OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN
Venue: arXiv cs.CR 2024
Year: 2024

Rising complexity of in-vehicle electronics is enabling new capabilities like autonomous driving and active safety. However, rising automation also increases risk of security threats which is compounded by lack of in-built security measures in legacy networks like CAN, allowing attackers to observe, tamper and modify information shared over such broadcast networks. Various intrusion detection approaches have been proposed to detect and tackle such threats, with machine learning models proving highly effective. However, deploying machine learning models will require high processing power through high-end processors or GPUs to perform them close to line rate. In this paper, we propose a hybrid FPGA-based ECU approach that can transparently integrate IDS functionality through a dedicated off-the-shelf hardware accelerator that implements a deep-CNN intrusion detection model. Our results show that the proposed approach provides an average accuracy of over 99% across multiple attack datasets with 0.64% false detection rates while consuming 94% less energy and achieving 51.8% reduction in per-message processing latency when compared to IDS implementations on GPUs.
Classification with neural networks with quadratic decision functions
Venue: arXiv cs.LG 2024
Year: 2024

Neural networks with quadratic decision functions have been introduced as alternatives to standard neural networks with affine linear ones. They are advantageous when the objects or classes to be identified are compact and of basic geometries like circles, ellipses etc. In this paper we investigate the use of such ansatz functions for classification. In particular we test and compare the algorithm on the MNIST dataset for classification of handwritten digits and for classification of subspecies. We also show, that the implementation can be based on the neural network structure in the software Tensorflow and Keras, respectively.
Character Recognition in Byzantine Seals with Deep Neural Networks
Venue: arXiv cs.CV 2024
Year: 2024

Seals are small coin-shaped artifacts, mostly made of lead, held with strings to seal letters. This work presents the first attempt towards automatic reading of text on Byzantine seal images.Byzantine seals are generally decorated with iconography on the obverse side and Greek text on the reverse side. Text may include the sender's name, position in the Byzantine aristocracy, and elements of prayers. Both text and iconography are precious literary sources that wait to be exploited electronically, so the development of computerized systems for interpreting seals images is of paramount importance. This work's contribution is hence a deep, two-stages, character reading pipeline for transcribing Byzantine seal images. A first deep convolutional neural network (CNN) detects characters in the seal (character localization). A second convolutional network reads the localized characters (character classification). Finally, a diplomatic transcription of the seal is provided by post-processing the two network outputs. We provide an experimental evaluation of each CNN in isolation and both CNNs in combination. All performances are evaluated by cross-validation. Character localization achieves a mean average precision (mAP@0.5) greater than 0.9. Classification of characters cropped from ground truth bounding boxes achieves Top-1 accuracy greater than 0.92. End-to-end evaluation shows the efficiency of the proposed approach when compared to the SoTA for similar tasks.
Fast gradient-free activation maximization for neurons in spiking neural networks
Venue: arXiv cs.NE 2024
Year: 2024

Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a large set of different stimuli. The properties of these stimuli should be varied iteratively in order to maximize the neuronal response. To utilize this feedback loop for a biological neural network, it is important to run it quickly and efficiently in order to reach the stimuli that maximizes certain neurons' activation with the least number of iterations possible. Here we present a framework with an efficient design for such a loop. We successfully tested it on an artificial spiking neural network (SNN), which is a model that simulates the asynchronous spiking activity of neurons in living brains. Our optimization method for activation maximization is based on the low-rank Tensor Train decomposition of the discrete activation function. The optimization space is the latent parameter space of images generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the first time that effective AM has been applied to SNNs. We track changes in the optimal stimuli for artificial neurons during training and show that highly selective neurons can form already in the early epochs of training and in the early layers of a convolutional spiking network. This formation of refined optimal stimuli is associated with an increase in classification accuracy. Some neurons, especially in the deeper layers, may gradually change the concepts they are selective for during learning, potentially explaining their importance for model performance.
Data Augmentation for Traffic Classification
Venue: arXiv cs.LG 2024
Year: 2024

Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks. In this work, we fulfill this gap by benchmarking 18 augmentation functions applied to 3 TC datasets using packet time series as input representation and considering a variety of training conditions. Our results show that (i) DA can reap benefits previously unexplored, (ii) augmentations acting on time series sequence order and masking are better suited for TC than amplitude augmentations and (iii) basic models latent space analysis can help understanding the positive/negative effects of augmentations on classification performance.
Semantic-Aware Resource Allocation in Constrained Networks with Limited User Participation
Venue: arXiv cs.IT 2024
Year: 2024

Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the need to tailor the resource allocation to meet the specific requirements of semantic transmission. In this paper, we focus on networks with limited resources where devices are constrained to transmit with limited bandwidth and power over large distance. Specifically, we devise an efficient strategy to select the most pertinent semantic features and participating users, taking into account the channel quality, the transmission time, and the recovery accuracy. To this end, we formulate an optimization problem with the goal of selecting the most relevant and accurate semantic features over devices while satisfying constraints on transmission time and quality of the channel. This involves optimizing communication resources, identifying participating users, and choosing specific semantic information for transmission. The underlying problem is inherently complex due to its non-convex nature and combinatorial constraints. To overcome this challenge, we efficiently approximate the optimal solution by solving a series of integer linear programming problems. Our numerical findings illustrate the effectiveness and efficiency of our approach in managing semantic communications in networks with limited resources.
HaliVer: Deductive Verification and Scheduling Languages Join Forces
Venue: arXiv cs.LO 2024
Year: 2024

