ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10175

Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier

引用次数: 0

Construction of CCC and ZCCS Through Additive Characters Over Galois Field 通过伽罗瓦场上的加法字符构建 CCC 和 ZCCS

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09757

Gobinda Ghosh, S. Majhi, Subhabrata Paul

引用次数: 0

Parameterized Algorithms for Steiner Forest in Bounded Width Graphs 有界宽度图中斯坦纳森林的参数化算法

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09835

A. Feldmann, M. Lampis

{"title":"Parameterized Algorithms for Steiner Forest in Bounded Width Graphs","authors":"A. Feldmann, M. Lampis","doi":"10.48550/arXiv.2402.09835","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09835","url":null,"abstract":"In this paper we reassess the parameterized complexity and approximability of the well-studied Steiner Forest problem in several graph classes of bounded width. The problem takes an edge-weighted graph and pairs of vertices as input, and the aim is to find a minimum cost subgraph in which each given vertex pair lies in the same connected component. It is known that this problem is APX-hard in general, and NP-hard on graphs of treewidth 3, treedepth 4, and feedback vertex set size 2. However, Bateni, Hajiaghayi and Marx [JACM, 2011] gave an approximation scheme with a runtime of $n^{O(frac{k^2}{varepsilon})}$ on graphs of treewidth $k$. Our main result is a much faster efficient parameterized approximation scheme (EPAS) with a runtime of $2^{O(frac{k^2}{varepsilon} log frac{k^2}{varepsilon})} cdot n^{O(1)}$. If $k$ instead is the vertex cover number of the input graph, we show how to compute the optimum solution in $2^{O(k log k)} cdot n^{O(1)}$ time, and we also prove that this runtime dependence on $k$ is asymptotically best possible, under ETH. Furthermore, if $k$ is the size of a feedback edge set, then we obtain a faster $2^{O(k)} cdot n^{O(1)}$ time algorithm, which again cannot be improved under ETH.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization 梦想捕手针对语义一致的文本到图像个性化的外观匹配自我关注

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09812

Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungryong Kim, Seunggyu Chang

{"title":"DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization","authors":"Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungryong Kim, Seunggyu Chang","doi":"10.48550/arXiv.2402.09812","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09812","url":null,"abstract":"The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts. Conventional methods representing the reference concepts using unique text embeddings often fail to accurately mimic the appearance of the reference. To address this, one solution may be explicitly conditioning the reference images into the target denoising process, known as key-value replacement. However, prior works are constrained to local editing since they disrupt the structure path of the pre-trained T2I model. To overcome this, we propose a novel plug-in method, called DreamMatcher, which reformulates T2I personalization as semantic matching. Specifically, DreamMatcher replaces the target values with reference values aligned by semantic matching, while leaving the structure path unchanged to preserve the versatile capability of pre-trained T2I models for generating diverse structures. We also introduce a semantic-consistent masking strategy to isolate the personalized concept from irrelevant regions introduced by the target prompts. Compatible with existing T2I models, DreamMatcher shows significant improvements in complex scenarios. Intensive analyses demonstrate the effectiveness of our approach.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSTEM: A Cognitive Platform for Collecting Cyber Threat Intelligence in the Wild TSTEM：在野外收集网络威胁情报的认知平台

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09973

Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, A. Mahmoodi, Justin Seby, Panos Kostakos

