OpenRaiser/Intern-Atlas huggingface dataset:
Learning from Exemplary Explanations
Venue: arXiv cs.LG 2023
Year: 2023

eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although the interactivity of XBL promotes model transparency, XBL requires a huge amount of user interaction and can become expensive as feedback is in the form of detailed annotation rather than simple category labelling which is more common in IML. This expense is exacerbated in high stakes domains such as medical image classification. To reduce the effort and expense of XBL we introduce a new approach that uses two input instances and their corresponding Gradient Weighted Class Activation Mapping (GradCAM) model explanations as exemplary explanations to implement XBL. Using a medical image classification task, we demonstrate that, using minimal human input, our approach produces improved explanations (+0.02, +3%) and achieves reduced classification performance (-0.04, -4%) when compared against a model trained without interactions.
AI-Generated Imagery: A New Era for the `Readymade'
Venue: arXiv cs.AI 2023
Year: 2023

While the term `art' defies any concrete definition, this paper aims to examine how digital images produced by generative AI systems, such as Midjourney, have come to be so regularly referred to as such. The discourse around the classification of AI-generated imagery as art is currently somewhat homogeneous, lacking the more nuanced aspects that would apply to more traditional modes of artistic media production. This paper aims to bring important philosophical considerations to the surface of the discussion around AI-generated imagery in the context of art. We employ existing philosophical frameworks and theories of language to suggest that some AI-generated imagery, by virtue of its visual properties within these frameworks, can be presented as `readymades' for consideration as art.
Security in Online Freelance Software Development: A case for Distributed Security Responsibility
Venue: arXiv cs.CR 2023
Year: 2023

Secure software is a cornerstone to safe and resilient digital ecosystems. It offers strong foundation to protect users' sensitive data and guard against cyber-threats. The rapidly increasing landscape of digital economy has encouraged developers from different socio-technical and socio-economic backgrounds to join online freelance marketplaces. While, secure software practices facilitate software developers in developing secure software, there is paucity of research on how freelance developers adhere to security practices and how they can be facilitated to improve their security behavior in under-resourced environments. Moreover, freelance developers are often held responsible for producing insecure code. In this position paper, we review existing literature and argue for the case of distributed security responsibilities in online freelance environment. We propose a research agenda aimed at offering an organized and systematic effort by researchers to address security needs and challenges of online freelance marketplaces. These include: characterising software security and defining separation of responsibilities, building trust in online freelance development communities, leveraging the potential of online freelancing platforms in the promotion of secure software development and building adaptive security interventions for online freelance software development. The research has the potential to bring forth existing security solutions to wider developer community and deliver substantial benefits to the broader security ecosystem.
Better bounds on the minimal Lee distance
Venue: arXiv cs.IT 2023
Year: 2023

This paper provides new and improved Singleton-like bounds for Lee metric codes over integer residue rings. We derive the bounds using various novel definitions of generalized Lee weights based on different notions of a support of a linear code. In this regard, we introduce three main different support types for codes in the Lee metric and analyze their utility to derive bounds on the minimum Lee distance. Eventually, we propose a new point of view to generalized weights and give an improved bound on the minimum distance of codes in the Lee metric for which we discuss the density of maximum Lee distance codes with respect to this novel Singleton-like bound.
AICT: An Adaptive Image Compression Transformer
Venue: arXiv cs.CV 2023
Year: 2023

Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT). Current methods that still rely on ConvNet-based entropy coding are limited in long-range modeling dependencies due to their local connectivity and an increasing number of architectural biases and priors. On the contrary, the proposed ICT can capture both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Further, we leverage a learnable scaling module with a sandwich ConvNeXt-based pre/post-processor to accurately extract more compact latent representation while reconstructing higher-quality images. Extensive experimental results on benchmark datasets showed that the proposed adaptive image compression transformer (AICT) framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural codec SwinT-ChARM.
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomness and spatial-correlations are captured by learning the drift and diffusion terms of the stochastic differential equation with a Gumble matrix embedding, respectively. In particular, this novel framework enables us to investigate the implicit regularization effect of the noise terms in S-GGNs. We provide a theoretical guarantee for the proposed S-GGNs by deriving the difference between the two corresponding loss functions in a small neighborhood of weight. Then, we employ Kuramoto's model to generate data for comparing the spectral density from the Hessian Matrix of the two loss functions. Experimental results on real-world data, demonstrate that S-GGNs exhibit superior convergence, robustness, and generalization, compared with state-of-the-arts.
Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platform is needed to accelerate research and let practitioners focus on the core problems. To this end, we present Agilicious, a co-designed hardware and software framework tailored to autonomous, agile quadrotor flight. It is completely open-source and open-hardware and supports both model-based and neural-network--based controllers. Also, it provides high thrust-to-weight and torque-to-inertia ratios for agility, onboard vision sensors, GPU-accelerated compute hardware for real-time perception and neural-network inference, a real-time flight controller, and a versatile software stack. In contrast to existing frameworks, Agilicious offers a unique combination of flexible software stack and high-performance hardware. We compare Agilicious with prior works and demonstrate it on different agile tasks, using both model-based and neural-network--based controllers. Our demonstrators include trajectory tracking at up to 5g and 70 km/h in a motion-capture system, and vision-based acrobatic flight and obstacle avoidance in both structured and unstructured environments using solely onboard perception. Finally, we demonstrate its use for hardware-in-the-loop simulation in virtual-reality environments. Thanks to its versatility, we believe that Agilicious supports the next generation of scientific and industrial quadrotor research.
Fast Decoding of Lifted Interleaved Linearized Reed-Solomon Codes for Multishot Network Coding
Venue: arXiv cs.IT 2023
Year: 2023

Mart{\'ı}nez-Pe{ñ}as and Kschischang (IEEE Trans.\ Inf.\ Theory, 2019) proposed lifted linearized Reed--Solomon codes as suitable codes for error control in multishot network coding. We show how to construct and decode \ac{LILRS} codes. Compared to the construction by Mart{\'ı}nez-Pe{ñ}as--Kschischang, interleaving allows to increase the decoding region significantly and decreases the overhead due to the lifting (i.e., increases the code rate), at the cost of an increased packet size. We propose two decoding schemes for \ac{LILRS} that are both capable of correcting insertions and deletions beyond half the minimum distance of the code by either allowing a list or a small decoding failure probability. We propose a probabilistic unique {\LOlike} decoder for \ac{LILRS} codes and an efficient interpolation-based decoding scheme that can be either used as a list decoder (with exponential worst-case list size) or as a probabilistic unique decoder. We derive upper bounds on the decoding failure probability of the probabilistic-unique decoders which show that the decoding failure probability is very small for most channel realizations up to the maximal decoding radius. The tightness of the bounds is verified by Monte Carlo simulations.
SparqLog: A System for Efficient Evaluation of SPARQL 1.1 Queries via Datalog [Experiment, Analysis and Benchmark]
Venue: arXiv cs.DB 2023
Year: 2023

Over the past decade, Knowledge Graphs have received enormous interest both from industry and from academia. Research in this area has been driven, above all, by the Database (DB) community and the Semantic Web (SW) community. However, there still remains a certain divide between approaches coming from these two communities. For instance, while languages such as SQL or Datalog are widely used in the DB area, a different set of languages such as SPARQL and OWL is used in the SW area. Interoperability between such technologies is still a challenge. The goal of this work is to present a uniform and consistent framework meeting important requirements from both, the SW and DB field.
Evaluating DNS Resiliency and Responsiveness with Truncation, Fragmentation & DoTCP Fallback
Venue: arXiv cs.NI 2023
Year: 2023

