OpenRaiser/Intern-Atlas huggingface dataset:
Challenges with the Application of Cyber Security for Airworthiness (CSA) in Real-World Contexts
Venue: arXiv cs.CR 2023
Year: 2023

The ever increasing push towards reliance upon computerised technology in commercial, general, and military aerospace brings with it an increasing amount of potential cyber hazards and attacks. Consequently, the variety of attack vectors is greater than ever. Recognized Good Practice standards such as DO 326A and ED 202A attempt to address this by providing guidelines for cyber security on in-service aircraft, though implementation work for such initiatives is still in early stages. From previous work on in service aircraft, the authors have determined that one of the key challenges is that of the retrospective application of new regulations to existing designs. This can present significant requirements for time, money, and Suitably Qualified and Experienced Personnel resource, things which are often in already limited supply in military environments. The authors have previously explored efficient ways of approaching compliance, with promising results. There is still the need to consider this retroactivity challenge in tandem with other key factors affecting the application of CSA, in order to determine any more potential mitigating actions that could lower the barrier to effective and efficient implementation of secure approaches in the air domain. This work explores the interrelated challenges surrounding real-world applications of CSA and the beginnings of how these may be overcome.
Impact Analysis of Antenna Array Geometry on Performance of Semi-blind Structured Channel Estimation for massive MIMO-OFDM systems
Venue: arXiv cs.IT 2023
Year: 2023

Channel estimation is always implemented in communication systems to overcome the effect of interference and noise. Especially, in wireless communications, this task is more challenging to improve system performance while saving resources. This paper focuses on investigating the impact of geometries of antenna arrays on the performance of structured channel estimation in massive MIMO-OFDM systems. We use Cram'er Rao Bound to analyze errors in two methods, i.e., training-based and semi-blind-based channel estimations. The simulation results show that the latter gets significantly better performance than the former. Besides, the system with Uniform Cylindrical Array outperforms the traditional Uniform Linear Array one in both estimation methods.
Graph-based Global Robot Simultaneous Localization and Mapping using Architectural Plans
Venue: arXiv cs.RO 2023
Year: 2023

In this paper, we propose a solution for graph-based global robot simultaneous localization and mapping (SLAM) using architectural plans. Before the start of the robot operation, the previously available architectural plan of the building is converted into our proposed architectural graph (A-Graph). When the robot starts its operation, it uses its onboard LIDAR and odometry to carry out an online SLAM relying on our situational graph (S-Graph), which includes both, a representation of the environment with multiple levels of abstractions, such as walls or rooms, and their relationships, as well as the robot poses with their associated keyframes. Our novel graph-to-graph matching method is used to relate the aforementioned S-Graph and A-Graph, which are aligned and merged, resulting in our novel informed Situational Graph (iS-Graph). Our iS-Graph not only provides graph-based global robot localization, but it extends the graph-based SLAM capabilities of the S-Graph by incorporating into it the prior knowledge of the environment existing in the architectural plan
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning
Venue: arXiv cs.CV 2023
Year: 2023

Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks. However, traditional aggregation-based multimodal fusion methods ignore the inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance. In this work, we propose a novel multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting. Specifically, we first capture task-related unimodal representations and the unimodal predictions from the introduced unimodal predicting task. Then the unimodal representations are aligned with the more effective one by the designed multimodal contrastive method under the supervision of the unimodal predictions. Experimental results with fused features on two image-text classification benchmarks UPMC-Food-101 and N24News show that our proposed Unimodality-Supervised MultiModal Contrastive UniS-MMC learning method outperforms current state-of-the-art multimodal methods. The detailed ablation study and analysis further demonstrate the advantage of our proposed method.
Pink-Eggs Dataset V1: A Step Toward Invasive Species Management Using Deep Learning Embedded Solutions
Venue: arXiv cs.CV 2023
Year: 2023

We introduce a novel dataset consisting of images depicting pink eggs that have been identified as Pomacea canaliculata eggs, accompanied by corresponding bounding box annotations. The purpose of this dataset is to aid researchers in the analysis of the spread of Pomacea canaliculata species by utilizing deep learning techniques, as well as supporting other investigative pursuits that require visual data pertaining to the eggs of Pomacea canaliculata. It is worth noting, however, that the identity of the eggs in question is not definitively established, as other species within the same taxonomic family have been observed to lay similar-looking eggs in regions of the Americas. Therefore, a crucial prerequisite to any decision regarding the elimination of these eggs would be to establish with certainty whether they are exclusively attributable to invasive Pomacea canaliculata or if other species are also involved. The dataset is available at https://www.kaggle.com/datasets/deeshenzhen/pinkeggs
Bézout identities and control of the heat equation
Venue: arXiv cs.SC 2023
Year: 2023