The HaliVer tool integrates deductive verification into the popular scheduling language Halide, used for image processing pipelines and array computations. HaliVer uses Vercors, a separation logic-based verifier, to verify the correctness of (1) the Halide algorithms and (2) the optimised parallel code produced by \halide when an optimisation schedule is applied to the algorithm. This allows proving complex, optimised code correct while reducing the effort to provide the required verification annotations. For both approaches, the same specification is used. We evaluated the tool on several optimised programs generated from characteristic Halide algorithms, using all but one of the essential scheduling directives available in Halide. Without annotation effort, Haliver proves memory safety in almost all programs. With annotations Haliver, additionally, proves functional correctness properties. We show that the approach is viable and reduces the manual annotation effort by an order of magnitude.
Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems
Venue: arXiv cs.LG 2024
Year: 2024

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.
An AI-Resilient Text Rendering Technique for Reading and Skimming Documents
Venue: arXiv cs.HC 2024
Year: 2024

Readers find text difficult to consume for many reasons. Summarization can address some of these difficulties, but introduce others, such as omitting, misrepresenting, or hallucinating information, which can be hard for a reader to notice. One approach to addressing this problem is to instead modify how the original text is rendered to make important information more salient. We introduce Grammar-Preserving Text Saliency Modulation (GP-TSM), a text rendering method with a novel means of identifying what to de-emphasize. Specifically, GP-TSM uses a recursive sentence compression method to identify successive levels of detail beyond the core meaning of a passage, which are de-emphasized by rendering words in successively lighter but still legible gray text. In a lab study (n=18), participants preferred GP-TSM over pre-existing word-level text rendering methods and were able to answer GRE reading comprehension questions more efficiently.
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
Venue: arXiv cs.HC 2024
Year: 2024

Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To alleviate this issue, we propose a novel visual GUI agent -- SeeClick, which only relies on screenshots for task automation. In our preliminary study, we have discovered a key challenge in developing visual GUI agents: GUI grounding -- the capacity to accurately locate screen elements based on instructions. To tackle this challenge, we propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate the curation of GUI grounding data. Along with the efforts above, we have also created ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. After pre-training, SeeClick demonstrates significant improvement in ScreenSpot over various baselines. Moreover, comprehensive evaluations on three widely used benchmarks consistently support our finding that advancements in GUI grounding directly correlate with enhanced performance in downstream GUI agent tasks. The model, data and code are available at https://github.com/njucckevin/SeeClick.
Design Principles & Issues for Gaze and Pinch Interaction
Venue: arXiv cs.HC 2024
Year: 2024

With the imminent release of the Apple Vision Pro, a wave of innovative technology will be going to get into people's hands. The "eyes and hands" interface mixes up interaction design, indicating a need for principles, frameworks, and standards. This article highlights 5 design principles and 5 issues for designing eyes & hands interfaces, drawing insights from both my personal experience and scientific articles in the area of human-computer interaction. Whether you're interested in design, tech, or research in this evolving space, the article provides valuable perspectives to enhance your understanding.
On the selection of the correct number of terms for profile construction: theoretical and empirical analysis
Venue: arXiv cs.IR 2024
Year: 2024

In this paper, we examine the problem of building a user profile from a set of documents. This profile will consist of a subset of the most representative terms in the documents that best represent user preferences or interests. Inspired by the discrete concentration theory we have conducted an axiomatic study of seven properties that a selection function should fulfill: the minimum and maximum uncertainty principle, invariant to adding zeros, invariant to scale transformations, principle of nominal increase, transfer principle and the richest get richer inequality. We also present a novel selection function based on the use of similarity metrics, and more specifically the cosine measure which is commonly used in information retrieval, and demonstrate that this verifies six of the properties in addition to a weaker variant of the transfer principle, thereby representing a good selection approach. The theoretical study was complemented with an empirical study to compare the performance of different selection criteria (weight- and unweight-based) using real data in a parliamentary setting. In this study, we analyze the performance of the different functions focusing on the two main factors affecting the selection process: profile size (number of terms) and weight distribution. These profiles are then used in a document filtering task to show that our similarity-based approach performs well in terms not only of recommendation accuracy but also efficiency (we obtain smaller profiles and consequently faster recommendations).
Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation
Venue: arXiv cs.MA 2024
Year: 2024

One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, $\sum_{k}$-assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest.
The Radiation Oncology NLP Database
Venue: arXiv cs.CL 2024
Year: 2024