Since its introduction in 1987, the DNS has become one of the core components of the Internet. While it was designed to work with both TCP and UDP, DNS-over-UDP (DoUDP) has become the default option due to its low overhead. As new Resource Records were introduced, the sizes of DNS responses increased considerably. This expansion of message body has led to truncation and IP fragmentation more often in recent years where large UDP responses make DNS an easy vector for amplifying denial-of-service attacks which can reduce the resiliency of DNS services. This paper investigates the resiliency, responsiveness, and usage of DoTCP and DoUDP over IPv4 and IPv6 for 10 widely used public DNS resolvers. In these experiments, these aspects are investigated from the edge and from the core of the Internet to represent the communication of the resolvers with DNS clients and authoritative name servers. Overall, more than 14M individual measurements from 2527 RIPE Atlas Probes have been analyzed, highlighting that most resolvers show similar resiliency for both DoTCP and DoUDP. While DNS Flag Day 2020 recommended 1232 bytes of buffer sizes yet we find out that 3 out of 10 resolvers mainly announce very large EDNS(0) buffer sizes both from the edge as well as from the core, which potentially causes fragmentation. In reaction to large response sizes from authoritative name servers, we find that resolvers do not fall back to the usage of DoTCP in many cases, bearing the risk of fragmented responses. As the message sizes in the DNS are expected to grow further, this problem will become more urgent in the future.
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Venue: arXiv cs.LG 2023
Year: 2023

Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks. However, original PINNs often suffer from bottlenecks, such as low accuracy and non-convergence, limiting their applicability in complex physical contexts. To alleviate these issues, we proposed auxiliary-task learning-based physics-informed neural networks (ATL-PINNs), which provide four different auxiliary-task learning modes and investigate their performance compared with original PINNs. We also employ the gradient cosine similarity algorithm to integrate auxiliary problem loss with the primary problem loss in ATL-PINNs, which aims to enhance the effectiveness of the auxiliary-task learning modes. To the best of our knowledge, this is the first study to introduce auxiliary-task learning modes in the context of physics-informed learning. We conduct experiments on three PDE problems across different fields and scenarios. Our findings demonstrate that the proposed auxiliary-task learning modes can significantly improve solution accuracy, achieving a maximum performance boost of 96.62% (averaging 28.23%) compared to the original single-task PINNs. The code and dataset are open source at https://github.com/junjun-yan/ATL-PINN.
Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches
Venue: arXiv cs.CL 2023
Year: 2023

Poetry holds immense significance within the cultural and traditional fabric of any nation. It serves as a vehicle for poets to articulate their emotions, preserve customs, and convey the essence of their culture. Arabic poetry is no exception, having played a cherished role in the heritage of the Arabic community throughout history and maintaining its relevance in the present era. Typically, comprehending Arabic poetry necessitates the expertise of a linguist who can analyze its content and assess its quality. This paper presents the introduction of a framework called \textit{Ashaar} https://github.com/ARBML/Ashaar, which encompasses a collection of datasets and pre-trained models designed specifically for the analysis and generation of Arabic poetry. The pipeline established within our proposed approach encompasses various aspects of poetry, such as meter, theme, and era classification. It also incorporates automatic poetry diacritization, enabling more intricate analyses like automated extraction of the \textit{Arudi} style. Additionally, we explore the feasibility of generating conditional poetry through the pre-training of a character-based GPT model. Furthermore, as part of this endeavor, we provide four datasets: one for poetry generation, another for diacritization, and two for Arudi-style prediction. These datasets aim to facilitate research and development in the field of Arabic poetry by enabling researchers and enthusiasts to delve into the nuances of this rich literary tradition.
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights
Venue: arXiv cs.LG 2023
Year: 2023

