OpenRaiser/Intern-Atlas huggingface dataset:
Naive Bayes Classifiers and One-hot Encoding of Categorical Variables
Venue: arXiv cs.LG 2024
Year: 2024

This paper investigates the consequences of encoding a $K$-valued categorical variable incorrectly as $K$ bits via one-hot encoding, when using a Na\"ıve Bayes classifier. This gives rise to a product-of-Bernoullis (PoB) assumption, rather than the correct categorical Na\"ıve Bayes classifier. The differences between the two classifiers are analysed mathematically and experimentally. In our experiments using probability vectors drawn from a Dirichlet distribution, the two classifiers are found to agree on the maximum a posteriori class label for most cases, although the posterior probabilities are usually greater for the PoB case.
S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. Recent studies are primarily dedicated to designing Transformer-based architectures for spatial-spectral long-range dependencies modeling, which is computationally expensive with quadratic complexity. Selective structured state space model (Mamba), which is efficient for modeling long-range dependencies with linear complexity, has recently shown promising progress. However, its potential in hyperspectral image processing that requires handling numerous spectral bands has not yet been explored. In this paper, we innovatively propose S$^2$Mamba, a spatial-spectral state space model for hyperspectral image classification, to excavate spatial-spectral contextual features, resulting in more efficient and accurate land cover analysis. In S$^2$Mamba, two selective structured state space models through different dimensions are designed for feature extraction, one for spatial, and the other for spectral, along with a spatial-spectral mixture gate for optimal fusion. More specifically, S$^2$Mamba first captures spatial contextual relations by interacting each pixel with its adjacent through a Patch Cross Scanning module and then explores semantic information from continuous spectral bands through a Bi-directional Spectral Scanning module. Considering the distinct expertise of the two attributes in homogenous and complicated texture scenes, we realize the Spatial-spectral Mixture Gate by a group of learnable matrices, allowing for the adaptive incorporation of representations learned across different dimensions. Extensive experiments conducted on HSI classification benchmarks demonstrate the superiority and prospect of S$^2$Mamba. The code will be made available at: https://github.com/PURE-melo/S2Mamba.
L3Cube-MahaNews: News-based Short Text and Long Document Classification Datasets in Marathi
Venue: arXiv cs.CL 2024
Year: 2024

The availability of text or topic classification datasets in the low-resource Marathi language is limited, typically consisting of fewer than 4 target labels, with some achieving nearly perfect accuracy. In this work, we introduce L3Cube-MahaNews, a Marathi text classification corpus that focuses on News headlines and articles. This corpus stands out as the largest supervised Marathi Corpus, containing over 1.05L records classified into a diverse range of 12 categories. To accommodate different document lengths, MahaNews comprises three supervised datasets specifically designed for short text, long documents, and medium paragraphs. The consistent labeling across these datasets facilitates document length-based analysis. We provide detailed data statistics and baseline results on these datasets using state-of-the-art pre-trained BERT models. We conduct a comparative analysis between monolingual and multilingual BERT models, including MahaBERT, IndicBERT, and MuRIL. The monolingual MahaBERT model outperforms all others on every dataset. These resources also serve as Marathi topic classification datasets or models and are publicly available at https://github.com/l3cube-pune/MarathiNLP .
Generating Situated Reflection Triggers about Alternative Solution Paths: A Case Study of Generative AI for Computer-Supported Collaborative Learning
Venue: arXiv cs.AI 2024
Year: 2024

An advantage of Large Language Models (LLMs) is their contextualization capability - providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We present a design and evaluation of a proof-of-concept LLM application to offer students dynamic and contextualized feedback. Specifically, we augment an Online Programming Exercise bot for a college-level Cloud Computing course with ChatGPT, which offers students contextualized reflection triggers during a collaborative query optimization task in database design. We demonstrate that LLMs can be used to generate highly situated reflection triggers that incorporate details of the collaborative discussion happening in context. We discuss in depth the exploration of the design space of the triggers and their correspondence with the learning objectives as well as the impact on student learning in a pilot study with 34 students.
Tracy, Traces, and Transducers: Computable Counterexamples and Explanations for HyperLTL Model-Checking
Venue: arXiv cs.LO 2024
Year: 2024