Computing analytic Bézout identities remains a difficult task, which has many applications in control theory. Flat PDE systems have cast a new light on this problem. We consider here a simple case of special interest: a rod of length $a+b$, insulated at both ends and heated at point $x=a$. The case $a=0$ is classical, the temperature of the other end $θ(b,t)$ being then a flat output, with parametrization $θ(x,t)=\cosh((b-x)(\partial/\partial t)^{1/2}θ(b,t)$. When $a$ and $b$ are integers, with $a$ odd and $b$ even, the system is flat and the flat output is obtained from the Bézout identity $f(x)\cosh(ax)+g(x)\cosh(bx)=1$, the omputation of which boils down to a Bézout identity of Chebyshev polynomials. But this form is not the most efficient and a smaller expression $f(x)=\sum_{k=1}^{n} c_{k}\cosh(kx)$ may be computed in linear time. These results are compared with an approximations by a finite system, using a classical discretization. We provide experimental computations, approximating a non rational value $r$ by a sequence of fractions $b/a$, showing that the power series for the Bézout relation seems to converge.
EXPRESSNET: An Explainable Residual Slim Network for Fingerprint Presentation Attack Detection
Venue: arXiv cs.CV 2023
Year: 2023

Presentation attack is a challenging issue that persists in the security of automatic fingerprint recognition systems. This paper proposes a novel explainable residual slim network that detects the presentation attack by representing the visual features in the input fingerprint sample. The encoder-decoder of this network along with the channel attention block converts the input sample into its heatmap representation while the modified residual convolutional neural network classifier discriminates between live and spoof fingerprints. The entire architecture of the heatmap generator block and modified ResNet classifier works together in an end-to-end manner. The performance of the proposed model is validated on benchmark liveness detection competition databases i.e. Livdet 2011, 2013, 2015, 2017, and 2019 and the classification accuracy of 96.86\%, 99.84\%, 96.45\%, 96.07\%, 96.27\% are achieved on them, respectively. The performance of the proposed model is compared with the state-of-the-art techniques, and the proposed method outperforms state-of-the-art methods in benchmark protocols of presentation attack detection in terms of classification accuracy.
About Evaluation of F1 Score for RECENT Relation Extraction System
Venue: arXiv cs.CL 2023
Year: 2023

This document contains a discussion of the F1 score evaluation used in the article 'Relation Classification with Entity Type Restriction' by Shengfei Lyu, Huanhuan Chen published on Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. The authors created a system named RECENT and claim it achieves (then) a new state-of-the-art result 75.2 (previous 74.8) on the TACRED dataset, while after correcting errors and reevaluation the final result is 65.16
Efficacy of Educational Misinformation Games
Venue: arXiv cs.CY 2023
Year: 2023

Misinformation has become a significant issue in today's society, with the proliferation of false information through various mediums such as social media and traditional news sources. The rapid spread of misinformation has made it increasingly difficult for people to separate truth from fiction, and this has the potential to cause significant harm to individuals and society as a whole. In addition, there currently exists an information gap with regard to internet education, with many schools across America not having the teaching personnel nor resources to adequately educate their students about the dangers of the internet, specifically with regard to misinformation in the political sphere. To address the dangers of misinformation, some game developers have created video games that aim to educate players on the issue and help them develop critical thinking skills. These games can be used to raise awareness about the importance of verifying information before sharing it. By doing so, they can help reduce the spread of misinformation and promote a more informed and discerning public. They can also provide players with a safe and controlled environment to practice these skills and build confidence in their ability to evaluate information. However, these existing games often suffer from various shortcomings such as failing to adequately address how misinformation specifically exploits the biases within people to be effective and rarely covering how evolving modern technologies like sophisticated chatbots and deep fakes have made individuals even more vulnerable to misinformation. The purpose of this study is to create an educational misinformation game to address this information gap and investigate its efficacy as an educational tool while also iterating on the designs for previous games in the space.
Time for Change: How Clocks Break UWB Secure Ranging
Venue: arXiv cs.CR 2023
Year: 2023

