OpenRaiser/Intern-Atlas huggingface dataset:
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks
Venue: arXiv cs.CV 2023
Year: 2023

Fingerprint traits are widely recognized for their unique qualities and security benefits. Despite their extensive use, fingerprint features can be vulnerable to puppet attacks, where attackers manipulate a reluctant but genuine user into completing the authentication process. Defending against such attacks is challenging due to the coexistence of a legitimate identity and an illegitimate intent. In this paper, we propose PUPGUARD, a solution designed to guard against puppet attacks. This method is based on user behavioral patterns, specifically, the user needs to press the capture device twice successively with different fingers during the authentication process. PUPGUARD leverages both the image features of fingerprints and the timing characteristics of the pressing intervals to establish two-factor authentication. More specifically, after extracting image features and timing characteristics, and performing feature selection on the image features, PUPGUARD fuses these two features into a one-dimensional feature vector, and feeds it into a one-class classifier to obtain the classification result. This two-factor authentication method emphasizes dynamic behavioral patterns during the authentication process, thereby enhancing security against puppet attacks. To assess PUPGUARD's effectiveness, we conducted experiments on datasets collected from 31 subjects, including image features and timing characteristics. Our experimental results demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted comparative experiments to validate the superiority of combining image features and timing characteristics within PUPGUARD for enhancing resistance against puppet attacks.
LLM-based Control Code Generation using Image Recognition
Venue: arXiv cs.SE 2023
Year: 2023

LLM-based code generation could save significant manual efforts in industrial automation, where control engineers manually produce control logic for sophisticated production processes. Previous attempts in control logic code generation lacked methods to interpret schematic drawings from process engineers. Recent LLMs now combine image recognition, trained domain knowledge, and coding skills. We propose a novel LLM-based code generation method that generates IEC 61131-3 Structure Text control logic source code from Piping-and-Instrumentation Diagrams (P&IDs) using image recognition. We have evaluated the method in three case study with industrial P&IDs and provide first evidence on the feasibility of such a code generation besides experiences on image recognition glitches.
Sinhala-English Word Embedding Alignment: Introducing Datasets and Benchmark for a Low Resource Language
Venue: arXiv cs.CL 2023
Year: 2023

Since their inception, embeddings have become a primary ingredient in many flavours of Natural Language Processing (NLP) tasks supplanting earlier types of representation. Even though multilingual embeddings have been used for the increasing number of multilingual tasks, due to the scarcity of parallel training data, low-resource languages such as Sinhala, tend to focus more on monolingual embeddings. Then when it comes to the aforementioned multi-lingual tasks, it is challenging to utilize these monolingual embeddings given that even if the embedding spaces have a similar geometric arrangement due to an identical training process, the embeddings of the languages considered are not aligned. This is solved by the embedding alignment task. Even in this, high-resource language pairs are in the limelight while low-resource languages such as Sinhala which is in dire need of help seem to have fallen by the wayside. In this paper, we try to align Sinhala and English word embedding spaces based on available alignment techniques and introduce a benchmark for Sinhala language embedding alignment. In addition to that, to facilitate the supervised alignment, as an intermediate task, we also introduce Sinhala-English alignment datasets. These datasets serve as our anchor datasets for supervised word embedding alignment. Even though we do not obtain results comparable to the high-resource languages such as French, German, or Chinese, we believe our work lays the groundwork for more specialized alignment between English and Sinhala embeddings.
Parallel Verification of Natural Deduction Proof Graphs
Venue: arXiv cs.LO 2023
Year: 2023