The future of machine learning lies in moving data collection along with training to the edge. Federated Learning, for short FL, has been recently proposed to achieve this goal. The principle of this approach is to aggregate models learned over a large number of distributed clients, i.e., resource-constrained mobile devices that collect data from their environment, to obtain a new more general model. The latter is subsequently redistributed to clients for further training. A key feature that distinguishes federated learning from data-center-based distributed training is the inherent heterogeneity. In this work, we introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneity in federated optimization, in terms of both heterogeneous data and local updates. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Semi-supervised learning has become increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional information. However, most existing semi-supervised segmentation methods only focus on extracting information from unlabeled data, disregarding the potential of labeled data to further improve the performance of the model. In this paper, we propose a novel Correlation Aware Mutual Learning (CAML) framework that leverages labeled data to guide the extraction of information from unlabeled data. Our approach is based on a mutual learning strategy that incorporates two modules: the Cross-sample Mutual Attention Module (CMA) and the Omni-Correlation Consistency Module (OCC). The CMA module establishes dense cross-sample correlations among a group of samples, enabling the transfer of label prior knowledge to unlabeled data. The OCC module constructs omni-correlations between the unlabeled and labeled datasets and regularizes dual models by constraining the omni-correlation matrix of each sub-model to be consistent. Experiments on the Atrial Segmentation Challenge dataset demonstrate that our proposed approach outperforms state-of-the-art methods, highlighting the effectiveness of our framework in medical image segmentation tasks. The codes, pre-trained weights, and data are publicly available.
Deploying ZKP Frameworks with Real-World Data: Challenges and Proposed Solutions
Venue: arXiv cs.CR 2023
Year: 2023

Zero-knowledge proof (ZKP) frameworks have the potential to revolutionize the handling of sensitive data in various domains. However, deploying ZKP frameworks with real-world data presents several challenges, including scalability, usability, and interoperability. In this project, we present Fact Fortress, an end-to-end framework for designing and deploying zero-knowledge proofs of general statements. Our solution leverages proofs of data provenance and auditable data access policies to ensure the trustworthiness of how sensitive data is handled and provide assurance of the computations that have been performed on it. ZKP is mostly associated with blockchain technology, where it enhances transaction privacy and scalability through rollups, addressing the data inherent to the blockchain. Our approach focuses on safeguarding the privacy of data external to the blockchain, with the blockchain serving as publicly auditable infrastructure to verify the validity of ZK proofs and track how data access has been granted without revealing the data itself. Additionally, our framework provides high-level abstractions that enable developers to express complex computations without worrying about the underlying arithmetic circuits and facilitates the deployment of on-chain verifiers. Although our approach demonstrated fair scalability for large datasets, there is still room for improvement, and further work is needed to enhance its scalability. By enabling on-chain verification of computation and data provenance without revealing any information about the data itself, our solution ensures the integrity of the computations on the data while preserving its privacy.
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs can be used to scale biomedical knowledge curation. We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access. We conduct a case study on adverse drug event (ADE) extraction, which is an important area for improving care. On standard ADE extraction evaluation, a GPT-3.5 distilled PubMedBERT model attained comparable accuracy as supervised state-of-the-art models without using any labeled data. Despite being over 1,000 times smaller, the distilled model outperformed its teacher GPT-3.5 by over 6 absolute points in F1 and GPT-4 by over 5 absolute points. Ablation studies on distillation model choice (e.g., PubMedBERT vs BioGPT) and ADE extraction architecture shed light on best practice for biomedical knowledge extraction. Similar gains were attained by distillation for other standard biomedical knowledge extraction tasks such as gene-disease associations and protected health information, further illustrating the promise of this approach.
Discovering Image Usage Online: A Case Study With "Flatten the Curve''
Venue: arXiv cs.SI 2023
Year: 2023

Understanding the spread of images across the web helps us understand the reuse of scientific visualizations and their relationship with the public. The "Flatten the Curve" graphic was heavily used during the COVID-19 pandemic to convey a complex concept in a simple form. It displays two curves comparing the impact on case loads for medical facilities if the populace either adopts or fails to adopt protective measures during a pandemic. We use five variants of the "Flatten the Curve" image as a case study for viewing the spread of an image online. To evaluate its spread, we leverage three information channels: reverse image search engines, social media, and web archives. Reverse image searches give us a current view into image reuse. Social media helps us understand a variant's popularity over time. Web archives help us see when it was preserved, highlighting a view of popularity for future researchers. Our case study leverages document URLs can be used as a proxy for images when studying the spread of images online.
Virtual Physical Coupling of Two Lower-Limb Exoskeletons
Venue: arXiv cs.RO 2023
Year: 2023

Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals' lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users' kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation.