Tootfinder

Google announces a threat detection capability coming to Android that uses on-device AI to spot malware, analyzing apps' use of permissions and services (Sarah Perez/TechCrunch)
https://techcrunch.com/2024/05/15/goog

Google takes aim at Android malware with an AI-powered live threat detection service | TechCrunch
Google is preparing to launch a new system to help address the problem of malware on Android. Its new live threat detection service leverages Google Play

Google unveils Android 15 security features, like Theft Detection Lock, which detects motions associated with theft, and Private Space, to store sensitive data (Allison Johnson/The Verge)
https://www.theverge.com/2024/5/15/24157068/a…

Android will be able to detect if your phone has been snatched
One of Android’s newest security features can detect when your phone has been stolen out of your hands. It comes with other new security and privacy measures.

Overlapping community detection algorithms using Modularity and the cosine
Do Duy Hieu, Phan Thi Ha Duong
https://arxiv.org/abs/2403.08000 https://<…

Overlapping community detection algorithms using Modularity and the cosine
The issue of network community detection has been extensively studied across many fields. Most community detection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to multiple communities simultaneously.This paper presents two overlapping network community detection algorithms that build on the two-step approach, using the extended modularity and cosine function. The applicability of our algorithms extends to both undirected and directed graph str…

Intelligent Reflecting Surface-Enabled Anti-Detection for Secure Sensing and Communications
Beixiong Zheng, Xue Xiong, Tiantian Ma, Derrick Wing Kwan Ng, A. Lee Swindlehurst, Rui Zhang
https://arxiv.org/abs/2404.08366

Intelligent Reflecting Surface-Enabled Anti-Detection for Secure Sensing and Communications
The ever-increasing reliance on wireless communication and sensing has led to growing concerns over the vulnerability of sensitive information to unauthorized detection and interception. Traditional anti-detection methods are often inadequate, suffering from limited adaptability and diminished effectiveness against advanced detection technologies. To overcome these challenges, this article presents the intelligent reflecting surface (IRS) as a groundbreaking technology for enabling flexible ele…

This https://arxiv.org/abs/2404.03507 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects. Also, DETR-like methods using a fixed number of queries make them unsuitable for aerial datasets, which only contain tiny objects, and the numbers of instances are imbalanced between diffe…

Graph-Based DDoS Attack Detection in IoT Systems with Lossy Network
Arvin Hekmati, Bhaskar Krishnamachari
https://arxiv.org/abs/2403.09118 https://<…

Graph-Based DDoS Attack Detection in IoT Systems with Lossy Network
This study introduces a robust solution for the detection of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) systems, leveraging the capabilities of Graph Convolutional Networks (GCN). By conceptualizing IoT devices as nodes within a graph structure, we present a detection mechanism capable of operating efficiently even in lossy network environments. We introduce various graph topologies for modeling IoT networks and evaluate them for detecting tunable futuristic DDoS a…

GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
Zubair Qazi, William Shiao, Evangelos E. Papalexakis
https://arxiv.org/abs/2403.07321

GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance of detection models in identifying generated responses from ChatGPT. The dataset consists of a diverse collection of context-prompt pairs …

FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders
Soumen Basu, Mayuna Gupta, Chetan Madan, Pankaj Gupta, Chetan Arora
https://arxiv.org/abs/2403.08848

FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders
In recent years, automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultrasound sonography (US) images exhibit limited generalization, emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture disease manifestation. This study advocates for a paradigm shift towards video-based GBC detection, leveraging the inherent advantages of spa…

Change Point Detection with Copula Entropy based Two-Sample Test
Jian Ma
https://arxiv.org/abs/2403.07892 https://arxiv.org/pdf/2403.…

Change Point Detection with Copula Entropy based Two-Sample Test
Change point detection is a typical task that aim to find changes in time series and can be tackled with two-sample test. Copula Entropy is a mathematical concept for measuring statistical independence and a two-sample test based on it was introduced recently. In this paper we propose a nonparametric multivariate method for multiple change point detection with the copula entropy-based two-sample test. The single change point detection is first proposed as a group of two-sample tests on every po…

This https://arxiv.org/abs/2308.12697 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Prompt-Enhanced Software Vulnerability Detection Using ChatGPT
With the increase in software vulnerabilities that cause significant economic and social losses, automatic vulnerability detection has become essential in software development and maintenance. Recently, large language models (LLMs) like GPT have received considerable attention due to their stunning intelligence, and some studies consider using ChatGPT for vulnerability detection. However, they do not fully consider the characteristics of LLMs, since their designed questions to ChatGPT are simpl…

This https://arxiv.org/abs/2402.15281 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Neural Implicit Swept Volume Models for Fast Collision Detection
Collision detection is one of the most time-consuming operations during motion planning. Thus, there is an increasing interest in exploring machine learning techniques to speed up collision detection and sampling-based motion planning. A recent line of research focuses on utilizing neural signed distance functions of either the robot geometry or the swept volume of the robot motion. Building on this, we present a novel neural implicit swept volume model to continuously represent arbitrary motio…

Analyze 🧲⁠❌⁠🧲

AirGuard

de.seemoo.at_tracking_detection
https://github.com/seemoo-lab/AirGuard
org.woheller69.audio_analyzer_for_android
https://f-droid.org/packages/org.woheller69.audio_analyzer_for_android
de.markusfisch.android.binaryeye
https://github.com/markusfisch/BinaryEye
f.cking.software
https://github.com/Semper-Viventem/MetaRadar
com.oF2pks.classyshark3xodus
https://f-droid.org/packages/com.oF2pks.classyshark3xodus
de.k3b.android.contentproviderhelper
https://f-droid.org/packages/de.k3b.android.contentproviderhelper
com.kgurgul.cpuinfo
https://github.com/kamgurgul/cpu-info
com.android.gpstest.osmdroid
https://f-droid.org/packages/com.android.gpstest.osmdroid
fr.ralala.hexviewer
https://f-droid.org/packages/fr.ralala.hexviewer
com.hwloc.lstopo
https://f-droid.org/packages/com.hwloc.lstopo
us.spotco.malwarescanner
https://divestos.org/fdroid/official?appId=us.spotco.malwarescanner
com.oF2pks.kalturadeviceinfos
https://f-droid.org/packages/com.oF2pks.kalturadeviceinfos
com.absinthe.libchecker
https://github.com/LibChecker/LibChecker
de.k3b.android.lossless_jpg_crop
https://f-droid.org/packages/de.k3b.android.lossless_jpg_crop
com.f0x1d.logfox
https://github.com/f0x1d/logfox
com.github.muellerma.nfcreader
https://f-droid.org/packages/com.github.muellerma.nfcreader
de.tu_darmstadt.seemoo.nfcgate
https://github.com/nfcgate/nfcgate
im.nfc.nfsee
https://github.com/nfcim/nfsee
com.tomfong.simpleqr
https://github.com/tomfong/simple-qr
com.vrem.wifianalyzer
https://github.com/VREMSoftwareDevelopment/WifiAnalyzer
org.bitbatzen.wlanscanner
https://github.com/bewue/WLANScanner

JWST detection of a supernova associated with #GRB221009A without an r-process signature: https://www.nature.com/articles/s41550-024-02237-4 -> Brightest gamma-ray burst of all time came from the collapse of a massive star: https://news.northwestern.edu/stories/2024/04/brightest-gamma-ray-burst-of-all-time-came-from-the-collapse-of-a-massive-star/ - James Webb Space Telescope observations show no sign of heavy elements.

This https://arxiv.org/abs/2307.11157 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of t…

on device scam call detection is actually rlly cool, i bet thatll save a ton of ppl from being scammed if it works well

I was looking over some of the new EU AI restrictions, and notably it barred educational institutions and employers from using biometric emotion detection.
This seems fine, but #gpt4o makes this complicated. You can't turn off its emotion detection, it's not even really explicit, but it's very much there. The demo where the person breathes hard and soft is one example, but GPT-V (vi…

This https://arxiv.org/abs/2308.08491 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Entropy production and fluctuation theorems for monitored quantum systems under imperfect detection
The thermodynamic behavior of Markovian open quantum systems can be described at the level of fluctuations by using continuous monitoring approaches. However, practical applications require assessing imperfect detection schemes, where the definition of main thermodynamic quantities becomes subtle and universal fluctuation relations are unknown. Here we fill this gap by deriving a universal fluctuation relation that links entropy production in ideal and in inefficient monitoring setups. This pro…

A look at the growing number of startups offering AI deepfake detection services, with some claiming startling levels of accuracy despite untested capabilities (Washington Post)
https://wapo.st/4byxn55

Fooled by AI? These firms sell deepfake detection that’s ‘REAL 100%.’
A surge of companies now claim to offer hyper-accurate detection services, but their capabilities are largely untested.

Overlapping community detection algorithms using Modularity and the cosine
Do Duy Hieu, Phan Thi Ha Duong
https://arxiv.org/abs/2403.08000 https://<…

Overlapping community detection algorithms using Modularity and the cosine
The issue of network community detection has been extensively studied across many fields. Most community detection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to multiple communities simultaneously.This paper presents two overlapping network community detection algorithms that build on the two-step approach, using the extended modularity and cosine function. The applicability of our algorithms extends to both undirected and directed graph str…

Intelligent Reflecting Surface-Enabled Anti-Detection for Secure Sensing and Communications
Beixiong Zheng, Xue Xiong, Tiantian Ma, Derrick Wing Kwan Ng, A. Lee Swindlehurst, Rui Zhang
https://arxiv.org/abs/2404.08366

Intelligent Reflecting Surface-Enabled Anti-Detection for Secure Sensing and Communications
The ever-increasing reliance on wireless communication and sensing has led to growing concerns over the vulnerability of sensitive information to unauthorized detection and interception. Traditional anti-detection methods are often inadequate, suffering from limited adaptability and diminished effectiveness against advanced detection technologies. To overcome these challenges, this article presents the intelligent reflecting surface (IRS) as a groundbreaking technology for enabling flexible ele…

Prospects for detection of ultra high frequency gravitational waves from hyperbolic encounters with resonant cavities
Aur\'elien Barrau, Juan Garc\'ia-Bellido, Killian Martineau, Martin Teuscher
https://arxiv.org/abs/2404.08379

Prospects for detection of ultra high frequency gravitational waves from hyperbolic encounters with resonant cavities
In this brief note, we pursue the systematic investigation of possible gravitational wave sources in the gigahertz band. We focus on hyperbolic encounters of light black holes and evaluate precisely the expected signal when accounting for the detailed characteristics of haloscope experiments. Considering the GraHal setup as a benchmark, we insist on the correct signal-to-noise ratio expression taking into account the appropriate timescales. The associated maximum distance - of the order of a fe…

OTFS Channel Estimation and Detection for Channels with Very Large Delay Spread
Preety Priya, Yi Hong, Emanuele Viterbo
https://arxiv.org/abs/2404.08333 ht…

OTFS Channel Estimation and Detection for Channels with Very Large Delay Spread
In low latency applications and in general, for overspread channels, channel delay spread is a large percentage of the transmission frame duration. In this paper, we consider OTFS in an overspread channel exhibiting a delay spread that exceeds the block duration in a frame, where traditional channel estimation (CE) fails. We propose a two-stage CE method based on a delay-Doppler (DD) training frame, consisting of a dual chirp converted from time domain and a higher power pilot. The first stage …

Likely detection of magnetic field related LFQPO in the soft X-ray re-brightening of GRS~1915 105
Ling-Da Kong, Long Ji, Andrea Santangelo, Meng-Lei Zhou, Qing-Cang Shui, Shu Zhang
https://arxiv.org/abs/2403.09341

Likely detection of magnetic field related LFQPO in the soft X-ray re-brightening of GRS~1915+105
Utilizing NICER observations, we present an analysis of the soft X-ray re-brightening event of GRS 1915+105 observed in 2021. During this event, we observed the emergence of a stable, long-lasting low-frequency quasi-periodic oscillation (LFQPO) with frequencies ranging from 0.17 to 0.21 Hz. Through a careful spectral analysis, we demonstrate that a low-temperature Compton-thick gas model well characterizes the emitted radiation. By examining the spectrum and identifying numerous absorption lin…

GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
Zubair Qazi, William Shiao, Evangelos E. Papalexakis
https://arxiv.org/abs/2403.07321

GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance of detection models in identifying generated responses from ChatGPT. The dataset consists of a diverse collection of context-prompt pairs …

User seeks ideas on the application of AI in cybersecurity beyond threat detection. They invite discussion on existing uses or potential innovations. https://reddit.com/r/cybersecurity/comments/1c3zy3k/

Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection
Litao Li, Steven H. H. Ding, Andrew Walenstein, Philippe Charland, Benjamin C. M. Fung
https://arxiv.org/abs/2404.08562

Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection
Software vulnerabilities are a challenge in cybersecurity. Manual security patches are often difficult and slow to be deployed, while new vulnerabilities are created. Binary code vulnerability detection is less studied and more complex compared to source code, and this has important practical implications. Deep learning has become an efficient and powerful tool in the security domain, where it provides end-to-end and accurate prediction. Modern deep learning approaches learn the program semanti…

This https://arxiv.org/abs/2312.13936 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting
Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents an optimized parallel implementation of Leiden, a high quality community detection method, for shared memory multicore systems. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Leiden implementatio…

Detecting AI fakes is a losing battle.
Instead, we should assume everything is fake unless proven valid, using tools optimized for proving provenance. Anything else is at minimum suspect.
https://www.linkedin.com/pulse/deepfake-detection-…

Deepfake Detection, A Lost Cause
Lately, there’s been a ton of buzz about deepfakes in the news. Honestly, I’m not sure if they are going to destroy democracy and all that, but I am convinced of one thing: trying to detect them is a lost cause.

This https://arxiv.org/abs/2308.02671 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
In the realm of dijet searches in high-energy physics, a significant challenge has emerged: with experiments producing more and more data, the traditional methods of using analytic functions to describe dijet mass spectra start to fail. To address this, we suggest the application of an anomaly detection approach to eliminate less interesting background events based on event final states. This method not only bypasses the limitations of conventional background models but also significantly enhan…

Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge
Xinkun Ai, Wei Zheng, Ming Zhang, Yonghua Ding, Dalong Chen, Zhongyong Chen, Bihao Guo, Chengshuo Shen, Nengchao Wang, Zhoujun Yang, Zhipeng Chen, Yuan Pan, Biao Shen, Binjia Xiao
https://arxiv.org/abs/2404.08241<…

Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge
Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven methods, requiring extensive discharge data for training. However, future tokamaks require disruption prediction from the first shot, posing challenges of data scarcity during the early operation period. In this period disruption prediction aims to support safe exploration of operation range and accumulate necessary …

This https://arxiv.org/abs/2403.18554 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection
Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress, state-of-the-art CoSODs could be easily affected by some adversarial perturbations, leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g., concept) of the co-salient objects. In this paper, we propose a novel robustn…

Robust COVID-19 Detection in CT Images with CLIP
Li Lin, Yamini Sri Krubha, Zhenhuan Yang, Cheng Ren, Xin Wang, Shu Hu
https://arxiv.org/abs/2403.08947 htt…

Robust COVID-19 Detection in CT Images with CLIP
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data. In this work, we introduce the first lightweight detector designed to overcome these obstacles, leveraging a frozen CLIP image encoder and a trainable multilayer perception (MLP). Enhanced with Conditional Value at Risk (CVaR) for robu…

