OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models
Venue: arXiv cs.LG 2024
Year: 2024

Monitoring calf behaviour continuously would be beneficial to identify routine practices (e.g., weaning, dehorning, etc.) that impact calf welfare in dairy farms. In that regard, accelerometer data collected from neck collars can be used along with Machine Learning models to classify calf behaviour automatically. Hand-crafted features are commonly used in Machine Learning models, while ROCKET and Catch22 features are specifically designed for time-series classification problems in related fields. This study aims to compare the performance of ROCKET and Catch22 features to Hand-Crafted features. 30 Irish Holstein Friesian and Jersey pre-weaned calves were monitored using accelerometer sensors allowing for 27.4 hours of annotated behaviors. Additional time-series were computed from the raw X, Y and Z-axis and split into 3-second time windows. ROCKET, Catch22 and Hand-Crafted features were calculated for each time window, and the dataset was then split into the train, validation and test sets. Each set of features was used to train three Machine Learning models (Random Forest, eXtreme Gradient Boosting, and RidgeClassifierCV) to classify six behaviours indicative of pre-weaned calf welfare (drinking milk, grooming, lying, running, walking and other). Models were tuned with the validation set, and the performance of each feature-model combination was evaluated with the test set. The best performance across the three models was obtained with ROCKET [average balanced accuracy +/- standard deviation] (0.70 +/- 0.07), followed by Catch22 (0.69 +/- 0.05), surpassing Hand-Crafted (0.65 +/- 0.034). The best balanced accuracy (0.77) was obtained with ROCKET and RidgeClassifierCV, followed by Catch22 and Random Forest (0.73). Thus, tailoring these approaches for specific behaviours and contexts will be crucial in advancing precision livestock farming and enhancing animal welfare on a larger scale.
WorldGPT: Empowering LLM as Multimodal World Model
Venue: arXiv cs.AI 2024
Year: 2024

World models are progressively being employed across diverse fields, extending from basic environment simulation to complex scenario construction. However, existing models are mainly trained on domain-specific states and actions, and confined to single-modality state representations. In this paper, We introduce WorldGPT, a generalist world model built upon Multimodal Large Language Model (MLLM). WorldGPT acquires an understanding of world dynamics through analyzing millions of videos across various domains. To further enhance WorldGPT's capability in specialized scenarios and long-term tasks, we have integrated it with a novel cognitive architecture that combines memory offloading, knowledge retrieval, and context reflection. As for evaluation, we build WorldNet, a multimodal state transition prediction benchmark encompassing varied real-life scenarios. Conducting evaluations on WorldNet directly demonstrates WorldGPT's capability to accurately model state transition patterns, affirming its effectiveness in understanding and predicting the dynamics of complex scenarios. We further explore WorldGPT's emerging potential in serving as a world simulator, helping multimodal agents generalize to unfamiliar domains through efficiently synthesising multimodal instruction instances which are proved to be as reliable as authentic data for fine-tuning purposes. The project is available on \url{https://github.com/DCDmllm/WorldGPT}.
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation
Venue: arXiv cs.CV 2024
Year: 2024

Skeleton-based action recognition is vital for comprehending human-centric videos and has applications in diverse domains. One of the challenges of skeleton-based action recognition is dealing with low-quality data, such as skeletons that have missing or inaccurate joints. This paper addresses the issue of enhancing action recognition using low-quality skeletons through a general knowledge distillation framework. The proposed framework employs a teacher-student model setup, where a teacher model trained on high-quality skeletons guides the learning of a student model that handles low-quality skeletons. To bridge the gap between heterogeneous high-quality and lowquality skeletons, we present a novel part-based skeleton matching strategy, which exploits shared body parts to facilitate local action pattern learning. An action-specific part matrix is developed to emphasize critical parts for different actions, enabling the student model to distill discriminative part-level knowledge. A novel part-level multi-sample contrastive loss achieves knowledge transfer from multiple high-quality skeletons to low-quality ones, which enables the proposed knowledge distillation framework to include training low-quality skeletons that lack corresponding high-quality matches. Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D HOI datasets demonstrate the effectiveness of the proposed knowledge distillation framework.
TextGram: Towards a better domain-adaptive pretraining
Venue: arXiv cs.CL 2024
Year: 2024

