OpenRaiser/Intern-Atlas huggingface dataset:
Bridging Research and Readers: A Multi-Modal Automated Academic Papers Interpretation System
Venue: arXiv cs.CL 2024
Year: 2024

In the contemporary information era, significantly accelerated by the advent of Large-scale Language Models, the proliferation of scientific literature is reaching unprecedented levels. Researchers urgently require efficient tools for reading and summarizing academic papers, uncovering significant scientific literature, and employing diverse interpretative methodologies. To address this burgeoning demand, the role of automated scientific literature interpretation systems has become paramount. However, prevailing models, both commercial and open-source, confront notable challenges: they often overlook multimodal data, grapple with summarizing over-length texts, and lack diverse user interfaces. In response, we introduce an open-source multi-modal automated academic paper interpretation system (MMAPIS) with three-step process stages, incorporating LLMs to augment its functionality. Our system first employs the hybrid modality preprocessing and alignment module to extract plain text, and tables or figures from documents separately. It then aligns this information based on the section names they belong to, ensuring that data with identical section names are categorized under the same section. Following this, we introduce a hierarchical discourse-aware summarization method. It utilizes the extracted section names to divide the article into shorter text segments, facilitating specific summarizations both within and between sections via LLMs with specific prompts. Finally, we have designed four types of diversified user interfaces, including paper recommendation, multimodal Q\&A, audio broadcasting, and interpretation blog, which can be widely applied across various scenarios. Our qualitative and quantitative evaluations underscore the system's superiority, especially in scientific summarization, where it outperforms solutions relying solely on GPT-4.
Named Service Networking as a primer for the Metaverse
Venue: arXiv cs.NI 2024
Year: 2024

Ubiquitous extended reality environments such as the Metaverse will have a significant impact on the Internet, which will evolve to interconnect a large number of mixed reality spaces. Currently, Metaverse development is related to the creation of mixed reality environments, not tackling the required networking functionalities. This article analyzes suitable networking design choices to support the Metaverse, proposing a new service-centric networking approach capable of incorporating low-latency data fetching, distributed computing, and fusion of heterogeneous data types over the Cloud-to-Thing continuum.
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints
Venue: arXiv cs.CL 2024
Year: 2024

The progress introduced by pre-trained language models and their fine-tuning has resulted in significant improvements in most downstream NLP tasks. The unsupervised training of a language model combined with further target task fine-tuning has become the standard QA fine-tuning procedure. In this work, we demonstrate that this strategy is sub-optimal for fine-tuning QA models, especially under a low QA annotation budget, which is a usual setting in practice due to the extractive QA labeling cost. We draw our conclusions by conducting an exhaustive analysis of the performance of the alternatives of the sequential fine-tuning strategy on different QA datasets. Based on the experiments performed, we observed that the best strategy to fine-tune the QA model in low-budget settings is taking a pre-trained language model (PLM) and then fine-tuning PLM with a dataset composed of the target dataset and SQuAD dataset. With zero extra annotation effort, the best strategy outperforms the standard strategy by 2.28% to 6.48%. Our experiments provide one of the first investigations on how to best fine-tune a QA system under a low budget and are therefore of the utmost practical interest to the QA practitioners.
Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer
Venue: arXiv cs.LG 2024
Year: 2024

Multimodal Continual Instruction Tuning (MCIT) enables Multimodal Large Language Models (MLLMs) to meet continuously emerging requirements without expensive retraining. MCIT faces two major obstacles: catastrophic forgetting (where old knowledge is forgotten) and negative forward transfer (where the performance of future tasks is degraded). Although existing methods have greatly alleviated catastrophic forgetting, they still suffer from negative forward transfer. We discover a large discrepancy in different input embeddings by performing singular value decomposition (SVD) on input embeddings. This discrepancy results in the model learning irrelevant information for old and pre-trained tasks, leading to catastrophic forgetting and negative forward transfer. To address these issues, we propose Prompt Tuning with Positive Forward Transfer (Fwd-Prompt), a prompt-based method that projects the prompt gradient to the residual space to minimize interference between tasks and to the pre-trained subspace for reusing pre-trained knowledge. Our experiments demonstrate that Fwd-Prompt achieves state-of-the-art performance while updating fewer parameters and requiring no old samples. Our research illuminates the potential of continuously adapting MLLMs to new tasks under the instruction tuning paradigm and encourages future studies to explore MCIT.
The Mikado Filesystem: An experimental RPC filesystem running over gRPC
Venue: arXiv cs.NI 2024
Year: 2024