HyperLTL model-checking enables the automated verification of information-flow properties for security-critical systems. However, it only provides a binary answer. Here, we introduce two paradigms to compute counterexamples and explanations for HyperLTL model-checking, thereby considerably increasing its usefulness. Both paradigms are based on the maxim ``counterexamples/explanations are Skolem functions for the existentially quantified trace variables''. Our first paradigm is complete (everything can be explained), but restricted to ultimately periodic system traces. The second paradigm works with (Turing machine) computable Skolem functions and is therefore much more general, but also shown incomplete (not everything can computably be explained). Finally, we prove that it is decidable whether a given finite transition system and a formula have computable Skolem functions witnessing that the system satisfies the formula. Our algorithm also computes transducers implementing computable Skolem functions, if they exist.
Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet
Venue: arXiv cs.CV 2024
Year: 2024

Segmentation and labeling of vertebrae in MRI images of the spine are critical for the diagnosis of illnesses and abnormalities. These steps are indispensable as MRI technology provides detailed information about the tissue structure of the spine. Both supervised and unsupervised segmentation methods exist, yet acquiring sufficient data remains challenging for achieving high accuracy. In this study, we propose an enhancing approach based on modified attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by incorporating novel masking logic, thus significantly advancing the state-of-the-art in vertebral segmentation and labeling. This contributes to more precise and reliable diagnosis and treatment planning.
VoroTO: Multiscale Topology Optimization of Voronoi Structures using Surrogate Neural Networks
Venue: arXiv cs.CE 2024
Year: 2024

Cellular structures found in nature exhibit remarkable properties such as high strength, high energy absorption, excellent thermal/acoustic insulation, and fluid transfusion. Many of these structures are Voronoi-like; therefore researchers have proposed Voronoi multi-scale designs for a wide variety of engineering applications. However, designing such structures can be computationally prohibitive due to the multi-scale nature of the underlying analysis and optimization. In this work, we propose the use of a neural network (NN) to carry out efficient topology optimization (TO) of multi-scale Voronoi structures. The NN is first trained using Voronoi parameters (cell site locations, thickness, orientation, and anisotropy) to predict the homogenized constitutive properties. This network is then integrated into a conventional TO framework to minimize structural compliance subject to a volume constraint. Special considerations are given for ensuring positive definiteness of the constitutive matrix and promoting macroscale connectivity. Several numerical examples are provided to showcase the proposed method.
Near-Term Enforcement of AI Chip Export Controls Using A Firmware-Based Design for Offline Licensing
Venue: arXiv cs.CR 2024
Year: 2024

Offline Licensing is a mechanism for compute governance that could be used to prevent unregulated training of potentially dangerous frontier AI models. The mechanism works by disabling AI chips unless they have an unused license from a regulator. In this report, we present a design for a minimal version of Offline Licensing that could be delivered via a firmware update. Existing AI chips could potentially support Offline Licensing within a year if they have the following (relatively common) hardware security features: firmware verification, firmware rollback protection, and secure non-volatile memory. Public documentation suggests that NVIDIA's H100 AI chip already has these security features. Without additional hardware modifications, the system is susceptible to physical hardware attacks. However, these attacks might require expensive equipment and could be difficult to reliably apply to thousands of AI chips. A firmware-based Offline Licensing design shares the same legal requirements and license approval mechanism as a hardware-based solution. Implementing a firmware-based solution now could accelerate the eventual deployment of a more secure hardware-based solution in the future. For AI chip manufacturers, implementing this security mechanism might allow chips to be sold to customers that would otherwise be prohibited by export restrictions. For governments, it may be important to be able to prevent unsafe or malicious actors from training frontier AI models in the next few years. Based on this initial analysis, firmware-based Offline Licensing could partially solve urgent security and trade problems and is technically feasible for AI chips that have common hardware security features.
Design and Optimization of Reconfigurable Intelligent Surfaces Using the PEEC Method
Venue: arXiv cs.IT 2024
Year: 2024