{"title":"TSTEM: A Cognitive Platform for Collecting Cyber Threat Intelligence in the Wild","authors":"Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, A. Mahmoodi, Justin Seby, Panos Kostakos","doi":"10.48550/arXiv.2402.09973","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09973","url":null,"abstract":"The extraction of cyber threat intelligence (CTI) from open sources is a rapidly expanding defensive strategy that enhances the resilience of both Information Technology (IT) and Operational Technology (OT) environments against large-scale cyber-attacks. While previous research has focused on improving individual components of the extraction process, the community lacks open-source platforms for deploying streaming CTI data pipelines in the wild. To address this gap, the study describes the implementation of an efficient and well-performing platform capable of processing compute-intensive data pipelines based on the cloud computing paradigm for real-time detection, collecting, and sharing CTI from different online sources. We developed a prototype platform (TSTEM), a containerized microservice architecture that uses Tweepy, Scrapy, Terraform, ELK, Kafka, and MLOps to autonomously search, extract, and index IOCs in the wild. Moreover, the provisioning, monitoring, and management of the TSTEM platform are achieved through infrastructure as a code (IaC). Custom focus crawlers collect web content, which is then processed by a first-level classifier to identify potential indicators of compromise (IOCs). If deemed relevant, the content advances to a second level of extraction for further examination. Throughout this process, state-of-the-art NLP models are utilized for classification and entity extraction, enhancing the overall IOC extraction methodology. Our experimental results indicate that these models exhibit high accuracy (exceeding 98%) in the classification and extraction tasks, achieving this performance within a time frame of less than a minute. The effectiveness of our system can be attributed to a finely-tuned IOC extraction method that operates at multiple stages, ensuring precise identification of relevant information with low false positives.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM-based Federated Recommendation 基于 LLM 的联合推荐

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09959

Jujia Zhao, Wenjie Wang, Chen Xu, Zhaochun Ren, See-kiong Ng, Tat-seng Chua

{"title":"LLM-based Federated Recommendation","authors":"Jujia Zhao, Wenjie Wang, Chen Xu, Zhaochun Ren, See-kiong Ng, Tat-seng Chua","doi":"10.48550/arXiv.2402.09959","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09959","url":null,"abstract":"Large Language Models (LLMs), with their advanced contextual understanding abilities, have demonstrated considerable potential in enhancing recommendation systems via fine-tuning methods. However, fine-tuning requires users' behavior data, which poses considerable privacy risks due to the incorporation of sensitive user information. The unintended disclosure of such data could infringe upon data protection laws and give rise to ethical issues. To mitigate these privacy issues, Federated Learning for Recommendation (Fed4Rec) has emerged as a promising approach. Nevertheless, applying Fed4Rec to LLM-based recommendation presents two main challenges: first, an increase in the imbalance of performance across clients, affecting the system's efficiency over time, and second, a high demand on clients' computational and storage resources for local training and inference of LLMs. To address these challenges, we introduce a Privacy-Preserving LLM-based Recommendation (PPLR) framework. The PPLR framework employs two primary strategies. First, it implements a dynamic balance strategy, which involves the design of dynamic parameter aggregation and adjustment of learning speed for different clients during the training phase, to ensure relatively balanced performance across all clients. Second, PPLR adopts a flexible storage strategy, selectively retaining certain sensitive layers of the language model on the client side while offloading non-sensitive layers to the server. This approach aims to preserve user privacy while efficiently saving computational and storage resources. Experimental results demonstrate that PPLR not only achieves a balanced performance among clients but also enhances overall system performance in a manner that is both computationally and storage-efficient, while effectively protecting user privacy.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recovering the Pre-Fine-Tuning Weights of Generative Models 恢复生成模型的预微调权重

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10208

Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen

引用次数: 0

Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias 受多体物理学启发的电感偏置多激励投射模拟

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10192

Philip A. LeMaitre, Marius Krumm, H. Briegel

{"title":"Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias","authors":"Philip A. LeMaitre, Marius Krumm, H. Briegel","doi":"10.48550/arXiv.2402.10192","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10192","url":null,"abstract":"With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature making it difficult to interpret and understand their decisions. This problem led to the development of the field known as eXplainable Artificial Intelligence (XAI). One method in this field known as Projective Simulation (PS) models a chain-of-thought as a random walk of a particle on a graph with vertices that have concepts attached to them. While this description has various benefits, including the possibility of quantization, it cannot be naturally used to model thoughts that combine several concepts simultaneously. To overcome this limitation, we introduce Multi-Excitation Projective Simulation (mePS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph. A definition for a dynamic hypergraph is put forward to describe the agent's training history along with applications to AI and hypergraph visualization. An inductive bias inspired by the remarkably successful few-body interaction models used in quantum many-body physics is formalized for our classical mePS framework and employed to tackle the exponential complexity associated with naive implementations of hypergraphs. We prove that our inductive bias reduces the complexity from exponential to polynomial, with the exponent representing the cutoff on how many particles can interact. We numerically apply our method to two toy environments and a more complex scenario modelling the diagnosis of a broken computer. These environments demonstrate the resource savings provided by an appropriate choice of inductive bias, as well as showcasing aspects of interpretability. A quantum model for mePS is also briefly outlined and some future directions for it are discussed.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LAPDoc: Layout-Aware Prompting for Documents LAPDoc：文档布局感知提示