Computer applications seeking to persist files remotely across the Internet are faced with a bewildering choice of mechanisms which tend to boil down to monolithic proprietary closed-source Vendor solutions. We introduce The Mikado Filesystem (mikfs), which provides an open simple lightweight interoperable portable extensible remote filesystem that is open source. mikfs consists of client applications accessing remote servers via RPC running over TCP/IP connections. mikfs is defined as a concrete set of API method calls over gRPC expressed in Google's Protocol Buffers' IDL. gRPC supports a wide variety of programming languages & platforms. For a given language + platform, the gRPC toolset can generate client- & server-side stubs from the IDL callable from client & server code in the selected languages, e.g., a client written in C# or java running on a Windows PC can access a server written in C++ running on Linux. mikfs consists of a virtual hierarchical tree of files & directories. This logical filesystem is not constrained to the limits and file naming conventions of the host's own physical native filesystem. API methods are provided for authentication; for atomic file-level operations on files & directories; for clients to register to receive notifications of file & directory changes on a server. The public API allows developers to write their own new servers and clients; allowing migration of hosted files between different implementations; extension with new methods & features; is Open Source code available for inspection and adaptation. gRPC provides secure authenticated connection & communication over HTTP/2; End-to-End Privacy & Security against eavesdropping of data in transit; support for multiple alternate user login mechanisms. mikfs is provided as source code, 'The Bootstrap Distribution', consisting of an ecosystem of clients, servers, tools and utilities.
Cross-Domain AI for Early Attack Detection and Defense Against Malicious Flows in O-RAN
Venue: arXiv cs.CR 2024
Year: 2024

Only the chairs can edit In the fight against cyber attacks, Network Softwarization (NS) is a flexible and adaptable shield, using advanced software to spot malicious activity in regular network traffic. However, the availability of comprehensive datasets for mobile networks, which are fundamental for the development of Machine Learning (ML) solutions for attack detection near their source, is still limited. Cross-Domain Artificial Intelligence (AI) can be the key to address this, although its application in Open Radio Access Network (O-RAN) is still at its infancy. To address these challenges, we deployed an end-to-end O-RAN network, that was used to collect data from the RAN and the transport network. These datasets allow us to combine the knowledge from an in-network ML traffic classifier for attack detection to bolster the training of an ML-based traffic classifier specifically tailored for the RAN. Our results demonstrate the potential of the proposed approach, achieving an accuracy rate of 93%. This approach not only bridges critical gaps in mobile network security but also showcases the potential of cross-domain AI in enhancing the efficacy of network security measures.
MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Demystifying interactions between temporal patterns of different scales is fundamental to precise long-range time series forecasting. However, previous works lack the ability to model high-order interactions. To promote more comprehensive pattern interaction modeling for long-range time series forecasting, we propose a Multi-Scale Hypergraph Transformer (MSHyper) framework. Specifically, a multi-scale hypergraph is introduced to provide foundations for modeling high-order pattern interactions. Then by treating hyperedges as nodes, we also build a hyperedge graph to enhance hypergraph modeling. In addition, a tri-stage message passing mechanism is introduced to aggregate pattern information and learn the interaction strength between temporal patterns of different scales. Extensive experiments on five real-world datasets demonstrate that MSHyper achieves state-of-the-art (SOTA) performance across various settings.
Hot Fixing Software: A Comprehensive Review of Terminology, Techniques, and Applications
Venue: arXiv cs.SE 2024
Year: 2024

A hot fix is an unplanned improvement to a specific time-critical issue deployed to a software system in production. While hot fixing is an essential and common activity in software maintenance, it has never been surveyed as a research activity. Thus, such a review is long overdue. In this paper, we conduct a comprehensive literature review of work on hot fixing. We highlight the fields where this topic has been addressed, inconsistencies we identified in the terminology, gaps in the literature, and directions for future work. Our search concluded with 91 articles on the topic between the years 2000 and 2022. The articles found encompass many different research areas such as log analysis, runtime patching (also known as hot patching), and automated repair, as well as various application domains such as security, mobile, and video games. We find that many directions can take hot fix research forward such as unifying existing terminology, establishing a benchmark set of hot fixes, researching costs and frequency of hot fixes, and researching the possibility of end-to-end automation of detection, mitigation, and deployment. We discuss these avenues in detail to inspire the community to systematize hot fixing as a software engineering activity.
A Fast Control Plane for a Large-Scale and High-Speed Optical Circuit Switch System
Venue: arXiv cs.NI 2024
Year: 2024

We experimentally verify a fast control plane with 100 microseconds of configuration time that can support more than 1000 racks, leveraged by a software-defined network controller and an industrial real-time Ethernet standard EtherCAT.
Enhancing Rural Agricultural Value Chains through Electric Mobility Services in Ethiopia
Venue: arXiv cs.SE 2024
Year: 2024

Transportation is a constitutional part of most supply and value chains in modern economies. Smallholder farmers in rural Ethiopia face severe challenges along their supply and value chains. In particular, suitable, affordable, and available transport services are in high demand. To develop context-specific technical solutions, a problem-to-solution methodology based on the interaction with technology is developed. With this approach, we fill the gap between proven transportation assessment frameworks and general user-centered techniques. Central to our approach is an electric test vehicle that is implemented in rural supply and value chains for research, development, and testing. Based on our objective and the derived methodological requirements, a set of existing methods is selected. Local partners are integrated in an organizational framework that executes major parts of this research endeavour in Arsi Zone, Oromia Region, Ethiopia.
Foundations of Vector Retrieval
Venue: arXiv cs.DS 2024
Year: 2024

Vectors are universal mathematical objects that can represent text, images, speech, or a mix of these data modalities. That happens regardless of whether data is represented by hand-crafted features or learnt embeddings. Collect a large enough quantity of such vectors and the question of retrieval becomes urgently relevant: Finding vectors that are more similar to a query vector. This monograph is concerned with the question above and covers fundamental concepts along with advanced data structures and algorithms for vector retrieval. In doing so, it recaps this fascinating topic and lowers barriers of entry into this rich area of research.
Detection of Distributed Denial of Service Attacks Carried Out by Botnets in Software-Defined Networks
Venue: arXiv cs.NI 2024
Year: 2024

Recent years witnessed a surge in network traffic due to the emergence of new online services, causing periodic saturation and complexity problems. Additionally, the growing number of IoT devices further compounds the problem. Software Defined Network (SDN) is a new architecture which offers innovative advantages that help to reduce saturation problems. Despite its benefits, SDNs not only can be affected by traditional attacks but also introduce new security challenges. In this context, Distributed Denial of Service (DDoS) is one of the most important attacks that can damage an SDN network's normal operation. Furthermore, if these attacks are executed using botnets, they can use thousands of compromised devices to disrupt critical online services. This paper proposes a framework for detecting DDoS attacks generated by a group of botnets in an SDN network. The framework is implemented using open-source tools such as Mininet and OpenDaylight and tested in a centralized network topology using BYOB and SNORT. The results demonstrate real-time attack identification by implementing an intrusion detection mechanism in the victim client. Our proposed solution offers quick and effective detection of DDoS attacks in SDN networks. The framework can successfully differentiate the type of attack with high accuracy in a short time
LRSCwait: Enabling Scalable and Efficient Synchronization in Manycore Systems through Polling-Free and Retry-Free Operation
Venue: arXiv cs.AR 2024
Year: 2024

Extensive polling in shared-memory manycore systems can lead to contention, decreased throughput, and poor energy efficiency. Both lock implementations and the general-purpose atomic operation, load-reserved/store-conditional (LRSC), cause polling due to serialization and retries. To alleviate this overhead, we propose LRwait and SCwait, a synchronization pair that eliminates polling by allowing contending cores to sleep while waiting for previous cores to finish their atomic access. As a scalable implementation of LRwait, we present Colibri, a distributed and scalable approach to managing LRwait reservations. Through extensive benchmarking on an open-source RISC-V platform with 256 cores, we demonstrate that Colibri outperforms current synchronization approaches for various concurrent algorithms with high and low contention regarding throughput, fairness, and energy efficiency. With an area overhead of only 6%, Colibri outperforms LRSC-based implementations by a factor of 6.5x in terms of throughput and 7.1x in terms of energy efficiency.
Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm
Venue: arXiv cs.LG 2024
Year: 2024

