OpenRaiser/Intern-Atlas huggingface dataset:
Low-cost singular value decomposition with optimal sensor placement
Venue: arXiv cs.CE 2023
Year: 2023

This paper presents a new method capable of reconstructing datasets with great precision and very low computational cost using a novel variant of the singular value decomposition (SVD) algorithm that has been named low-cost SVD (lcSVD). This algorithm allows to reconstruct a dataset from a minimum amount of points, that can be selected randomly, equidistantly or can be calculated using the optimal sensor placement functionality that is also presented in this paper, which finds minimizing the reconstruction error to validate the calculated sensor positions. This method also allows to find the optimal number of sensors, aiding users in optimizing experimental data recollection. The method is tested in a series of datasets, which vary between experimental and numerical simulations, two- and three-dimensional data and laminar and turbulent flow, which have been used to demonstrate the capacity of this method based on its high reconstruction accuracy, robustness, and computational resource optimization. Maximum speed-up factors of 630 and memory reduction of 37% are found when compared to the application of standard SVD to the dataset. This method will be incorporated into ModelFLOWs-app's next version release.
Towards Formal Fault Injection for Safety Assessment of Automated Systems
Venue: arXiv cs.AI 2023
Year: 2023

Reasoning about safety, security, and other dependability attributes of autonomous systems is a challenge that needs to be addressed before the adoption of such systems in day-to-day life. Formal methods is a class of methods that mathematically reason about a system's behavior. Thus, a correctness proof is sufficient to conclude the system's dependability. However, these methods are usually applied to abstract models of the system, which might not fully represent the actual system. Fault injection, on the other hand, is a testing method to evaluate the dependability of systems. However, the amount of testing required to evaluate the system is rather large and often a problem. This vision paper introduces formal fault injection, a fusion of these two techniques throughout the development lifecycle to enhance the dependability of autonomous systems. We advocate for a more cohesive approach by identifying five areas of mutual support between formal methods and fault injection. By forging stronger ties between the two fields, we pave the way for developing safe and dependable autonomous systems. This paper delves into the integration's potential and outlines future research avenues, addressing open challenges along the way.
PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
Venue: arXiv cs.CV 2023
Year: 2023

In surgical procedures, correct instrument counting is essential. Instance segmentation is a location method that locates not only an object's bounding box but also each pixel's specific details. However, obtaining mask-level annotations is labor-intensive in instance segmentation. To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg). PWISeg adopts an FCN-based architecture with point-to-box and point-to-mask branches to model the relationships between feature points and bounding boxes, as well as feature points and segmentation masks on FPN, accomplishing instrument detection and segmentation jointly in a single model. Since mask level annotations are hard to available in the real world, for point-to-mask training, we introduce an unsupervised projection loss, utilizing the projected relation between predicted masks and bboxes as supervision signal. On the other hand, we annotate a few pixels as the key pixel for each instrument. Based on this, we further propose a key pixel association loss and a key pixel distribution loss, driving the point-to-mask branch to generate more accurate segmentation predictions. To comprehensively evaluate this task, we unveil a novel surgical instrument dataset with manual annotations, setting up a benchmark for further research. Our comprehensive research trial validated the superior performance of our PWISeg. The results show that the accuracy of surgical instrument segmentation is improved, surpassing most methods of instance segmentation via weakly supervised bounding boxes. This improvement is consistently observed in our proposed dataset and when applied to the public HOSPI-Tools dataset.
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
Venue: arXiv cs.CV 2023
Year: 2023

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability.
SurgPLAN: Surgical Phase Localization Network for Phase Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Surgical phase recognition is crucial to providing surgery understanding in smart operating rooms. Despite great progress in automatic surgical phase recognition, most existing methods are still restricted by two problems. First, these methods cannot capture discriminative visual features for each frame and motion information with simple 2D networks. Second, the frame-by-frame recognition paradigm degrades the performance due to unstable predictions within each phase, termed as phase shaking. To address these two challenges, we propose a Surgical Phase LocAlization Network, named SurgPLAN, to facilitate a more accurate and stable surgical phase recognition with the principle of temporal detection. Specifically, we first devise a Pyramid SlowFast (PSF) architecture to serve as the visual backbone to capture multi-scale spatial and temporal features by two branches with different frame sampling rates. Moreover, we propose a Temporal Phase Localization (TPL) module to generate the phase prediction based on temporal region proposals, which ensures accurate and consistent predictions within each surgical phase. Extensive experiments confirm the significant advantages of our SurgPLAN over frame-by-frame approaches in terms of both accuracy and stability.
A Framework for Modeling, Analyzing, and Decision-Making in Disease Spread Dynamics and Medicine/Vaccine Distribution
Venue: arXiv cs.MA 2023
Year: 2023

The challenges posed by epidemics and pandemics are immense, especially if the causes are novel. This article introduces a versatile open-source simulation framework designed to model intricate dynamics of infectious diseases across diverse population centres. Taking inspiration from historical precedents such as the Spanish flu and COVID-19, and geographical economic theories such as Central place theory, the simulation integrates agent-based modelling to depict the movement and interactions of individuals within different settlement hierarchies. Additionally, the framework provides a tool for decision-makers to assess and strategize optimal distribution plans for limited resources like vaccines or cures as well as to impose mobility restrictions.
Towards Flexibility and Robustness of LSM Trees
Venue: arXiv cs.DB 2023
Year: 2023

Log-Structured Merge trees (LSM trees) are increasingly used as part of the storage engine behind several data systems, and are frequently deployed in the cloud. As the number of applications relying on LSM-based storage backends increases, the problem of performance tuning of LSM trees receives increasing attention. We consider both nominal tunings - where workload and execution environment are accurately known a priori - and robust tunings - which consider uncertainty in the workload knowledge. This type of workload uncertainty is common in modern applications, notably in shared infrastructure environments like the public cloud. To address this problem, we introduce ENDURE, a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policy, size ratio, and memory allocation on the overall performance. ENDURE considers a robust formulation of the throughput maximization problem and recommends a tuning that offers near-optimal throughput when the executed workload is not the same, instead in a neighborhood of the expected workload. Additionally, we explore the robustness of flexible LSM designs by proposing a new unified design called K-LSM that encompasses existing designs. We deploy our robust tuning system, ENDURE, on a state-of-the-art key-value store, RocksDB, and demonstrate throughput improvements of up to 5x in the presence of uncertainty. Our results indicate that the tunings obtained by ENDURE are more robust than tunings obtained under our expanded LSM design space. This indicates that robustness may not be inherent to a design, instead, it is an outcome of a tuning process that explicitly accounts for uncertainty.
Software Dependability Measurement at the Age Of 36
Venue: arXiv cs.SE 2023
Year: 2023

Thirty-six years after the first edition of IEEE standard 982.1, Measures of the Software Aspects of Dependability, the third edition focuses on the measurement of in-service software dependability. This article explains how this new point of view evolved and shaped the third edition's guidance for software dependability measurement.
Interpretable Reinforcement Learning for Robotics and Continuous Control
Venue: arXiv cs.RO 2023
Year: 2023

Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches in reinforcement learning have achieved tremendous success in learning policies for continuous control problems such as robotics and autonomous driving, the lack of interpretability is a fundamental barrier to adoption. We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, reinforcement learning approaches to produce high-performing, interpretable policies. The key to our approach is a procedure for allowing direct optimization in a sparse decision-tree-like representation. We validate ICCTs against baselines across six domains, showing that ICCTs are capable of learning policies that parity or outperform baselines by up to 33% in autonomous driving scenarios while achieving a 300x-600x reduction in the number of parameters against deep learning baselines. We prove that ICCTs can serve as universal function approximators and display analytically that ICCTs can be verified in linear time. Furthermore, we deploy ICCTs in two realistic driving domains, based on interstate Highway-94 and 280 in the US. Finally, we verify ICCT's utility with end-users and find that ICCTs are rated easier to simulate, quicker to validate, and more interpretable than neural networks.
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations
Venue: arXiv cs.CL 2023
Year: 2023

We propose a theoretical framework for formulating language model decoder algorithms with dynamic programming and information theory. With dynamic programming, we lift the design of decoder algorithms from the logit space to the action-state value function space, and show that the decoding algorithms are consequences of optimizing the action-state value functions. Each component in the action-state value function space has an information theoretical interpretation. With the lifting and interpretation, it becomes evident what the decoder algorithm is optimized for, and hence facilitating the arbitration of the tradeoffs in sensibleness, diversity, and attribution.
Traffic Video Object Detection using Motion Prior
Venue: arXiv cs.CV 2023
Year: 2023

Traffic videos inherently differ from generic videos in their stationary camera setup, thus providing a strong motion prior where objects often move in a specific direction over a short time interval. Existing works predominantly employ generic video object detection framework for traffic video object detection, which yield certain advantages such as broad applicability and robustness to diverse scenarios. However, they fail to harness the strength of motion prior to enhance detection accuracy. In this work, we propose two innovative methods to exploit the motion prior and boost the performance of both fully-supervised and semi-supervised traffic video object detection. Firstly, we introduce a new self-attention module that leverages the motion prior to guide temporal information integration in the fully-supervised setting. Secondly, we utilise the motion prior to develop a pseudo-labelling mechanism to eliminate noisy pseudo labels for the semi-supervised setting. Both of our motion-prior-centred methods consistently demonstrates superior performance, outperforming existing state-of-the-art approaches by a margin of 2% in terms of mAP.
Investigating AI's Challenges in Reasoning and Explanation from a Historical Perspective
Venue: arXiv cs.AI 2023
Year: 2023

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.
MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture
Venue: arXiv cs.CV 2023
Year: 2023

Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models. Nevertheless, challenges persist in the form of multi-view geometric inconsistencies and slow generation speeds within the existing 3D synthesis frameworks. This can be attributed to two factors: firstly, the deficiency of abundant geometric a priori knowledge in optimization, and secondly, the entanglement issue between geometry and texture in conventional 3D generation methods.In response, we introduce MetaDreammer, a two-stage optimization approach that leverages rich 2D and 3D prior knowledge. In the first stage, our emphasis is on optimizing the geometric representation to ensure multi-view consistency and accuracy of 3D objects. In the second stage, we concentrate on fine-tuning the geometry and optimizing the texture, thereby achieving a more refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages, respectively, we effectively mitigate the interdependence between geometry and texture. MetaDreamer establishes clear optimization objectives for each stage, resulting in significant time savings in the 3D generation process. Ultimately, MetaDreamer can generate high-quality 3D objects based on textual prompts within 20 minutes, and to the best of our knowledge, it is the most efficient text-to-3D generation method. Furthermore, we introduce image control into the process, enhancing the controllability of 3D generation. Extensive empirical evidence confirms that our method is not only highly efficient but also achieves a quality level that is at the forefront of current state-of-the-art 3D generation techniques.
Exploring Emotions in EEG: Deep Learning Approach with Feature Fusion
Venue: arXiv cs.HC 2023
Year: 2023

Emotion is an intricate physiological response that plays a crucial role in how we respond and cooperate with others in our daily affairs. Numerous experiments have been evolved to recognize emotion, however still require exploration to intensify the performance. To enhance the performance of effective emotion recognition, this study proposes a subject-dependent robust end-to-end emotion recognition system based on a 1D convolutional neural network (1D-CNN). We evaluate the SJTU\footnote{\href{https://en.wikipedia.org/wiki/Shanghai_Jiao_Tong_University}{Shanghai Jiao Tong University(SJTU)}} Emotion EEG Dataset SEED-V with five emotions (happy, sad, neural, fear, and disgust). To begin with, we utilize the Fast Fourier Transform (FFT) to decompose the raw EEG signals into six frequency bands and extract the power spectrum feature from the frequency bands. After that, we combine the extracted power spectrum feature with eye movement and differential entropy (DE) features. Finally, for classification, we apply the combined data to our proposed system. Consequently, it attains 99.80\% accuracy which surpasses each prior state-of-the-art system.
Practical Cybersecurity Ethics: Mapping CyBOK to Ethical Concerns
Venue: arXiv cs.CR 2023
Year: 2023