Due to its suitability for wireless ranging, Ultra-Wide Band (UWB) has gained traction over the past years. UWB chips have been integrated into consumer electronics and considered for security-relevant use cases, such as access control or contactless payments. However, several publications in the recent past have shown that it is difficult to protect the integrity of instance measurements on the physical layer. In this paper, we identify transceiver clock imperfections as a new, important parameter that has been widely ignored so far. We present Mix-Down and Stretch-and-Advance, two novel attacks against the current (IEEE 802.15.4z) and the upcoming (IEEE 802.15.4ab) UWB standard, respectively. We demonstrate Mix-Down on commercial chips and achieve distance reduction from 10 m to 0 m. For the Stretch-and-Advance attack, we show analytically that the current proposal of IEEE 802.15.4ab allows reductions of over 90 m. In order to prevent the attack, we propose and analyze an effective countermeasure.
MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
Venue: arXiv cs.DC 2023
Year: 2023

Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging, error-prone task. In this paper, we address this problem by developing MPI-RICAL, a novel data-driven, programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code. Specifically, we train a supervised language model to suggest MPI functions and their proper locations in the code on the fly. We also introduce MPICodeCorpus, the first publicly available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub. Experimental results have been done on MPICodeCorpus and more importantly, on a compiled benchmark of MPI-based parallel programs for numerical computations that represent real-world scientific applications. MPI-RICAL achieves F1 scores between 0.87-0.91 on these programs, demonstrating its accuracy in suggesting correct MPI functions at appropriate code locations.. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rical
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
Venue: arXiv cs.SD 2023
Year: 2023

In this technical report, a low-complexity deep learning system for acoustic scene classification (ASC) is presented. The proposed system comprises two main phases: (Phase I) Training a teacher network; and (Phase II) training a student network using distilled knowledge from the teacher. In the first phase, the teacher, which presents a large footprint model, is trained. After training the teacher, the embeddings, which are the feature map of the second last layer of the teacher, are extracted. In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher. Our experiments conducted on DCASE 2023 Task 1 Development dataset have fulfilled the requirement of low-complexity and achieved the best classification accuracy of 57.4%, improving DCASE baseline by 14.5%.
Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data
Venue: arXiv cs.CR 2023
Year: 2023

A Distributed Denial-of-service (DDoS) attack is a malicious attempt to disrupt the regular traffic of a targeted server, service, or network by sending a flood of traffic to overwhelm the target or its surrounding infrastructure. As technology improves, new attacks have been developed by hackers. Traditional statistical and shallow machine learning techniques can detect superficial anomalies based on shallow data and feature selection, however, these approaches cannot detect unseen DDoS attacks. In this context, we propose a reconstruction-based anomaly detection model named LSTM-Autoencoder (LSTM-AE) which combines two deep learning-based models for detecting DDoS attack anomalies. The proposed structure of long short-term memory (LSTM) networks provides units that work with each other to learn the long short-term correlation of data within a time series sequence. Autoencoders are used to identify the optimal threshold based on the reconstruction error rates evaluated on each sample across all time-series sequences. As such, a combination model LSTM-AE can not only learn delicate sub-pattern differences in attacks and benign traffic flows, but also minimize reconstructed benign traffic to obtain a lower range reconstruction error, with attacks presenting a larger reconstruction error. In this research, we trained and evaluated our proposed LSTM-AE model on reflection-based DDoS attacks (DNS, LDAP, and SNMP). The results of our experiments demonstrate that our method performs better than other state-of-the-art methods, especially for LDAP attacks, with an accuracy of over 99.
Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers
Venue: arXiv cs.LG 2023
Year: 2023

To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approximated activation functions is close to that of the original model.
Efficient Computation of General Modules for ALC Ontologies (Extended Version)
Venue: arXiv cs.AI 2023
Year: 2023