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.09841

Marcel Lamott, Yves-Noel Weweler, A. Ulges, Faisal Shafait, Dirk Krechel, Darko Obradovic

{"title":"LAPDoc: Layout-Aware Prompting for Documents","authors":"Marcel Lamott, Yves-Noel Weweler, A. Ulges, Faisal Shafait, Dirk Krechel, Darko Obradovic","doi":"10.48550/arXiv.2402.09841","DOIUrl":"https://doi.org/10.48550/arXiv.2402.09841","url":null,"abstract":"Recent advances in training large language models (LLMs) using massive amounts of solely textual data lead to strong generalization across many domains and tasks, including document-specific tasks. Opposed to that there is a trend to train multi-modal transformer architectures tailored for document understanding that are designed specifically to fuse textual inputs with the corresponding document layout. This involves a separate fine-tuning step for which additional training data is required. At present, no document transformers with comparable generalization to LLMs are available That raises the question which type of model is to be preferred for document understanding tasks. In this paper we investigate the possibility to use purely text-based LLMs for document-specific tasks by using layout enrichment. We explore drop-in modifications and rule-based methods to enrich purely textual LLM prompts with layout information. In our experiments we investigate the effects on the commercial ChatGPT model and the open-source LLM Solar. We demonstrate that using our approach both LLMs show improved performance on various standard document benchmarks. In addition, we study the impact of noisy OCR and layout errors, as well as the limitations of LLMs when it comes to utilizing document layout. Our results indicate that layout enrichment can improve the performance of purely text-based LLMs for document understanding by up to 15% compared to just using plain document text. In conclusion, this approach should be considered for the best model choice between text-based LLM or multi-modal document transformers.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantized Embedding Vectors for Controllable Diffusion Language Models 可控扩散语言模型的量化嵌入向量

ArXiv Pub Date : 2024-02-15 DOI: 10.48550/arXiv.2402.10107

Cheng Kang, Xinye Chen, Yong Hu, Daniel Novak

{"title":"Quantized Embedding Vectors for Controllable Diffusion Language Models","authors":"Cheng Kang, Xinye Chen, Yong Hu, Daniel Novak","doi":"10.48550/arXiv.2402.10107","DOIUrl":"https://doi.org/10.48550/arXiv.2402.10107","url":null,"abstract":"Improving the controllability, portability, and inference speed of diffusion language models (DLMs) is a key challenge in natural language generation. While recent research has shown significant success in complex text generation with language models, the memory and computational power are still very demanding and fall short of expectations, which naturally results in low portability and instability for the models. To mitigate these issues, numerous well-established methods were proposed for neural network quantization. To further enhance their portability of independent deployment as well as improve their stability evaluated by language perplexity, we propose a novel approach called the Quantized Embedding Controllable Diffusion Language Model (QE-CDLM). QE-CDLM builds upon the recent successful controllable DLMs by remodeling the task-specific embedding space via quantization. This leads to a gradient-based controller for the generation tasks, and more stable intermediate latent variables are obtained, which naturally brings in an accelerated convergence as well as better controllability. Additionally, the adaption fine-tuning method is employed to reduce tunable weights. Experimental results on five challenging fine-grained control tasks demonstrate that QE-CDLM compares favorably to existing methods in terms of quality and feasibility, achieving better perplexity and lightweight fine-tuning.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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