The design and optimization of Reconfigurable Intelligent Surfaces (RISs) are key challenges for future wireless communication systems. RISs are devices that can manipulate electromagnetic (EM) waves in a programmable way, thus enhancing the performance and efficiency of wireless links. To achieve this goal, it is essential to have reliable EM models that can capture the behavior of RISs in different scenarios. This work demonstrates that the Partial Elements Equivalent Circuit (PEEC) method is a powerful tool for EM analysis of RIS-aided wireless links. It might also be integrated with optimization algorithms in order to optimize wireless communication networks.
SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies
Venue: arXiv cs.LG 2024
Year: 2024

While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in safety-critical applications, such as automated driving systems (ADS). Counterfactual (CF) explanations have recently gained prominence for their ability to interpret black-box Deep Learning (DL) models. CF examples are associated with minimal changes in the input, resulting in a complementary output by the DL model. Finding such alternations, particularly for high-dimensional visual inputs, poses significant challenges. Besides, the temporal dependency introduced by the reliance of the DRL agent action on a history of past state observations further complicates the generation of CF examples. To address these challenges, we propose using a saliency map to identify the most influential input pixels across the sequence of past observed states by the agent. Then, we feed this map to a deep generative model, enabling the generation of plausible CFs with constrained modifications centred on the salient regions. We evaluate the effectiveness of our framework in diverse domains, including ADS, Atari Pong, Pacman and space-invaders games, using traditional performance metrics such as validity, proximity and sparsity. Experimental results demonstrate that this framework generates more informative and plausible CFs than the state-of-the-art for a wide range of environments and DRL agents. In order to foster research in this area, we have made our datasets and codes publicly available at https://github.com/Amir-Samadi/SAFE-RL.
"What Keeps People Secure is That They Met The Security Team": Deconstructing Drivers And Goals of Organizational Security Awareness
Venue: arXiv cs.CR 2024
Year: 2024

Security awareness campaigns in organizations now collectively cost billions of dollars annually. There is increasing focus on ensuring certain security behaviors among employees. On the surface, this would imply a user-centered view of security in organizations. Despite this, the basis of what security awareness managers do and what decides this are unclear. We conducted n=15 semi-structured interviews with full-time security awareness managers, with experience across various national and international companies in European countries, with thousands of employees. Through thematic analysis, we identify that success in awareness management is fragile while having the potential to improve; there are a range of restrictions, and mismatched drivers and goals for security awareness, affecting how it is structured, delivered, measured, and improved. We find that security awareness as a practice is underspecified, and split between messaging around secure behaviors and connecting to employees, with a lack of recognition for the measures that awareness managers regard as important. We discuss ways forward, including alternative indicators of success, and security usability advocacy for employees.
Network Intent Decomposition and Optimization for Energy-Aware Radio Access Network
Venue: arXiv cs.NI 2024
Year: 2024

With recent advancements in the sixth generation (6G) communication technologies, more vertical industries have encountered diverse network services. How to reduce energy consumption is critical to meet the expectation of the quality of diverse network services. In particular, the number of base stations in 6G is huge with coupled adjustable network parameters. However, the problem is complex with multiple network objectives and parameters. Network intents are difficult to map to individual network elements and require enhanced automation capabilities. In this paper, we present a network intent decomposition and optimization mechanism in an energy-aware radio access network scenario. By characterizing the intent ontology with a standard template, we present a generic network intent representation framework. Then we propose a novel intent modeling method using Knowledge Acquisition in automated Specification language, which can model the network ontology. To clarify the number and types of network objectives and energy-saving operations, we develop a Softgoal Interdependency Graph-based network intent decomposition model, and thus, a network intent decomposition algorithm is presented. Simulation results demonstrate that the proposed algorithm outperforms without conflict analysis in intent decomposition time. Moreover, we design a deep Q-network-assisted intent optimization scheme to validate the performance gain.
Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
Venue: arXiv cs.DC 2024
Year: 2024