We present a method for extracting general modules for ontologies formulated in the description logic ALC. A module for an ontology is an ideally substantially smaller ontology that preserves all entailments for a user-specified set of terms. As such, it has applications such as ontology reuse and ontology analysis. Different from classical modules, general modules may use axioms not explicitly present in the input ontology, which allows for additional conciseness. So far, general modules have only been investigated for lightweight description logics. We present the first work that considers the more expressive description logic ALC. In particular, our contribution is a new method based on uniform interpolation supported by some new theoretical results. Our evaluation indicates that our general modules are often smaller than classical modules and uniform interpolants computed by the state-of-the-art, and compared with uniform interpolants, can be computed in a significantly shorter time. Moreover, our method can be used for, and in fact improves, the computation of uniform interpolants and classical modules.
Fuzzy Temporal Protoforms for the Quantitative Description of Processes in Natural Language
Venue: arXiv cs.CL 2023
Year: 2023

In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relations and temporal distances between events, among other features. Through integrating process mining techniques and fuzzy sets within the usual Data-to-Text architecture, our framework is able to extract relevant quantitative temporal as well as structural information from a process and describe it in natural language involving uncertain terms. A real use-case in the cardiology domain is presented, showing the potential of our model for providing natural language explanations addressed to domain experts.
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
Venue: arXiv cs.CV 2023
Year: 2023

Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blurry, noise, color distortion, and so on. However, most previous studies mainly focus on daytime image dehazing or partial degradations presented in nighttime hazy scenes, which may lead to unsatisfactory restoration results. In this paper, we propose an end-to-end transformer-based framework for nighttime haze removal, called NightHazeFormer. Our proposed approach consists of two stages: supervised pre-training and semi-supervised fine-tuning. During the pre-training stage, we introduce two powerful priors into the transformer decoder to generate the non-learnable prior queries, which guide the model to extract specific degradations. For the fine-tuning, we combine the generated pseudo ground truths with input real-world nighttime hazy images as paired images and feed into the synthetic domain to fine-tune the pre-trained model. This semi-supervised fine-tuning paradigm helps improve the generalization to real domain. In addition, we also propose a large-scale synthetic dataset called UNREAL-NH, to simulate the real-world nighttime haze scenarios comprehensively. Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
Stable Dinner Party Seating Arrangements
Venue: arXiv cs.GT 2023
Year: 2023

A group of $n$ agents with numerical preferences for each other are to be assigned to the $n$ seats of a dining table. We study two natural topologies:~circular (cycle) tables and panel (path) tables. For a given seating arrangement, an agent's utility is the sum of their preference values towards their (at most two) direct neighbors. An arrangement is envy-free if no agent strictly prefers someone else's seat, and it is stable if no two agents strictly prefer each other's seats. Recently, it was shown that for both paths and cycles it is NP-hard to decide whether an envy-free arrangement exists, even for symmetric binary preferences. In contrast, we show that, if agents come from a bounded number of classes, the problem is solvable in polynomial time for arbitrarily-valued possibly asymmetric preferences, including outputting an arrangement if possible. We also give simpler proofs of the previous hardness results if preferences are allowed to be asymmetric. For stability, it is known that deciding the existence of stable arrangements is NP-hard for both topologies, but only if sufficiently-many numerical values are allowed. As it turns out, even constructing unstable instances can be challenging in certain cases, e.g., binary values. We completely characterize the existence of stable arrangements based on the number of distinct values in the preference matrix and the number of agent classes. We also ask the same question for non-negative values and give an almost-complete characterization, the most interesting outstanding case being that of paths with two-valued non-negative preferences, for which we experimentally find that stable arrangements always exist and prove it under the additional constraint that agents can only swap seats when sitting at most two positions away. We moreover give a polynomial algorithm for determining a stable arrangement assuming a bounded number of classes.
Image Reconstruction using Superpixel Clustering and Tensor Completion
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different criteria for choosing the representative pixel and find that the centroid pixel performs the best. We also propose two smooth tensor completion algorithms that can effectively reconstruct different types of images from the selected pixels. Our experiments show that our superpixel-based method achieves better results than uniform sampling for various missing ratios.
UQpy v4.1: Uncertainty Quantification with Python
Venue: arXiv cs.SE 2023
Year: 2023

This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to simplify previous tightly coupled features, and improve its extensibility and modularity. To improve the robustness of UQpy, software engineering best practices were adopted. A new software development workflow significantly improved collaboration between team members, and continous integration and automated testing ensured the robustness and reliability of software performance. Continuous deployment of UQpy allowed its automated packaging and distribution in system agnostic format via multiple channels, while a Docker image enables the use of the toolbox regardless of operating system limitations.