We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology, an important medical specialty that has received limited attention from the NLP community in the past. With the advent of Artificial General Intelligence (AGI), there is an increasing need for specialized datasets and benchmarks to facilitate research and development. ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration. It encompasses various NLP tasks including Logic Reasoning, Text Classification, Named Entity Recognition (NER), Question Answering (QA), Text Summarization, and Patient-Clinician Conversations, each with a distinct focus on radiation oncology concepts and application cases. In addition, we have developed an instruction-tuning dataset consisting of over 20k instruction pairs (based on ROND) and trained a large language model, CancerChat. This serves to demonstrate the potential of instruction-tuning large language models within a highly-specialized medical domain. The evaluation results in this study could serve as baseline results for future research. ROND aims to stimulate advancements in radiation oncology and clinical NLP by offering a platform for testing and improving algorithms and models in a domain-specific context. The ROND dataset is a joint effort of multiple U.S. health institutions. The data is available at https://github.com/zl-liu/Radiation-Oncology-NLP-Database.
Warum wir es für eine gute Idee gehalten haben, eine DACH-Spieledatenbank aufzubauen
Venue: arXiv cs.HC 2024
Year: 2024

We are in the process of creating a database of digital games from the DACH region. This article provides an insight into the context in which it was created and the underlying methodological considerations behind the games database. The database was compiled collaboratively and lists digital games developed in Germany, Austria and Switzerland up to the year 2000. In this report, we outline our initial considerations and the various stages of realisation as well as the input data on which the database was built, the aims of the data model and the difficulties we faced during the creation process. We then pin down the current status of the games database and give an outlook on the project's future plans. -- Unser Werkstattbericht gibt Einblick in den Entstehungskontext sowie die zugrundeliegenden methodischen Überlegungen hinter der von den Autor*innen publizierten Spieledatenbank. Diese wurde kollaborativ erarbeitet und führt digitale Spiele, die in Deutschland, Österreich und der Schweiz bis zum Jahr 2000 entwickelt wurden. In diesem Bericht skizzieren wir neben unseren Ausgangsüberlegungen und den verschiedenen Arbeitsschritten bei der Realisierung außerdem auch, auf welcher Datenbasis die Datenbank aufgebaut und geprüft wurde, was die Ziele des Datenmodells sind und mit welchen Schwierigkeiten wir im Prozess der Erstellung konfrontiert waren. Hiernach ordnen wir den aktuellen Stand der Spieledatenbank ein und geben einen Ausblick auf die weiteren Pläne des Projekts.
Low-Complexity Integer Divider Architecture for Homomorphic Encryption
Venue: arXiv cs.CR 2024
Year: 2024

Homomorphic encryption (HE) allows computations to be directly carried out on ciphertexts and enables privacy-preserving cloud computing. The computations on the coefficients of the polynomials involved in HE are always followed by modular reduction, and the overall complexity of ciphertext multiplication can be reduced by utilizing the quotient. Our previous design considers the cases that the dividend is an integer multiple of the modulus and the modulus is in the format of $2^w-2^u\pm1$, where $u<w/2$. In this paper, the division is generalized for larger $u$ and dividend not an integer multiple of the modulus. An algorithm is proposed to compute the quotient and vigorous mathematical proofs are provided. Moreover, efficient hardware architecture is developed for implementing the proposed algorithm. Compared to alternative division approaches that utilize the inverse of the divisor, for $w=32$, the proposed design achieves at least 9% shorter latency and 79\% area reduction for 75% possible values of $u$.
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have demonstrated remarkable success in modeling complex relationships in graph-structured data. A recent innovation in this field is the family of Differential Equation-Inspired Graph Neural Networks (DE-GNNs), which leverage principles from continuous dynamical systems to model information flow on graphs with built-in properties such as feature smoothing or preservation. However, existing DE-GNNs rely on first or second-order temporal dependencies. In this paper, we propose a neural extension to those pre-defined temporal dependencies. We show that our model, called TDE-GNN, can capture a wide range of temporal dynamics that go beyond typical first or second-order methods, and provide use cases where existing temporal models are challenged. We demonstrate the benefit of learning the temporal dependencies using our method rather than using pre-defined temporal dynamics on several graph benchmarks.
LLM4Fuzz: Guided Fuzzing of Smart Contracts with Large Language Models
Venue: arXiv cs.CR 2024
Year: 2024

As blockchain platforms grow exponentially, millions of lines of smart contract code are being deployed to manage extensive digital assets. However, vulnerabilities in this mission-critical code have led to significant exploitations and asset losses. Thorough automated security analysis of smart contracts is thus imperative. This paper introduces LLM4Fuzz to optimize automated smart contract security analysis by leveraging large language models (LLMs) to intelligently guide and prioritize fuzzing campaigns. While traditional fuzzing suffers from low efficiency in exploring the vast state space, LLM4Fuzz employs LLMs to direct fuzzers towards high-value code regions and input sequences more likely to trigger vulnerabilities. Additionally, LLM4Fuzz can leverage LLMs to guide fuzzers based on user-defined invariants, reducing blind exploration overhead. Evaluations of LLM4Fuzz on real-world DeFi projects show substantial gains in efficiency, coverage, and vulnerability detection compared to baseline fuzzing. LLM4Fuzz also uncovered five critical vulnerabilities that can lead to a loss of more than $247k.