Graph-based interactive theorem provers offer a visual representation of proofs, explicitly representing the dependencies and inferences between each of the proof steps in a graph or hypergraph format. The number and complexity of these dependency links can determine how long it takes to verify the validity of the entire proof. Towards this end, we present a set of parallel algorithms for the formal verification of graph-based natural-deduction (ND) style proofs. We introduce a definition of layering that captures dependencies between the proof steps (nodes). Nodes in each layer can then be verified in parallel as long as prior layers have been verified. To evaluate the performance of our algorithms on proof graphs, we propose a framework for finding the performance bounds and patterns using directed acyclic network topologies (DANTs). This framework allows us to create concrete instances of DANTs for empirical evaluation of our algorithms. With this, we compare our set of parallel algorithms against a serial implementation with two experiments: one scaling both the problem size and the other scaling the number of threads. Our findings show that parallelization results in improved verification performance for certain DANT instances. We also show that our algorithms scale for certain DANT instances with respect to the number of threads.
Using Cooperative Game Theory to Prune Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks. The ever-growing size of deep neural networks (DNNs) increases their performance, but also their computational requirements. We introduce a method called Game Theory Assisted Pruning (GTAP), which reduces the neural network's size while preserving its predictive accuracy. GTAP is based on eliminating neurons in the network based on an estimation of their joint impact on the prediction quality through game theoretic solutions. Specifically, we use a power index akin to the Shapley value or Banzhaf index, tailored using a procedure similar to Dropout (commonly used to tackle overfitting problems in machine learning). Empirical evaluation of both feedforward networks and convolutional neural networks shows that this method outperforms existing approaches in the achieved tradeoff between the number of parameters and model accuracy.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Venue: arXiv cs.CV 2023
Year: 2023

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.
The Set of Pure Gaps at Several Rational Places in Function Fields
Venue: arXiv cs.IT 2023
Year: 2023

In this work, using maximal elements in generalized Weierstrass semigroups and its relationship with pure gaps, we extend the results in \cite{CMT2024} and provide a way to completely determine the set of pure gaps at several rational places in an arbitrary function field $F$ over a finite field and its cardinality. As an example, we determine the cardinality and a simple explicit description of the set of pure gaps at several rational places distinct to the infinity place on Kummer extensions, which is a different characterization from that presented by Hu and Yang in \cite{HY2018}. Furthermore, we present some applications in coding theory and AG codes with good parameters.
ReuseSense: With Great Reuse Comes Greater Efficiency; Effectively Employing Computation Reuse on General-Purpose CPUs
Venue: arXiv cs.AR 2023
Year: 2023

Deep Neural Networks (DNNs) are the de facto algorithm for tackling cognitive tasks in real-world applications such as speech recognition and natural language processing. DNN inference comprises numerous dot product operations between inputs and weights that require numerous multiplications and memory accesses, which hinder their performance and energy consumption when evaluated in modern CPUs. In this work, we leverage the high degree of similarity between consecutive inputs in different DNN layers to improve the performance and energy efficiency of DNN inference on CPUs. To this end, we propose ReuseSense, a new hardware scheme that includes ReuseSensor, an engine to efficiently generate the compute and load instructions needed to evaluate a DNN layer accordingly when sensing similar inputs. By intelligently reusing previously computed product values, ReuseSense allows bypassing computations when encountering input values identical to previous ones. Additionally, it efficiently avoids redundant loads by skipping weight loads associated with the bypassed dot product computations. Our experiments show that ReuseSense achieves an 8x speedup in performance and a 74% reduction in total energy consumption across several DNNs on average over the baseline.
A Relay System for Semantic Image Transmission based on Shared Feature Extraction and Hyperprior Entropy Compression
Venue: arXiv cs.CV 2023
Year: 2023

Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out respectively at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, under the same conditions, the multi-scale structural similarity (MS-SSIM) of this system is superior to the comparison method by approximately 0.2.
From Principle to Practice: Vertical Data Minimization for Machine Learning
Venue: arXiv cs.LG 2023
Year: 2023

Aiming to train and deploy predictive models, organizations collect large amounts of detailed client data, risking the exposure of private information in the event of a breach. To mitigate this, policymakers increasingly demand compliance with the data minimization (DM) principle, restricting data collection to only that data which is relevant and necessary for the task. Despite regulatory pressure, the problem of deploying machine learning models that obey DM has so far received little attention. In this work, we address this challenge in a comprehensive manner. We propose a novel vertical DM (vDM) workflow based on data generalization, which by design ensures that no full-resolution client data is collected during training and deployment of models, benefiting client privacy by reducing the attack surface in case of a breach. We formalize and study the corresponding problem of finding generalizations that both maximize data utility and minimize empirical privacy risk, which we quantify by introducing a diverse set of policy-aligned adversarial scenarios. Finally, we propose a range of baseline vDM algorithms, as well as Privacy-aware Tree (PAT), an especially effective vDM algorithm that outperforms all baselines across several settings. We plan to release our code as a publicly available library, helping advance the standardization of DM for machine learning. Overall, we believe our work can help lay the foundation for further exploration and adoption of DM principles in real-world applications.
User-Centric Deployment of Automated Program Repair at Bloomberg
Venue: arXiv cs.SE 2023
Year: 2023

Automated program repair (APR) tools have unlocked the potential for the rapid rectification of codebase issues. However, to encourage wider adoption of program repair in practice, it is necessary to address the usability concerns related to generating irrelevant or out-of-context patches. When software engineers are presented with patches they deem uninteresting or unhelpful, they are burdened with more "noise" in their workflows and become less likely to engage with APR tools in future. This paper presents a novel approach to optimally time, target, and present auto-generated patches to software engineers. To achieve this, we designed, developed, and deployed a new tool dubbed B-Assist, which leverages GitHub's Suggested Changes interface to seamlessly integrate automated suggestions into active pull requests (PRs), as opposed to creating new, potentially distracting PRs. This strategy ensures that suggestions are not only timely, but also contextually relevant and delivered to engineers most familiar with the affected code. Evaluation among Bloomberg software engineers demonstrated their preference for this approach. From our user study, B-Assist's efficacy is evident, with the acceptance rate of patch suggestions being as high as 74.56%; engineers also found the suggestions valuable, giving usefulness ratings of at least 4 out of 5 in 78.2% of cases. Further, this paper sheds light on persisting usability challenges in APR and lays the groundwork for enhancing the user experience in future APR tools.
Removing Adverse Volumetric Effects From Trained Neural Radiance Fields
Venue: arXiv cs.CV 2023
Year: 2023

While the use of neural radiance fields (NeRFs) in different challenging settings has been explored, only very recently have there been any contributions that focus on the use of NeRF in foggy environments. We argue that the traditional NeRF models are able to replicate scenes filled with fog and propose a method to remove the fog when synthesizing novel views. By calculating the global contrast of a scene, we can estimate a density threshold that, when applied, removes all visible fog. This makes it possible to use NeRF as a way of rendering clear views of objects of interest located in fog-filled environments. Additionally, to benchmark performance on such scenes, we introduce a new dataset that expands some of the original synthetic NeRF scenes through the addition of fog and natural environments. The code, dataset, and video results can be found on our project page: https://vegardskui.com/fognerf/
Cross-Modal Search and Exploration of Greek Painted Pottery
Venue: arXiv cs.IR 2023
Year: 2023

This paper focuses on digitally-supported research methods for an important group of cultural heritage objects, the Greek pottery, especially with figured decoration. The design, development and application of new digital methods for searching, comparing, and visually exploring these vases needs an interdisciplinary approach to effectively analyse the various features of the vases, like shape, decoration, and manufacturing techniques, and relationships between the vases. We motivate the need and opportunities by a multimodal representation of the objects, including 3D shape, material, and painting. We then illustrate a range of innovative methods for these representations, including quantified surface and capacity comparison, material analysis, image flattening from 3D objects, retrieval and comparison of shapes and paintings, and multidimensional data visualization. We also discuss challenges and future work in this area.
Implicit Maximum a Posteriori Filtering via Adaptive Optimization
Venue: arXiv cs.LG 2023
Year: 2023

Bayesian filtering approximates the true underlying behavior of a time-varying system by inverting an explicit generative model to convert noisy measurements into state estimates. This process typically requires either storage, inversion, and multiplication of large matrices or Monte Carlo estimation, neither of which are practical in high-dimensional state spaces such as the weight spaces of artificial neural networks. Here, we frame the standard Bayesian filtering problem as optimization over a time-varying objective. Instead of maintaining matrices for the filtering equations or simulating particles, we specify an optimizer that defines the Bayesian filter implicitly. In the linear-Gaussian setting, we show that every Kalman filter has an equivalent formulation using K steps of gradient descent. In the nonlinear setting, our experiments demonstrate that our framework results in filters that are effective, robust, and scalable to high-dimensional systems, comparing well against the standard toolbox of Bayesian filtering solutions. We suggest that it is easier to fine-tune an optimizer than it is to specify the correct filtering equations, making our framework an attractive option for high-dimensional filtering problems.
Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications
Venue: arXiv cs.RO 2023
Year: 2023

Human motion trajectory prediction is a very important functionality for human-robot collaboration, specifically in accompanying, guiding, or approaching tasks, but also in social robotics, self-driving vehicles, or security systems. In this paper, a novel trajectory prediction model, Social Force Generative Adversarial Network (SoFGAN), is proposed. SoFGAN uses a Generative Adversarial Network (GAN) and Social Force Model (SFM) to generate different plausible people trajectories reducing collisions in a scene. Furthermore, a Conditional Variational Autoencoder (CVAE) module is added to emphasize the destination learning. We show that our method is more accurate in making predictions in UCY or BIWI datasets than most of the current state-of-the-art models and also reduces collisions in comparison to other approaches. Through real-life experiments, we demonstrate that the model can be used in real-time without GPU's to perform good quality predictions with a low computational cost.
Détection d'objets célestes dans des images astronomiques par IA explicable
Venue: arXiv cs.CV 2023
Year: 2023

Amateur and professional astronomers can easily capture a large number of deep sky images with recent smart telescopes. However, afterwards verification is still required to check whether the celestial objects targeted are actually visible in the images produced. Depending on the magnitude of the targets, the observation conditions and the time during which the data is captured, it is possible that only stars are present in the images. In this study, we propose an approach based on explainable Artificial Intelligence to automatically detect the presence and position of captured objects. -- -- Grâce à l'apport des télescopes automatisés grand public, les astronomes amateurs et professionnels peuvent capturer facilement une grande quantité d'images du ciel profond (comme par exemple les galaxies, nébuleuses, ou amas globulaires). Néanmoins, une vérification reste nécessaire à postériori pour vérifier si les objets célestes visés sont effectivement visibles dans les images produites: cela dépend notamment de la magnitude des cibles, des conditions d'observation mais aussi de la durée pendant laquelle les données sont capturées. Dans cette étude, nous proposons une approche basée sur l'IA explicable pour détecter automatiquement la présence et la position des objets capturés.
TacFR-Gripper: A Reconfigurable Fin Ray-Based Compliant Robotic Gripper with Tactile Skin for In-Hand Manipulation
Venue: arXiv cs.RO 2023
Year: 2023

This paper introduces the TacFR-Gripper, a reconfigurable Fin Ray-based soft and compliant robotic gripper equipped with tactile skin, which can be used for dexterous in-hand manipulation tasks. This gripper can adaptively grasp objects of diverse shapes and stiffness levels. An array of Force Sensitive Resistor (FSR) sensors is embedded within the robotic finger to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. We provide theoretical analysis for gripper design, including kinematic analysis, workspace analysis, and finite element analysis to identify the relationship between the gripper's load and its deformation. Moreover, we implemented a Graph Neural Network (GNN)-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. Three physical experiments were conducted to quantify the performance of the TacFR-Gripper. These experiments aimed to i) assess the grasp success rate across various everyday objects through different configurations, ii) verify the effectiveness of tactile skin with the GNN algorithm in grasping, iii) evaluate the gripper's in-hand manipulation capabilities for object pose control. The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin with the GNN algorithm enhances grasp stability by incorporating tactile feedback during manipulations. For more details of this project, please view our website: https://sites.google.com/view/tacfr-gripper/homepage.