech gestures, influences the user's engagement and interest during an ongoing argumentative interaction. The results of a laboratory study conducted with 56 participants demonstrate that the agent's co-speech gestures have a substantial impact on user engagement and interest and the overall perception of the system. Therefore, this research offers valuable insights for the design of future cooperative argumentative virtual agents.
Pitch-and-Spectrum-Aware Singing Quality Assessment with Bias Correction and Model Fusion
Venue: arXiv cs.SD 2024
Year: 2024

We participated in track 2 of the VoiceMOS Challenge 2024, which aimed to predict the mean opinion score (MOS) of singing samples. Our submission secured the first place among all participating teams, excluding the official baseline. In this paper, we further improve our submission and propose a novel Pitch-and-Spectrum-aware Singing Quality Assessment (PS-SQA) method. The PS-SQA is designed based on the self-supervised-learning (SSL) MOS predictor, incorporating singing pitch and spectral information, which are extracted using pitch histogram and non-quantized neural codec, respectively. Additionally, the PS-SQA introduces a bias correction strategy to address prediction biases caused by low-resource training samples, and employs model fusion technology to further enhance prediction accuracy. Experimental results confirm that our proposed PS-SQA significantly outperforms all competing systems across all system-level metrics, confirming its strong sing quality assessment capabilities.
Approximation algorithms for non-sequential star packing problems
Venue: arXiv cs.DS 2024
Year: 2024

For a positive integer $k \ge 1$, a $k$-star ($k^+$-star, $k^-$-star, respectively) is a connected graph containing a degree-$\ell$ vertex and $\ell$ degree-$1$ vertices, where $\ell = k$ ($\ell \ge k$, $1 \le \ell \le k$, respectively). The $k^+$-star packing problem is to cover as many vertices of an input graph $G$ as possible using vertex-disjoint $k^+$-stars in $G$; and given $k > t \ge 1$, the $k^-/t$-star packing problem is to cover as many vertices of $G$ as possible using vertex-disjoint $k^-$-stars but no $t$-stars in $G$. Both problems are NP-hard for any fixed $k \ge 2$. We present a $(1 + \frac {k^2}{2k+1})$- and a $\frac 32$-approximation algorithms for the $k^+$-star packing problem when $k \ge 3$ and $k = 2$, respectively, and a $(1 + \frac 1{t + 1 + 1/k})$-approximation algorithm for the $k^-/t$-star packing problem when $k > t \ge 2$. They are all local search algorithms and they improve the best known approximation algorithms for the problems, respectively.
RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer
Venue: arXiv cs.LG 2024
Year: 2024

This paper builds upon our previous work on the Reconciled Polynomial Network (RPN). The original RPN model was designed under the assumption of input data independence, presuming the independence among both individual instances within data batches and attributes in each data instance. However, this assumption often proves invalid for function learning tasks involving complex, interdependent data such as language, images, time series, and graphs. Ignoring such data interdependence may inevitably lead to significant performance degradation. To overcome these limitations, we introduce the new Reconciled Polynomial Network (version 2), namely RPN 2, in this paper. By incorporating data and structural interdependence functions, RPN 2 explicitly models data interdependence via new component functions in its architecture. This enhancement not only significantly improves RPN 2's learning performance but also substantially expands its unifying potential, enabling it to encompass a broader range of contemporary dominant backbone models within its canonical representation. These backbones include, but are not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and Transformers. Our analysis reveals that the fundamental distinctions among these backbone models primarily stem from their diverse approaches to defining the interdependence functions. Furthermore, this unified representation opens up new opportunities for designing innovative architectures with the potential to surpass the performance of these dominant backbones.