In the industrial practice of machine learning and statistical modeling, practitioners often work under the assumption of accessible, static, labeled data for evaluation and training. However, this assumption often deviates from reality, where data may be private, encrypted, difficult-to-measure, or unlabeled. In this paper, we bridge this gap by adapting the Hui-Walter paradigm, a method traditionally applied in epidemiology and medicine, to the field of machine learning. This approach enables us to estimate key performance metrics such as false positive rate, false negative rate, and priors in scenarios where no ground truth is available. We further extend this paradigm for handling online data, opening up new possibilities for dynamic data environments. Our methodology involves partitioning data into latent classes to simulate multiple data populations (if natural populations are unavailable) and independently training models to replicate multiple tests. By cross-tabulating binary outcomes across multiple categorizers and multiple populations, we are able to estimate unknown parameters through Gibbs sampling, eliminating the need for ground-truth or labeled data. This paper showcases the potential of our methodology to transform machine learning practices by allowing for accurate model assessment under dynamic and uncertain data conditions.
Floating Point HUB Adder for RISC-V Sargantana Processor
Venue: arXiv cs.AR 2024
Year: 2024

HUB format is an emerging technique to improve the hardware and time requirement when round to nearest is needed. On the other hand, RISC-V is an open-source ISA that many companies currently use in their designs. This paper presents a tailored floating point HUB adder implemented in the Sargantana RISC-V processor.
Weak Memory Demands Model-based Compiler Testing
Venue: arXiv cs.PL 2024
Year: 2024

A compiler bug arises if the behaviour of a compiled concurrent program, as allowed by its architecture memory model, is not a behaviour permitted by the source program under its source model. One might reasonably think that most compiler bugs have been found in the decade since the introduction of the C/C++ memory model. We observe that processor implementations are increasingly exploiting the behaviour of relaxed architecture models. As such, compiled programs may exhibit bugs not seen on older hardware. To account for this we require model-based compiler testing. While this observation is not surprising, its implications are broad. Compilers and their testing tools will need to be updated to follow hardware relaxations, concurrent test generators will need to be improved, and assumptions of prior work will need revisiting. We explore these ideas using a compiler toolchain bug we reported in LLVM.
Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs
Venue: arXiv cs.CV 2024
Year: 2024

The integration of machine learning in medicine has significantly improved diagnostic precision, particularly in the interpretation of complex structures like the human brain. Diagnosing challenging conditions such as Alzheimer's disease has prompted the development of brain age estimation techniques. These methods often leverage three-dimensional Magnetic Resonance Imaging (MRI) scans, with recent studies emphasizing the efficacy of 3D convolutional neural networks (CNNs) like 3D ResNet. However, the untapped potential of Vision Transformers (ViTs), known for their accuracy and interpretability, persists in this domain due to limitations in their 3D versions. This paper introduces Triamese-ViT, an innovative adaptation of the ViT model for brain age estimation. Our model uniquely combines ViTs from three different orientations to capture 3D information, significantly enhancing accuracy and interpretability. Tested on a dataset of 1351 MRI scans, Triamese-ViT achieves a Mean Absolute Error (MAE) of 3.84, a 0.9 Spearman correlation coefficient with chronological age, and a -0.29 Spearman correlation coefficient between the brain age gap (BAG) and chronological age, significantly better than previous methods for brian age estimation. A key innovation of Triamese-ViT is its capacity to generate a comprehensive 3D-like attention map, synthesized from 2D attention maps of each orientation-specific ViT. This feature is particularly beneficial for in-depth brain age analysis and disease diagnosis, offering deeper insights into brain health and the mechanisms of age-related neural changes.
Technical Report: On the Convergence of Gossip Learning in the Presence of Node Inaccessibility
Venue: arXiv cs.LG 2024
Year: 2024

Gossip learning (GL), as a decentralized alternative to federated learning (FL), is more suitable for resource-constrained wireless networks, such as Flying Ad-Hoc Networks (FANETs) that are formed by unmanned aerial vehicles (UAVs). GL can significantly enhance the efficiency and extend the battery life of UAV networks. Despite the advantages, the performance of GL is strongly affected by data distribution, communication speed, and network connectivity. However, how these factors influence the GL convergence is still unclear. Existing work studied the convergence of GL based on a virtual quantity for the sake of convenience, which failed to reflect the real state of the network when some nodes are inaccessible. In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology. We first decompose the weight divergence by whether the node is accessible or not. Then, we investigate the GL convergence under the dynamic of node accessibility and theoretically provide how the number of inaccessible nodes, data non-i.i.d.-ness, and duration of inaccessibility affect the convergence. Extensive experiments are carried out in practical settings to comprehensively verify the correctness of our theoretical findings.
Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.