For green AI, it is crucial to measure and reduce the carbon footprint emitted during the training of large language models. In NLP, performing pre-training on Transformer models requires significant computational resources. This pre-training involves using a large amount of text data to gain prior knowledge for performing downstream tasks. Thus, it is important that we select the correct data in the form of domain-specific data from this vast corpus to achieve optimum results aligned with our domain-specific tasks. While training on large unsupervised data is expensive, it can be optimized by performing a data selection step before pretraining. Selecting important data reduces the space overhead and the substantial amount of time required to pre-train the model while maintaining constant accuracy. We investigate the existing selection strategies and propose our own domain-adaptive data selection method - TextGram - that effectively selects essential data from large corpora. We compare and evaluate the results of finetuned models for text classification task with and without data selection. We show that the proposed strategy works better compared to other selection methods.
From Persona to Personalization: A Survey on Role-Playing Language Agents
Venue: arXiv cs.CL 2024
Year: 2024

Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate assigned personas. By harnessing multiple advanced abilities of LLMs, including in-context learning, instruction following, and social intelligence, RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. RPLAs can mimic a wide range of personas, ranging from historical figures and fictional characters to real-life individuals. Consequently, they have catalyzed numerous AI applications, such as emotional companions, interactive video games, personalized assistants and copilots, and digital clones. In this paper, we conduct a comprehensive survey of this field, illustrating the evolution and recent progress in RPLAs integrating with cutting-edge LLM technologies. We categorize personas into three types: 1) Demographic Persona, which leverages statistical stereotypes; 2) Character Persona, focused on well-established figures; and 3) Individualized Persona, customized through ongoing user interactions for personalized services. We begin by presenting a comprehensive overview of current methodologies for RPLAs, followed by the details for each persona type, covering corresponding data sourcing, agent construction, and evaluation. Afterward, we discuss the fundamental risks, existing limitations, and future prospects of RPLAs. Additionally, we provide a brief review of RPLAs in AI applications, which reflects practical user demands that shape and drive RPLA research. Through this work, we aim to establish a clear taxonomy of RPLA research and applications, and facilitate future research in this critical and ever-evolving field, and pave the way for a future where humans and RPLAs coexist in harmony.
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
Venue: arXiv cs.LG 2024
Year: 2024

Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at removing undesired data influences and associated model capabilities without compromising utility beyond the scope of unlearning. While interest in studying LLM unlearning is growing, the impact of the optimizer choice for LLM unlearning remains unexplored. In this work, we shed light on the significance of optimizer selection in LLM unlearning for the first time, establishing a clear connection between second-order optimization and influence unlearning (a classical approach using influence functions to update the model for data influence removal). This insight propels us to develop a second-order optimization-based LLM unlearning framework, termed Second-Order UnLearning (SOUL), which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. Our extensive experiments show that SOUL consistently outperforms conventional first-order methods across various unlearning tasks, models, and metrics, indicating that second-order optimization offers an effective and broadly applicable solution for LLM unlearning. Codes are available at https://github.com/OPTML-Group/SOUL.
LEGENT: Open Platform for Embodied Agents
Venue: arXiv cs.CL 2024
Year: 2024

Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in physical environments. Existing integrations often feature limited open sourcing, challenging collective progress in this field. We introduce LEGENT, an open, scalable platform for developing embodied agents using LLMs and LMMs. LEGENT offers a dual approach: a rich, interactive 3D environment with communicable and actionable agents, paired with a user-friendly interface, and a sophisticated data generation pipeline utilizing advanced algorithms to exploit supervision from simulated worlds at scale. In our experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks, showcasing promising generalization capabilities.
Tenspiler: A Verified Lifting-Based Compiler for Tensor Operations (Extended Version)
Venue: arXiv cs.PL 2024
Year: 2024

Tensor processing infrastructures such as deep learning frameworks and specialized hardware accelerators have revolutionized how computationally intensive code from domains such as deep learning and image processing is executed and optimized. These infrastructures provide powerful and expressive abstractions while ensuring high performance. However, to utilize them, code must be written specifically using the APIs / ISAs of such software frameworks or hardware accelerators. Importantly, given the fast pace of innovation in these domains, code written today quickly becomes legacy as new frameworks and accelerators are developed, and migrating such legacy code manually is a considerable effort. To enable developers in leveraging such DSLs while preserving their current programming paradigm, we introduce Tenspiler, a verified lifting-based compiler that uses program synthesis to translate sequential programs written in general-purpose programming languages (e.g., C++ or Python code) into tensor operations. Central to Tenspiler is our carefully crafted yet simple intermediate language, named TensIR, that expresses tensor operations. TensIR enables efficient lifting, verification, and code generation. Currently, Tenspiler already supports $\textbf{six}$ DSLs, spanning a broad spectrum of software and hardware environments. Furthermore, we show that new backends can be easily supported by Tenspiler by adding simple pattern-matching rules for TensIR. Using 10 real-world code benchmark suites, our experimental evaluation shows that by translating code to be executed on $\textbf{6}$ different software frameworks and hardware devices, Tenspiler offers on average 105$\times$ kernel and 9.65$\times$ end-to-end execution time improvement over the fully-optimized sequential implementation of the same benchmarks.
Robust Resource Sharing in Network Slicing via Hypothesis Testing
Venue: arXiv cs.NI 2024
Year: 2024

In network slicing, the network operator needs to satisfy the service level agreements of multiple slices at the same time and on the same physical infrastructure. To do so with reduced provisioned resources, the operator may consider resource sharing mechanisms. However, each slice then becomes susceptible to traffic surges in other slices which degrades performance isolation. To maintain both high efficiency and high isolation, we propose the introduction of hypothesis testing in resource sharing. Our approach comprises two phases. In the trial phase, the operator obtains a stochastic model for each slice that describes its normal behavior, provisions resources and then signs the service level agreements. In the regular phase, whenever there is resource contention, hypothesis testing is conducted to check which slices follow their normal behavior. Slices that fail the test are excluded from resource sharing to protect the well-behaved ones. We test our approach on a mobile traffic dataset. Results show that our approach fortifies the service level agreements against unexpected traffic patterns and achieves high efficiency via resource sharing. Overall, our approach provides an appealing tradeoff between efficiency and isolation.
Out-of-distribution Detection in Medical Image Analysis: A survey
Venue: arXiv cs.CV 2024
Year: 2024

Computer-aided diagnostics has benefited from the development of deep learning-based computer vision techniques in these years. Traditional supervised deep learning methods assume that the test sample is drawn from the identical distribution as the training data. However, it is possible to encounter out-of-distribution samples in real-world clinical scenarios, which may cause silent failure in deep learning-based medical image analysis tasks. Recently, research has explored various out-of-distribution (OOD) detection situations and techniques to enable a trustworthy medical AI system. In this survey, we systematically review the recent advances in OOD detection in medical image analysis. We first explore several factors that may cause a distributional shift when using a deep-learning-based model in clinic scenarios, with three different types of distributional shift well defined on top of these factors. Then a framework is suggested to categorize and feature existing solutions, while the previous studies are reviewed based on the methodology taxonomy. Our discussion also includes evaluation protocols and metrics, as well as the challenge and a research direction lack of exploration.
Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increased number of FL users may hinder completing large-scale FL over wireless networks due to high imposed latency. Cell-free massive multiple-input multiple-output~(CFmMIMO) is a promising architecture for implementing FL because it serves many users on the same time/frequency resources. While CFmMIMO enhances energy efficiency through spatial multiplexing and collaborative beamforming, it remains crucial to meticulously allocate uplink transmission powers to the FL users. In this paper, we propose an uplink power allocation scheme in FL over CFmMIMO by considering the effect of each user's power on the energy and latency of other users to jointly minimize the users' uplink energy and the latency of FL training. The proposed solution algorithm is based on the coordinate gradient descent method. Numerical results show that our proposed method outperforms the well-known max-sum rate by increasing up to~$27$\% and max-min energy efficiency of the Dinkelbach method by increasing up to~$21$\% in terms of test accuracy while having limited uplink energy and latency budget for FL over CFmMIMO.
BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models
Venue: arXiv cs.DC 2024
Year: 2024

The increasing demand for Large Language Models (LLMs) across various applications has led to a significant shift in the design of deep learning serving systems. Deploying LLMs, particularly in multi-tenant environments, poses substantial challenges due to their high computational and memory demands. We introduce BlockLLM, a serving system that leverages component sharing among fine-tuned LLM models to provide an efficient and flexible solution for LLM workloads. BlockLLM partitions models into finer-grained blocks, enabling the reuse of model components and independent provisioning to improve computation efficiency. BlockLLM comprises an offline block zoo for storing blocks and an online system to serve requests through chains of blocks. It offers multi-fold flexibilities: (1) Adaptive assembly of blocks on-the-fly through equivalence evaluation among blocks in the zoo; (2) Per-block batch size configuration and best-effort KV cache coordination at the individual block level; (3) Speculative execution and locality-aware block placement to reduce communication costs from dynamic block resource allocation. Our evaluation shows that BlockLLM reduces memory and storage footprints and improves computational efficiency, outperforming existing serving approach in 95%ile latency and GPU utilization by 33.5% and 20.1%, respectively, with minimal impact on accuracy
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
Venue: arXiv cs.CR 2024
Year: 2024

With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play a crucial role in ensuring network security, actively identifying and mitigating malicious behaviours. However, the relentless advancement of cyber-threats has rendered traditional/classical approaches insufficient in addressing the sophistication and complexity of attacks. This paper proposes a novel 3-stage intrusion detection system inspired by a simplified version of the Lockheed Martin cyber kill chain to detect advanced multi-step attacks. The proposed approach consists of three models, each responsible for detecting a group of attacks with common characteristics. The detection outcome of the first two stages is used to conduct a feasibility study on the possibility of predicting attacks in the third stage. Using the ToN IoT dataset, we achieved an average of 94% F1-Score among different stages, outperforming the benchmark approaches based on Random-forest model. Finally, we comment on the feasibility of this approach to be integrated in a real-world system and propose various possible future work.
L-DIT: A dApp for Live Detectability, Identifiability and Trackability for ASOs on the Behavioral Dynamics Blockchain
Venue: arXiv cs.CR 2024
Year: 2024

As the number of Anthropogenic Space Objects (ASOs) grows, there is an urgent need to ensure space safety, security, and sustainability (S3) for long-term space use. Currently, no globally effective method can quantify the safety, security, and Sustainability of all ASOs in orbit. Existing methods such as the Space Sustainability Rating (SSR) rely on volunteering private information to provide sustainability ratings. However, the need for such sensitive data might prove to be a barrier to adoption for space entities. For effective comparison of ASOs, the rating mechanism should apply to all ASOs, even retroactively, so that the sustainability of a single ASO can be assessed holistically. Lastly, geopolitical boundaries and alignments play a crucial and limiting role in a volunteered rating system, limiting the space safety, security, and sustainability. This work presents a Live Detectability, Identifiability, and Trackability (L-DIT) score through a distributed app (dApp) built on top of the Behavioral Dynamics blockchain (BDB). The BDB chain is a space situational awareness (SSA) chain that provides verified and cross-checked ASO data from multiple sources. This unique combination of consensus-based information from BDB and permissionless access to data allows the DIT scoring method presented here to be applied to all ASOs. While the underlying BDB chain collects, filters, and validates SSA data from various open (and closed if available) sources, the L-DIT dApp consumes the data from the chain to provide L-DIT score that can contribute towards an operator's, manufacturer's, or owner's sustainability practices. Our dApp provides data for all ASOs, allowing their sustainability score to be compared against other ASOs, regardless of geopolitical alignments, providing business value to entities such as space insurance providers and enabling compliance validation and enforcement.
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

In the burgeoning field of large language models (LLMs), the assessment of fundamental knowledge remains a critical challenge, particularly for models tailored to Chinese language and culture. This paper introduces FoundaBench, a pioneering benchmark designed to rigorously evaluate the fundamental knowledge capabilities of Chinese LLMs. FoundaBench encompasses a diverse array of 3354 multiple-choice questions across common sense and K-12 educational subjects, meticulously curated to reflect the breadth and depth of everyday and academic knowledge. We present an extensive evaluation of 12 state-of-the-art LLMs using FoundaBench, employing both traditional assessment methods and our CircularEval protocol to mitigate potential biases in model responses. Our results highlight the superior performance of models pre-trained on Chinese corpora, and reveal a significant disparity between models' reasoning and memory recall capabilities. The insights gleaned from FoundaBench evaluations set a new standard for understanding the fundamental knowledge of LLMs, providing a robust framework for future advancements in the field.
QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond
Venue: arXiv cs.CL 2024
Year: 2024

The proliferation of social media has led to information overload and increased interest in opinion mining. We propose "Question-Answering Network Analysis" (QANA), a novel opinion mining framework that utilizes Large Language Models (LLMs) to generate questions from users' comments, constructs a bipartite graph based on the comments' answerability to the questions, and applies centrality measures to examine the importance of opinions. We investigate the impact of question generation styles, LLM selections, and the choice of embedding model on the quality of the constructed QA networks by comparing them with annotated Key Point Analysis datasets. QANA achieves comparable performance to previous state-of-the-art supervised models in a zero-shot manner for Key Point Matching task, also reducing the computational cost from quadratic to linear. For Key Point Generation, questions with high PageRank or degree centrality align well with manually annotated key points. Notably, QANA enables analysts to assess the importance of key points from various aspects according to their selection of centrality measure. QANA's primary contribution lies in its flexibility to extract key points from a wide range of perspectives, which enhances the quality and impartiality of opinion mining.