OpenRaiser/Intern-Atlas huggingface dataset:
Learning Shortcuts: On the Misleading Promise of NLU in Language Models
Venue: arXiv cs.CL 2024
Year: 2024

The advent of large language models (LLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found that LLMs often resort to shortcuts when performing tasks, creating an illusion of enhanced performance while lacking generalizability in their decision rules. This phenomenon introduces challenges in accurately assessing natural language understanding in LLMs. Our paper provides a concise survey of relevant research in this area and puts forth a perspective on the implications of shortcut learning in the evaluation of language models, specifically for NLU tasks. This paper urges more research efforts to be put towards deepening our comprehension of shortcut learning, contributing to the development of more robust language models, and raising the standards of NLU evaluation in real-world scenarios.
Cost-effective and performant virtual WANs with CORNIFER
Venue: arXiv cs.NI 2024
Year: 2024

Virtual wide-area networks (WANs) are WAN-as-a-service cloud offerings that aim to bring the performance benefits of dedicated wide-area interconnects to enterprise customers. In this work, we show that the topology of a virtual WAN can render it both performance and cost inefficient. We develop Cornifer, a tool that designs virtual WAN topologies by deciding the number of virtual WAN nodes and their location in the cloud to minimize connection latency at low cost to enterprises. By leveraging millions of latency measurements from vantage points across the world to cloud points of presence, Cornifer designs virtual WAN topologies that improve weighted client latency by 26% and lower cost by 28% compared to the state-of-the-art. Cornifer identifies virtual WAN topologies at the Pareto frontier of the deployment cost vs. connection latency trade-off and proposes a heuristic for automatic selection of Pareto-optimal virtual WAN topologies for enterprises.
XTable in Action: Seamless Interoperability in Data Lakes
Venue: arXiv cs.DB 2024
Year: 2024

Contemporary approaches to data management are increasingly relying on unified analytics and AI platforms to foster collaboration, interoperability, seamless access to reliable data, and high performance. Data Lakes featuring open standard table formats such as Delta Lake, Apache Hudi, and Apache Iceberg are central components of these data architectures. Choosing the right format for managing a table is crucial for achieving the objectives mentioned above. The challenge lies in selecting the best format, a task that is onerous and can yield temporary results, as the ideal choice may shift over time with data growth, evolving workloads, and the competitive development of table formats and processing engines. Moreover, restricting data access to a single format can hinder data sharing resulting in diminished business value over the long term. The ability to seamlessly interoperate between formats and with negligible overhead can effectively address these challenges. Our solution in this direction is an innovative omni-directional translator, XTable, that facilitates writing data in one format and reading it in any format, thus achieving the desired format interoperability. In this work, we demonstrate the effectiveness of XTable through application scenarios inspired by real-world use cases.
Large Language Models Help Reveal Unhealthy Diet and Body Concerns in Online Eating Disorders Communities
Venue: arXiv cs.SI 2024
Year: 2024

Eating disorders (ED), a severe mental health condition with high rates of mortality and morbidity, affect millions of people globally, especially adolescents. The proliferation of online communities that promote and normalize ED has been linked to this public health crisis. However, identifying harmful communities is challenging due to the use of coded language and other obfuscations. To address this challenge, we propose a novel framework to surface implicit attitudes of online communities by adapting large language models (LLMs) to the language of the community. We describe an alignment method and evaluate results along multiple dimensions of semantics and affect. We then use the community-aligned LLM to respond to psychometric questionnaires designed to identify ED in individuals. We demonstrate that LLMs can effectively adopt community-specific perspectives and reveal significant variations in eating disorder risks in different online communities. These findings highlight the utility of LLMs to reveal implicit attitudes and collective mindsets of communities, offering new tools for mitigating harmful content on social media.
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
Venue: arXiv cs.CV 2024
Year: 2024