In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable computing resources and automated workflow management on both cloud and high-performance supercomputers. Here we introduce Dflow, an open-source Python toolkit designed for scientists to construct workflows with simple programming interfaces. It enables complex process control and task scheduling across a distributed, heterogeneous infrastructure, leveraging containers and Kubernetes for flexibility. Dflow is highly observable and can scale to thousands of concurrent nodes per workflow, enhancing the efficiency of complex scientific computing tasks. The basic unit in Dflow, known as an Operation (OP), is reusable and independent of the underlying infrastructure or context. Dozens of workflow projects have been developed based on Dflow, spanning a wide range of projects. We anticipate that the reusability of Dflow and its components will encourage more scientists to publish their workflows and OP components. These components, in turn, can be adapted and reused in various contexts, fostering greater collaboration and innovation in the scientific community.
Spectral-Spatial Mamba for Hyperspectral Image Classification
Venue: arXiv cs.CV 2024
Year: 2024

Recently, deep learning models have achieved excellent performance in hyperspectral image (HSI) classification. Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, Transformer has the problem of quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of Transformers. Therefore, in this paper, we make a preliminary attempt to apply the Mamba to HSI classification, leading to the proposed spectral-spatial Mamba (SS-Mamba). Specifically, the proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB block consists of two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, respectively. Besides, the feature enhancement module modulates spatial and spectral tokens using HSI sample's center region information. In this way, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results compared with the state-of-the-art methods. The Mamba-based method opens a new window for HSI classification.
Understanding and Shaping Human-Technology Assemblages in the Age of Generative AI
Venue: arXiv cs.HC 2024
Year: 2024

Generative AI capabilities are rapidly transforming how we perceive, interact with, and relate to machines. This one-day workshop invites HCI researchers, designers, and practitioners to imaginatively inhabit and explore the possible futures that might emerge from humans combining generative AI capabilities into everyday technologies at massive scale. Workshop participants will craft stories, visualisations, and prototypes through scenario-based design to investigate these possible futures, resulting in the production of an open-annotated scenario library and a journal or interactions article to disseminate the findings. We aim to gather the DIS community knowledge to explore, understand and shape the relations this new interaction paradigm is forging between humans, their technologies and the environment in safe, sustainable, enriching, and responsible ways.
PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, image generation technology has rapidly advanced, resulting in the creation of a vast array of AI-generated images (AIGIs). However, the quality of these AIGIs is highly inconsistent, with low-quality AIGIs severely impairing the visual experience of users. Due to the widespread application of AIGIs, the AI-generated image quality assessment (AIGIQA), aimed at evaluating the quality of AIGIs from the perspective of human perception, has garnered increasing interest among scholars. Nonetheless, current research has not yet fully explored this field. We have observed that existing databases are limited to images generated from single scenario settings. Databases such as AGIQA-1K, AGIQA-3K, and AIGCIQA2023, for example, only include images generated by text-to-image generative models. This oversight highlights a critical gap in the current research landscape, underscoring the need for dedicated databases catering to image-to-image scenarios, as well as more comprehensive databases that encompass a broader range of AI-generated image scenarios. Addressing these issues, we have established a large scale perceptual quality assessment database for both text-to-image and image-to-image AIGIs, named PKU-AIGIQA-4K. We then conduct a well-organized subjective experiment to collect quality labels for AIGIs and perform a comprehensive analysis of the PKU-AIGIQA-4K database. Regarding the use of image prompts during the training process, we propose three image quality assessment (IQA) methods based on pre-trained models that include a no-reference method NR-AIGCIQA, a full-reference method FR-AIGCIQA, and a partial-reference method PR-AIGCIQA. Finally, leveraging the PKU-AIGIQA-4K database, we conduct extensive benchmark experiments and compare the performance of the proposed methods and the current IQA methods.
Fostering Trust in Smart Inverters: A Framework for Firmware Update Management and Tracking in VPP Context
Venue: arXiv cs.CR 2024
Year: 2024

Ensuring the reliability and security of smart inverters that provide the interface between distributed energy resources (DERs) and the power grid becomes paramount with the surge in integrating DERs into the (smart) power grid. Despite the importance of having updated firmware / software versions within a reasonable time frame, existing methods for establishing trust through firmware updates lack effective historical tracking and verification. This paper introduces a novel framework to manage and track firmware update history, leveraging verifiable credentials. By tracking the update history and implementing a trust cycle based on these verifiable updates, we aim to improve grid resilience, enhance cybersecurity, and increase transparency for stakeholders.