This https://arxiv.org/abs/2302.08429 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Non-Gaussianity from the Cross-correlation of the Astrophysical Gravitational Wave Background and the Cosmic Microwave Background
Since the first LIGO/Virgo detection, Gravitational Waves (GWs) have been very promising as a new complementary probe to understand our Universe. One of the next challenges of GW search is the detection and characterization of the stochastic gravitational wave background (SGWB), that is expected to open a window on the very early Universe (cosmological background) and to provide us new information on astrophysical source populations (astrophysical background). One way to characterize the SGWB a…

Optical detection of bacterial cells on stainless-steel surface with a low-magnification light microscope
Yuzhen Zhang, Zili Gao, Lili He
https://arxiv.org/abs/2403.07297

Optical detection of bacterial cells on stainless-steel surface with a low-magnification light microscope
A Rapid and cost-effective method for detecting bacterial cells on surfaces is critical to protect public health from various aspects, including food safety, clinical hygiene, and pharmacy quality. Herein, we first established an optical detection method based on a gold chip coating with 3-mercaptophenylboronic acid (3-MPBA) to capture bacterial cells, which allows for the detection and quantification of bacterial cells with a standard light microscope under low-magnification (10 fold) objectiv…

Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Fatima Ezzeddine, Mirna Saad, Omran Ayoub, Davide Andreoletti, Martin Gjoreski, Ihab Sbeity, Marc Langheinrich, Silvia Giordano
https://arxiv.org/abs/2404.06144

Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data. Such a process finds wide application in various fields, such as finance and healthcare. While the primary objective of AD is to yield high detection accuracy, the requirements of explainability and privacy are also paramount. The first ensures the transparency of the AD proces…

A robust SVM-based approach with feature selection and outliers detection for classification problems
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07753

A robust SVM-based approach with feature selection and outliers detection for classification problems
This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic ap…

Allergy detection dog is now working and we discovered that the organic “gluten-free” peppercorns we used for like two years actually are contaminated with gluten.
And so is the “gluten-free” ibuprofen I used to treat the migraines and gout that I get from gluten exposure.

Astrometric detection of exoplanets
Fabo Feng
https://arxiv.org/abs/2403.08226 https://arxiv.org/pdf/2403.08226

Astrometric detection of exoplanets
As the most ancient branch of astronomy, astrometry has been developed for thousands of years. However, it has only recently become possible to utilize astrometry for the detection of exoplanets. Gaia, an astrometric surveyor of 1 billion stars, is capable of measuring the position of stars with a precision as high as 20 $μ$as. Gaia is expected to discover more than 10,000 exoplanets by the end of its mission, surpassing the productivity of most exoplanet surveys. In this chapter, I will int…

AD-NEv : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Marcin Pietro\'n, Dominik \.Zurek, Kamil Faber, Roberto Corizzo
https://arxiv.org/abs/2404.07968

AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and non-gradient fine tuning. H…

Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication
Rodrigo Hernang\'omez, Jochen Fink, Renato L. G. Cavalcante, Zoran Utkovski, S{\l}awomir Sta\'nczak
https://arxiv.org/abs/2404.08455

Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication
In this paper, we formalize an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devise a superiorized iterative projection algorithm to approximate them. Our simulations show that this approach outperforms the detection performance of …

Topological insulators based on $p$-wave altermagnets, electrical control and detection of the altermagnetic domain wall
Motohiko Ezawa
https://arxiv.org/abs/2404.08300

Topological insulators based on $p$-wave altermagnets, electrical control and detection of the altermagnetic domain wall
We study a one-dimensional hybrid system made of a $p$-wave altermagnet and a metal possessing the orbital degree of freedom. The hybrid system is a topological insulator without the spin-orbit interaction. There emerge two edge states per one edge, because the system is mapped to a set of two copies of a topological insulator. Each copy resembles the long-range Su-Schrieffer-Heeger model but it is topologically different. Topological interface states emerge at a domain wall of the $p$-wave alt…

Compute-first optical detection for noise-resilient visual perception
Jungmin Kim, Nanfang Yu, Zongfu Yu
https://arxiv.org/abs/2403.09612 https://

Compute-first optical detection for noise-resilient visual perception
In the context of visual perception, the optical signal from a scene is transferred into the electronic domain by detectors in the form of image data, which are then processed for the extraction of visual information. In noisy and weak-signal environments such as thermal imaging for night vision applications, however, the performance of neural computing tasks faces a significant bottleneck due to the inherent degradation of data quality upon noisy detection. Here, we propose a concept of optica…

A Global Data-Driven Model for The Hippocampus and Nucleus Accumbens of Rat From The Local Field Potential Recordings (LFP)
Maedeh Sadeghi (Fault Detection and Identification), Mahdi Aliyari Shoorehdeli (Fault Detection and Identification), Shole jamali (Neuroscience Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran), Abbas Haghparast (Neuroscience Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran…

Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors
Anca Hangan, Dragos Lazea, Tudor Cioara
https://arxiv.org/abs/2403.09322

Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors
IoT devices have become indispensable components of our lives, and the advancement of AI technologies will make them even more pervasive, increasing the vulnerability to malfunctions or cyberattacks and raising privacy concerns. Encryption can mitigate these challenges; however, most existing anomaly detection techniques decrypt the data to perform the analysis, potentially undermining the encryption protection provided during transit or storage. Homomorphic encryption schemes are promising sol…

Assessment of background noise properties in time and time-frequency domains in the context of vibration-based local damage detection in real environment
Katarzyna Skowronek, Tomasz Barszcz, Jerome Antoni, Rados{\l}aw Zimroz, Agnieszka Wy{\l}oma\'nska
https://arxiv.org/abs/2403.08359…

Assessment of background noise properties in time and time-frequency domains in the context of vibration-based local damage detection in real environment
Any measurement in condition monitoring applications is associated with disturbing noise. Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the noise. This paper shares a novel perspective to the problem of local damage detection. The acquired vector of observations is considered as an additive mixture of signal of interest (SOI) and noise with strongly non-Gaussian, heavy-tailed properties, that masks the SOI. The distribution properties of the background n…

This https://arxiv.org/abs/2311.12117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Calibrating signal-to-noise ratio detection thresholds using gravitational-wave catalogs
Searching for gravitational-wave signals is a challenging and computationally intensive endeavor undertaken by multiple independent analysis pipelines. While detection depends only on observed noisy data, it is sometimes inconsistently defined in terms of source parameters that in reality are unknown, e.g., by placing a threshold on the optimal signal-to-noise ratio (SNR). We present a method to calibrate unphysical thresholds to search results by performing Bayesian inference on real observati…

This https://arxiv.org/abs/2404.01492 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge
A common practice in deep learning consists of training large neural networks on massive datasets to perform accurately for different domains and tasks. While this methodology may work well in numerous application areas, it only applies across modalities due to a larger distribution shift in data captured using different sensors. This paper focuses on the problem of adapting a large object detection model to one or multiple modalities while being efficient. To do so, we propose ModTr as an alte…

This https://arxiv.org/abs/2311.11632 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Quantum entanglement of ions for light dark matter detection
A detection scheme is explored for light dark matter, such as axion dark matter or dark photon dark matter, using a Paul ion trap system. We first demonstrate that a qubit, constructed from the ground and first excited states of vibrational modes of ions in a Paul trap, can serve as an effective sensor for weak electric fields due to its resonant excitation. As a consequence, a Paul ion trap allows us to search for weak electric fields induced by light dark matter with masses around the neV ran…

This https://arxiv.org/abs/2404.01210 has been replaced.
link: https://scholar.google.com/scholar?q=a

AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
In this paper, we present our team's submissions for SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The participants were asked to perform binary classification to identify cases of fluent overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model on hallucination detection and a Natural Language Inference (NLI) model. The most successful strategy involved creating an ensemble of these models, result…

Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication
Rodrigo Hernang\'omez, Jochen Fink, Renato L. G. Cavalcante, Zoran Utkovski, S{\l}awomir Sta\'nczak
https://arxiv.org/abs/2404.08455

Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication
In this paper, we formalize an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devise a superiorized iterative projection algorithm to approximate them. Our simulations show that this approach outperforms the detection performance of …

Google announces AI-powered scam call detection on Android, a chatty new voice mode with its Gemini Live feature, and voice and video search with Google Lens (Emma Roth/The Verge)
https://www.theverge.com/24153841/google-io-2024-ai-gemini-android-c…

Google I/O 2024: everything announced
Google made a ton of AI-related announcements during its 2024 I/O event, but it had some updates to share about Android, too.

This https://arxiv.org/abs/2403.03266 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

High-z Gamma-Ray Bursts detection by SVOM/ECLAIRs: Impact of instrumental biases on the burst measured properties
Context. Gamma Ray Bursts (GRBs) can be detected at cosmological distances and therefore can be used to study the contents and phases of the early Universe. The 4 -- 150 keV wide-field trigger camera ECLAIRs to fly on-board the Space-based multi-band Variable Object Monitor (SVOM) mission dedicated to study high-energy transient sky in synergy with multi-messenger follow-up instruments, is adapted to detect high-z GRBs. Aims. Investigating the detection capabilities of ECLAIRs for high redshift…

The post is asking about current efforts in operational technology (OT) security, specifically whether the focus is more on proactive tools and solutions or on detection, response, and mitigation. https://reddit.com/r/cybersecurity/comments/1cr7fpk/

LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
Xiangrui Cai, Yang Wang, Sihan Xu, Hao Li, Ying Zhang, Xiaojie Yuan
https://arxiv.org/abs/2403.09209

LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
Enterprises and organizations are faced with potential threats from insider employees that may lead to serious consequences. Previous studies on insider threat detection (ITD) mainly focus on detecting abnormal users or abnormal time periods (e.g., a week or a day). However, a user may have hundreds of thousands of activities in the log, and even within a day there may exist thousands of activities for a user, requiring a high investigation budget to verify abnormal users or activities given th…

A robust SVM-based approach with feature selection and outliers detection for classification problems
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07753

A robust SVM-based approach with feature selection and outliers detection for classification problems
This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic ap…

Apple releases iOS 17.5 with cross-platform detection of unwanted tracking devices, EU app downloads from websites, Apple News features like offline mode, more (Juli Clover/MacRumors)
https://www.macrumors.com/2024/05/13/apple-releases-ios-17-5/

Apple Releases iOS 17.5 With Cross-Platform Tracking Detection, EU App Downloads From Websites and More
Apple today released iOS 17.5 and iPadOS 17.5, major updates to the iOS 17 and iPadOS 17 operating system updates that came out last September. The...

CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Models
Qilin Zhou, Zhengyuan Wei, Haipeng Wang, Bo Jiang, W. K. Chan
https://arxiv.org/abs/2405.07668

Enhancing Rover Mobility Monitoring: Autoencoder-driven Anomaly Detection for Curiosity
Mielad Sabzehi, Peter Rollins
https://arxiv.org/abs/2405.07982 http…

Drift Detection: Introducing Gaussian Split Detector
Maxime Fuccellaro, Laurent Simon, Akka Zemmari
https://arxiv.org/abs/2405.08637 https://

This https://arxiv.org/abs/2401.00699 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum walk on simplicial complexes for simplicial community detection
Quantum walks have emerged as a transformative paradigm in quantum information processing and can be applied to various graph problems. This study explores discrete-time quantum walks on simplicial complexes, a higher-order generalization of graph structures. Simplicial complexes, encoding higher-order interactions through simplices, offer a richer topological representation of complex systems. Leveraging algebraic topology and discrete-time quantum walk, we present a quantum walk algorithm for…

This https://arxiv.org/abs/2308.09945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy
Diabetic retinopathy is a severe complication of diabetes that can lead to permanent blindness if not treated promptly. Early and accurate diagnosis of the disease is essential for successful treatment. This paper introduces a deep learning method for the detection and stage grading of diabetic retinopathy, using a single fundus retinal image. Our model utilizes transfer learning, employing two state-of-the-art pre-trained models as feature extractors and fine-tuning them on a new dataset. The …

This https://arxiv.org/abs/2401.03772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Boosted Dark Matter From Centaurus A and Its Detection
Dark matter can be boosted by high energy particles in astrophysical environments through elastic scattering. We study the production of boosted dark matter via scattering with electrons in the relativistic jet of the closest active galactic nucleus, Centaurus A, and its detection in the Super-Kamiokande experiment. Since there are a huge number of electrons in the jet and dark matter is extremely dense around the supermassive black hole that powers the jet, the number of boosted dark matter is…

Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography
Hika Barki, Wan-Young Chung
https://arxiv.org/abs/2404.08212 https…

Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography
Mental stress is a prevalent condition that can have negative impacts on one's health. Early detection and treatment are crucial for preventing related illnesses and maintaining overall wellness. This study presents a new method for identifying mental stress using a wearable biosensor worn in the ear. Data was gathered from 14 participants in a controlled environment using stress-inducing tasks such as memory and math tests. The raw photoplethysmography data was then processed by filtering, seg…

Internal emails show how Evolv's gun-detection AI system made its way to NYC subway stations, despite the company saying it's not designed for that environment (Georgia Gee/Wired)
https://www.wired.com/story/evolv-gun-detection-nyc-subways-emails/

LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Linmei Hu, Hongyu He, Duokang Wang, Ziwang Zhao, Yingxia Shao, Liqiang Nie
https://arxiv.org/abs/2403.07581

LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods learn post features directly by fine-tuning the pre-trained language models under the supervision of limited personality labels. This leads to inferior quality of post features and consequently affects the performance. In addition, they treat persona…

Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography
Hika Barki, Wan-Young Chung
https://arxiv.org/abs/2404.08212 https…

Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography
Mental stress is a prevalent condition that can have negative impacts on one's health. Early detection and treatment are crucial for preventing related illnesses and maintaining overall wellness. This study presents a new method for identifying mental stress using a wearable biosensor worn in the ear. Data was gathered from 14 participants in a controlled environment using stress-inducing tasks such as memory and math tests. The raw photoplethysmography data was then processed by filtering, seg…

This https://arxiv.org/abs/2303.12278 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network
Controller Area Network (CAN) is an essential networking protocol that connects multiple electronic control units (ECUs) in a vehicle. However, CAN-based in-vehicle networks (IVNs) face security risks owing to the CAN mechanisms. An adversary can sabotage a vehicle by leveraging the security risks if they can access the CAN bus. Thus, recent actions and cybersecurity regulations (e.g., UNR 155) require carmakers to implement intrusion detection systems (IDSs) in their vehicles. The IDS should d…

Open-Vocabulary Object Detection via Neighboring Region Attention Alignment
Sunyuan Qiang, Xianfei Li, Yanyan Liang, Wenlong Liao, Tao He, Pai Peng
https://arxiv.org/abs/2405.08593

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
Siqi Han, Chao Zhang, Jiaxin Lei, Qingquan Han, Yuhui Du, Anhe Wang, Shuo Bai, Milin Zhang
https://arxiv.org/abs/2404.08199

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the art…

This https://arxiv.org/abs/2304.05946 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Entanglement detection with classical deep neural networks
In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for two-qubit systems and over $90\%$ accuracy for three-qubit systems. Additionally, our approach successful…

LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Linmei Hu, Hongyu He, Duokang Wang, Ziwang Zhao, Yingxia Shao, Liqiang Nie
https://arxiv.org/abs/2403.07581

LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods learn post features directly by fine-tuning the pre-trained language models under the supervision of limited personality labels. This leads to inferior quality of post features and consequently affects the performance. In addition, they treat persona…

3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data
Xiting Zhao, S\"oren Schwertfeger
https://arxiv.org/abs/2403.06538 http…

3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data
Reflective surfaces present a persistent challenge for reliable 3D mapping and perception in robotics and autonomous systems. However, existing reflection datasets and benchmarks remain limited to sparse 2D data. This paper introduces the first large-scale 3D reflection detection dataset containing more than 50,000 aligned samples of multi-return Lidar, RGB images, and 2D/3D semantic labels across diverse indoor environments with various reflections. Textured 3D ground truth meshes enable autom…

This https://arxiv.org/abs/2403.18998 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Few-Shot Cross-System Anomaly Trace Classification for Microservice-based systems
Microservice-based systems (MSS) may experience failures in various fault categories due to their complex and dynamic nature. To effectively handle failures, AIOps tools utilize trace-based anomaly detection and root cause analysis. In this paper, we propose a novel framework for few-shot abnormal trace classification for MSS. Our framework comprises two main components: (1) Multi-Head Attention Autoencoder for constructing system-specific trace representations, which enables (2) Transformer En…

This https://arxiv.org/abs/2312.13936 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting
Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents an optimized parallel implementation of Leiden, a high quality community detection method, for shared memory multicore systems. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Leiden implementatio…

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
Siqi Han, Chao Zhang, Jiaxin Lei, Qingquan Han, Yuhui Du, Anhe Wang, Shuo Bai, Milin Zhang
https://arxiv.org/abs/2404.08199

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the art…

SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
Jiuding Yang, Hui Liu, Weidong Guo, Zhuwei Rao, Yu Xu, Di Niu
https://arxiv.org/abs/2403.07557

SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the …

Open-Vocabulary Object Detection via Neighboring Region Attention Alignment
Sunyuan Qiang, Xianfei Li, Yanyan Liang, Wenlong Liao, Tao He, Pai Peng
https://arxiv.org/abs/2405.08593

3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data
Xiting Zhao, S\"oren Schwertfeger
https://arxiv.org/abs/2403.06538 http…

3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data
Reflective surfaces present a persistent challenge for reliable 3D mapping and perception in robotics and autonomous systems. However, existing reflection datasets and benchmarks remain limited to sparse 2D data. This paper introduces the first large-scale 3D reflection detection dataset containing more than 50,000 aligned samples of multi-return Lidar, RGB images, and 2D/3D semantic labels across diverse indoor environments with various reflections. Textured 3D ground truth meshes enable autom…

SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
Jiuding Yang, Hui Liu, Weidong Guo, Zhuwei Rao, Yu Xu, Di Niu
https://arxiv.org/abs/2403.07557

SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the …

A Timely Survey on Vision Transformer for Deepfake Detection
Zhikan Wang, Zhongyao Cheng, Jiajie Xiong, Xun Xu, Tianrui Li, Bharadwaj Veeravalli, Xulei Yang
https://arxiv.org/abs/2405.08463

A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
Maria Teresa Parreira, Sukruth Gowdru Lingaraju, Adolfo Ramirez-Aristizabal, Manaswi Saha, Michael Kuniavsky, Wendy Ju
https://arxiv.org/abs/2403.06315

A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. For HRI research, the goal is often to develop generalized models. This makes domain generalization - retaining performance in different settings - a critical issue. In this study, we present a concise analysis of domain generalization in failure detection models trained on human facial expressions. Using two distinct datasets of humans reacting to videos …

A Timely Survey on Vision Transformer for Deepfake Detection
Zhikan Wang, Zhongyao Cheng, Jiajie Xiong, Xun Xu, Tianrui Li, Bharadwaj Veeravalli, Xulei Yang
https://arxiv.org/abs/2405.08463

ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Zhiwei Liu, Boyang Liu, Paul Thompson, Kailai Yang, Raghav Jain, Sophia Ananiadou
https://arxiv.org/abs/2403.06765

ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in natural language processing, particularly the emergence of large language models (LLMs), have improved the prospects of accurate misinformation detection. However, most LLM-based approaches to conspiracy theory detection focus only on binary classification and fail to account for the important relationship between mi…

Physics Sensor Based Deep Learning Fall Detection System
Zeyuan Qu, Tiange Huang, Yuxin Ji, Yongjun Li
https://arxiv.org/abs/2403.06994 https://

Physics Sensor Based Deep Learning Fall Detection System
Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embed…

GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles
Murad Mehrab Abrar, Raian Islam, Shalaka Satam, Sicong Shao, Salim Hariri, Pratik Satam
https://arxiv.org/abs/2405.08359

A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
Maria Teresa Parreira, Sukruth Gowdru Lingaraju, Adolfo Ramirez-Aristizabal, Manaswi Saha, Michael Kuniavsky, Wendy Ju
https://arxiv.org/abs/2403.06315