Neural style transfer (NST) generates new images by combining the style of one image with the content of another. However, unauthorized NST can exploit artwork, raising concerns about artists' rights and motivating the development of proactive protection methods. We propose Locally Adaptive Adversarial Color Attack (LAACA), empowering artists to protect their artwork from unauthorized style transfer by processing before public release. By delving into the intricacies of human visual perception and the role of different frequency components, our method strategically introduces frequency-adaptive perturbations in the image. These perturbations significantly degrade the generation quality of NST while maintaining an acceptable level of visual change in the original image, ensuring that potential infringers are discouraged from using the protected artworks, because of its bad NST generation quality. Additionally, existing metrics often overlook the importance of color fidelity in evaluating color-mattered tasks, such as the quality of NST-generated images, which is crucial in the context of artistic works. To comprehensively assess the color-mattered tasks, we propose the Adversarial Color Distance Metric (ACDM), designed to quantify the color difference of images pre- and post-manipulations. Experimental results confirm that attacking NST using LAACA results in visually inferior style transfer, and the ACDM can efficiently measure color-mattered tasks. By providing artists with a tool to safeguard their intellectual property, our work relieves the socio-technical challenges posed by the misuse of NST in the art community.
Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks
Venue: arXiv cs.LG 2024
Year: 2024

Categorical variables often appear in datasets for classification and regression tasks, and they need to be encoded into numerical values before training. Since many encoders have been developed and can significantly impact performance, choosing the appropriate encoder for a task becomes a time-consuming yet important practical issue. This study broadly classifies machine learning models into three categories: 1) ATI models that implicitly perform affine transformations on inputs, such as multi-layer perceptron neural network; 2) Tree-based models that are based on decision trees, such as random forest; and 3) the rest, such as kNN. Theoretically, we prove that the one-hot encoder is the best choice for ATI models in the sense that it can mimic any other encoders by learning suitable weights from the data. We also explain why the target encoder and its variants are the most suitable encoders for tree-based models. This study conducted comprehensive computational experiments to evaluate 14 encoders, including one-hot and target encoders, along with eight common machine-learning models on 28 datasets. The computational results agree with our theoretical analysis. The findings in this study shed light on how to select the suitable encoder for data scientists in fields such as fraud detection, disease diagnosis, etc.
Should ChatGPT Write Your Breakup Text? Exploring the Role of AI in Relationship Dissolution
Venue: arXiv cs.HC 2024
Year: 2024

Relationships are essential to our happiness and wellbeing, yet their dissolution-the final stage of a relationship's lifecycle-is among the most stressful events individuals can experience, often leading to profound and lasting impacts. With the breakup process increasingly facilitated by technology, such as computer-mediated communication, and the likely future influence of generative AI (GenAI) tools, we conducted a semi-structured interview study with 21 participants. We aim to understand: 1) the current role of technology in the breakup process, 2) the needs and support individuals seek during this time, and 3) how GenAI might address or undermine these needs. Our findings show that people have distinct needs at various stages of breakups. While currently technology plays an important role, it falls short in supporting users' unmet needs. Participants envision that GenAI could: 1) aid in prompting self-reflection, providing neutral second opinions, and assisting with planning leading up to a breakup; 2) serve as a communication mediator, supporting wording and tone to facilitate emotional expression during breakup conversations; and 3) support personal growth and offer companionship after a breakup. However, our findings also reveal participants' concerns about involving GenAI in this process. Based on our results, we discuss the potential opportunities, design considerations, and harms of GenAI tools in facilitating people's relationship dissolution.
Joint Beam Direction Control and Radio Resource Allocation in Dynamic Multi-beam LEO Satellite Networks
Venue: arXiv cs.IT 2024
Year: 2024

Multi-beam low earth orbit (LEO) satellites are emerging as key components in beyond 5G and 6G to provide global coverage and high data rate. To fully unleash the potential of LEO satellite communication, resource management plays a key role. However, the uneven distribution of users, the coupling of multi-dimensional resources, complex inter-beam interference, and time-varying network topologies all impose significant challenges on effective communication resource management. In this paper, we study the joint optimization of beam direction and the allocation of spectrum, time, and power resource in a dynamic multi-beam LEO satellite network. The objective is to improve long-term user sum data rate while taking user fairness into account. Since the concerned resource management problem is mixed-integer non-convex programming, the problem is decomposed into three subproblems, namely beam direction control and time slot allocation, user subchannel assignment, and beam power allocation. Then, these subproblems are solved iteratively by leveraging matching with externalities and successive convex approximation, and the proposed algorithms are analyzed in terms of stability, convergence, and complexity. Extensive simulations are conducted, and the results demonstrate that our proposal can improve the number of served users by up to two times and the sum user data rate by up to 68%, compared to baseline schemes.
ASA -- The Adaptive Scheduling Algorithm
Venue: arXiv cs.DC 2024
Year: 2024