Research into the ethics of cybersecurity is an established and growing topic of investigation, however the translation of this research into practice is lacking: there exists a small number of professional codes of ethics or codes of practice in cybersecurity, however these are very broad and do not offer much insight into the ethical dilemmas that can be faced while performing specific cybersecurity activities. In order to address this gap, we leverage ongoing work on the Cyber Security Body of Knowledge (CyBOK) to help elicit and document the responsibilities and ethics of the profession. Based on a literature review of the ethics of cybersecurity, we use CyBOK to frame the exploration of ethical challenges in the cybersecurity profession through a series of 15 interviews with cybersecurity experts. Our approach is qualitative and exploratory, aiming to answer the research question "What ethical challenges, insights, and solutions arise in different areas of cybersecurity?". Our findings indicate that there are broad ethical challenges across the whole of cybersecurity, but also that different areas of cybersecurity can face specific ethical considerations for which more detailed guidance can help professionals in those areas. In particular, our findings indicate that security decision-making is expected of all security professionals, but that this requires them to balance a complex mix of technical, objective and subjective points of view, and that resolving conflicts raises challenging ethical dilemmas. We conclude that more work is needed to explore, map, and integrate ethical considerations into cybersecurity practice; the urgent need to conduct further research into the ethics of cybersecurity AI; and highlight the importance of this work for individuals and professional bodies who seek to develop and mature the cybersecurity profession in a responsible manner.
JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing
Venue: arXiv cs.CL 2023
Year: 2023

Advancements in sign language processing have been hindered by a lack of sufficient data, impeding progress in recognition, translation, and production tasks. The absence of comprehensive sign language datasets across the world's sign languages has widened the gap in this field, resulting in a few sign languages being studied more than others, making this research area extremely skewed mostly towards sign languages from high-income countries. In this work we introduce a new large and highly multilingual dataset for sign language translation: JWSign. The dataset consists of 2,530 hours of Bible translations in 98 sign languages, featuring more than 1,500 individual signers. On this dataset, we report neural machine translation experiments. Apart from bilingual baseline systems, we also train multilingual systems, including some that take into account the typological relatedness of signed or spoken languages. Our experiments highlight that multilingual systems are superior to bilingual baselines, and that in higher-resource scenarios, clustering language pairs that are related improves translation quality.
Consensus-based Resource Scheduling for Collaborative Multi-Robot Tasks
Venue: arXiv cs.RO 2023
Year: 2023

We propose integrating the edge-computing paradigm into the multi-robot collaborative scheduling to maximize resource utilization for complex collaborative tasks, which many robots must perform together. Examples include collaborative map-merging to produce a live global map during exploration instead of traditional approaches that schedule tasks on centralized cloud-based systems to facilitate computing. Our decentralized approach to a consensus-based scheduling strategy benefits a multi-robot-edge collaboration system by adapting to dynamic computation needs and communication-changing statistics as the system tries to optimize resources while maintaining overall performance objectives. Before collaborative task offloading, continuous device, and network profiling are performed at the computing resources, and the distributed scheduling scheme then selects the resource with maximum utility derived using a utility maximization approach. Thorough evaluations with and without edge servers on simulation and real-world multi-robot systems demonstrate that a lower task latency, a large throughput gain, and better frame rate processing may be achieved compared to the conventional edge-based systems.
Generative AI in Undergraduate Information Technology Education -- Insights from nine courses
Venue: arXiv cs.CY 2023
Year: 2023

The increasing use of digital teaching and emerging technologies, particularly AI-based tools, such as ChatGPT, is presenting an inevitable and significant impact on higher education. The capability of processing and generating text could bring change to several areas, such as learning assessments or learning experiences. Besides the negative impact, i.e exam cheating, we also see a positive side that ChatGPT can bring to education. This research article aims to contribute to the current debate on ChatGPT by systematic reflection and experience reported from nine bachelor IT courses at a Norwegian university. We conducted inductive empirical research with reflective notes and focused groups of lecturers from nine different IT courses. The findings were thematically organized with numerous use cases in teaching IT subjects. Our discussion highlights the disruptive implications of AI assistant usage in higher education and emphasizes the need for educators to shape this transformation.