A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. For HRI research, the goal is often to develop generalized models. This makes domain generalization - retaining performance in different settings - a critical issue. In this study, we present a concise analysis of domain generalization in failure detection models trained on human facial expressions. Using two distinct datasets of humans reacting to videos …

TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection
Mart\'in Bay\'on-Guti\'errez, Mar\'ia Teresa Garc\'ia-Ord\'as, H\'ector Alaiz Moret\'on, Jose Aveleira-Mata, Sergio Rubio Mart\'in, Jos\'e Alberto Ben\'itez-Andrades
https://arxiv.org/abs/2405.08429

Deep Prompt Multi-task Network for Abuse Language Detection
Jian Zhu, Yuping Ruan, Jingfei Chang, Cheng Luo
https://arxiv.org/abs/2403.05268 https://

Deep Prompt Multi-task Network for Abuse Language Detection
The detection of abusive language remains a long-standing challenge with the extensive use of social networks. The detection task of abusive language suffers from limited accuracy. We argue that the existing detection methods utilize the fine-tuning technique of the pre-trained language models (PLMs) to handle downstream tasks. Hence, these methods fail to stimulate the general knowledge of the PLMs. To address the problem, we propose a novel Deep Prompt Multi-task Network (DPMN) for abuse lang…

TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection
Mart\'in Bay\'on-Guti\'errez, Mar\'ia Teresa Garc\'ia-Ord\'as, H\'ector Alaiz Moret\'on, Jose Aveleira-Mata, Sergio Rubio Mart\'in, Jos\'e Alberto Ben\'itez-Andrades
https://arxiv.org/abs/2405.08429

A Flow is a Stream of Packets: A Stream-Structured Data Approach for DDoS Detection
Raja Giryes, Lior Shafir, Avishai Wool
https://arxiv.org/abs/2405.07232

Deep Prompt Multi-task Network for Abuse Language Detection
Jian Zhu, Yuping Ruan, Jingfei Chang, Cheng Luo
https://arxiv.org/abs/2403.05268 https://

Deep Prompt Multi-task Network for Abuse Language Detection
The detection of abusive language remains a long-standing challenge with the extensive use of social networks. The detection task of abusive language suffers from limited accuracy. We argue that the existing detection methods utilize the fine-tuning technique of the pre-trained language models (PLMs) to handle downstream tasks. Hence, these methods fail to stimulate the general knowledge of the PLMs. To address the problem, we propose a novel Deep Prompt Multi-task Network (DPMN) for abuse lang…

CEASE: Collision-Evaluation-based Active Sense System for Collaborative Robotic Arms
Xian Huang, Yuanjiong Ying, Wei Dong
https://arxiv.org/abs/2403.05761 …

CEASE: Collision-Evaluation-based Active Sense System for Collaborative Robotic Arms
Collision detection via visual fences can significantly enhance the safety of collaborative robotic arms. Existing work typically performs such detection based on pre-deployed stationary cameras outside the robotic arm's workspace. These stationary cameras can only provide a restricted detection range and constrain the mobility of the robotic system. To cope with this issue, we propose an active sense method enabling a wide range of collision risk evaluation in dynamic scenarios. First, an acti…

Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, Kede Ma
https://arxiv.org/abs/2405.08487

VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Li-Ming Zhan, Bo Liu, Xiao-Ming Wu
https://arxiv.org/abs/2404.06217

VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications. While there has been a growing focus on OOD detection in visual data, the field of textual OOD detection has received less attention. Only a few attempts have been made to directly apply general OOD detection methods to natural language processing (NLP) tasks, without adequately considering the characteristics of textual data. In this paper, we delve i…

Facilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection
Xuanze Chen, Chenkai Hu, Shengbo Gong, Chengxiang Jin, Jiajun Zhou, Shanqing Yu, Qi Xuan
https://arxiv.org/abs/2405.08278

CEASE: Collision-Evaluation-based Active Sense System for Collaborative Robotic Arms
Xian Huang, Yuanjiong Ying, Wei Dong
https://arxiv.org/abs/2403.05761 …

CEASE: Collision-Evaluation-based Active Sense System for Collaborative Robotic Arms
Collision detection via visual fences can significantly enhance the safety of collaborative robotic arms. Existing work typically performs such detection based on pre-deployed stationary cameras outside the robotic arm's workspace. These stationary cameras can only provide a restricted detection range and constrain the mobility of the robotic system. To cope with this issue, we propose an active sense method enabling a wide range of collision risk evaluation in dynamic scenarios. First, an acti…

Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, Kede Ma
https://arxiv.org/abs/2405.08487

This https://arxiv.org/abs/2404.06564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encod…

SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Parisa Jamadi Khiabani, Arkaitz Zubiaga
https://arxiv.org/abs/2403.05216

SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target. Our work advances research in few-shot stance detection by introducing SocialPET, a socially informed approach to leveraging language models for the task. Our proposed approach builds on the Pattern Exploiting Training (PET) technique, which addresses c…

SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Parisa Jamadi Khiabani, Arkaitz Zubiaga
https://arxiv.org/abs/2403.05216

SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target. Our work advances research in few-shot stance detection by introducing SocialPET, a socially informed approach to leveraging language models for the task. Our proposed approach builds on the Pattern Exploiting Training (PET) technique, which addresses c…