In High Performance Computing (HPC) infrastructures, the control of resources by batch systems can lead to prolonged queue waiting times and adverse effects on the overall execution times of applications, particularly in data-intensive and low-latency workflows where efficient processing hinges on resource planning and timely allocation. Allocating the maximum capacity upfront ensures the fastest execution but results in spare and idle resources, extended queue waits, and costly usage. Conversely, dynamic allocation based on workflow stage requirements optimizes resource usage but may negatively impact the total workflow makespan. To address these issues, we introduce ASA, the Adaptive Scheduling Algorithm. ASA is a novel, convergence-proven scheduling technique that minimizes jobs inter-stage waiting times by estimating the queue waiting times to proactively submit resource change requests ahead of time. It strikes a balance between exploration and exploitation, considering both learning (waiting times) and applying learnt insights. Real-world experiments over two supercomputers centers with scientific workflows demonstrate ASA's effectiveness, achieving near-optimal resource utilization and accuracy, with up to 10% and 2% reductions in average workflow queue waiting times and makespan, respectively.
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Audio-visual speech recognition (AVSR) is a multimodal extension of automatic speech recognition (ASR), using video as a complement to audio. In AVSR, considerable efforts have been directed at datasets for facial features such as lip-readings, while they often fall short in evaluating the image comprehension capabilities in broader contexts. In this paper, we construct SlideAVSR, an AVSR dataset using scientific paper explanation videos. SlideAVSR provides a new benchmark where models transcribe speech utterances with texts on the slides on the presentation recordings. As technical terminologies that are frequent in paper explanations are notoriously challenging to transcribe without reference texts, our SlideAVSR dataset spotlights a new aspect of AVSR problems. As a simple yet effective baseline, we propose DocWhisper, an AVSR model that can refer to textual information from slides, and confirm its effectiveness on SlideAVSR.
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Nuclei instance segmentation in histopathological images is of great importance for biological analysis and cancer diagnosis but remains challenging for two reasons. (1) Similar visual presentation of intranuclear and extranuclear regions of chromophobe nuclei often causes under-segmentation, and (2) current methods lack the exploration of nuclei structure, resulting in fragmented instance predictions. To address these problems, this paper proposes a structure encoding and interaction network, termed SEINE, which develops the structure modeling scheme of nuclei and exploits the structure similarity between nuclei to improve the integrality of each segmented instance. Concretely, SEINE introduces a contour-based structure encoding (SE) that considers the correlation between nuclei structure and semantics, realizing a reasonable representation of the nuclei structure. Based on the encoding, we propose a structure-guided attention (SGA) module that takes the clear nuclei as prototypes to enhance the structure learning for the fuzzy nuclei. To strengthen the structural learning ability, a semantic feature fusion (SFF) is presented to boost the semantic consistency of semantic and structure branches. Furthermore, a position enhancement (PE) method is applied to suppress incorrect nuclei boundary predictions. Extensive experiments demonstrate the superiority of our approaches, and SEINE achieves state-of-the-art (SOTA) performance on four datasets. The code is available at https://github.com/zhangye-zoe/SEINE.
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
Venue: arXiv cs.CV 2024
Year: 2024

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects.
A Survey on Hardware Accelerators for Large Language Models
Venue: arXiv cs.AR 2024
Year: 2024

Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. As the demand for more sophisticated LLMs continues to grow, there is a pressing need to address the computational challenges associated with their scale and complexity. This paper presents a comprehensive survey on hardware accelerators designed to enhance the performance and energy efficiency of Large Language Models. By examining a diverse range of accelerators, including GPUs, FPGAs, and custom-designed architectures, we explore the landscape of hardware solutions tailored to meet the unique computational demands of LLMs. The survey encompasses an in-depth analysis of architecture, performance metrics, and energy efficiency considerations, providing valuable insights for researchers, engineers, and decision-makers aiming to optimize the deployment of LLMs in real-world applications.
Deep Back-Filling: a Split Window Technique for Deep Online Cluster Job Scheduling
Venue: arXiv cs.DC 2024
Year: 2024

Job scheduling is a critical component of workload management systems that can significantly influence system performance, e.g., in HPC clusters. The scheduling objectives are often mixed, such as maximizing resource utilization and minimizing job waiting time. An increasing number of researchers are moving from heuristic-based approaches to Deep Reinforcement Learning approaches in order to optimize scheduling objectives. However, the job scheduler's state space is partially observable to a DRL-based agent because the job queue is practically unbounded. The agent's observation of the state space is constant in size since the input size of the neural networks is predefined. All existing solutions to this problem intuitively allow the agent to observe a fixed window size of jobs at the head of the job queue. In our research, we have seen that such an approach can lead to "window staleness" where the window becomes full of jobs that can not be scheduled until the cluster has completed sufficient work. In this paper, we propose a novel general technique that we call \emph{split window}, which allows the agent to observe both the head \emph{and tail} of the queue. With this technique, the agent can observe all arriving jobs at least once, which completely eliminates the window staleness problem. By leveraging the split window, the agent can significantly reduce the average job waiting time and average queue length, alternatively allowing the use of much smaller windows and, therefore, faster training times. We show a range of simulation results using HPC job scheduling trace data that supports the effectiveness of our technique.
MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

State-of-the-art video object detection methods maintain a memory structure, either a sliding window or a memory queue, to enhance the current frame using attention mechanisms. However, we argue that these memory structures are not efficient or sufficient because of two implied operations: (1) concatenating all features in memory for enhancement, leading to a heavy computational cost; (2) frame-wise memory updating, preventing the memory from capturing more temporal information. In this paper, we propose a multi-level aggregation architecture via memory bank called MAMBA. Specifically, our memory bank employs two novel operations to eliminate the disadvantages of existing methods: (1) light-weight key-set construction which can significantly reduce the computational cost; (2) fine-grained feature-wise updating strategy which enables our method to utilize knowledge from the whole video. To better enhance features from complementary levels, i.e., feature maps and proposals, we further propose a generalized enhancement operation (GEO) to aggregate multi-level features in a unified manner. We conduct extensive evaluations on the challenging ImageNetVID dataset. Compared with existing state-of-the-art methods, our method achieves superior performance in terms of both speed and accuracy. More remarkably, MAMBA achieves mAP of 83.7/84.6% at 12.6/9.1 FPS with ResNet-101. Code is available at https://github.com/guanxiongsun/vfe.pytorch.
HGAttack: Transferable Heterogeneous Graph Adversarial Attack
Venue: arXiv cs.LG 2024
Year: 2024

Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce, where resilience against adversarial attacks is crucial. However, existing adversarial attack methods, which are primarily designed for homogeneous graphs, fall short when applied to HGNNs due to their limited ability to address the structural and semantic complexity of HGNNs. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs. We design a novel surrogate model to closely resemble the behaviors of the target HGNN and utilize gradient-based methods for perturbation generation. Specifically, the proposed surrogate model effectively leverages heterogeneous information by extracting meta-path induced subgraphs and applying GNNs to learn node embeddings with distinct semantics from each subgraph. This approach improves the transferability of generated attacks on the target HGNN and significantly reduces memory costs. For perturbation generation, we introduce a semantics-aware mechanism that leverages subgraph gradient information to autonomously identify vulnerable edges across a wide range of relations within a constrained perturbation budget. We validate HGAttack's efficacy with comprehensive experiments on three datasets, providing empirical analyses of its generated perturbations. Outperforming baseline methods, HGAttack demonstrated significant efficacy in diminishing the performance of target HGNN models, affirming the effectiveness of our approach in evaluating the robustness of HGNNs against adversarial attacks.
A Comprehensive Scalable Framework for Cloud-Native Pattern Detection with Enhanced Expressiveness
Venue: arXiv cs.DB 2024
Year: 2024

Detecting complex patterns in large volumes of event logs has diverse applications in various domains, such as business processes and fraud detection. Existing systems like ELK are commonly used to tackle this challenge, but their performance deteriorates for large patterns, while they suffer from limitations in terms of expressiveness and explanatory capabilities for their responses. In this work, we propose a solution that integrates a Complex Event Processing (CEP) engine into a broader query processsor on top of a decoupled storage infrastructure containing inverted indices of log events. The results demonstrate that our system excels in scalability and robustness, particularly in handling complex queries. Notably, our proposed system delivers responses for large complex patterns within seconds, while ELK experiences timeouts after 10 minutes. It also significantly outperforms solutions relying on FlinkCEP and executing MATCH_RECOGNIZE SQL queries.