Tootfinder

Non-contact Lung Disease Classification via OFDM-based Passive 6G ISAC Sensing
Hasan Mujtaba Buttar, Muhammad Mahboob Ur Rahman, Muhammad Wasim Nawaz, Adnan Noor Mian, Adnan Zahid, Qammer H. Abbasi
https://arxiv.org/abs/2405.09458

SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents
Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
https://arxiv.org/abs/2404.09331

SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents
Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs)…

Systematic Literature Review of EM-SCA Attacks on Encryption
Muhammad Rusyaidi Zunaidi, Asanka Sayakkara, Mark Scanlon
https://arxiv.org/abs/2402.10030 htt…

Systematic Literature Review of EM-SCA Attacks on Encryption
Cryptography is vital for data security, but cryptographic algorithms can still be vulnerable to side-channel attacks (SCAs), physical assaults exploiting power consumption and EM radiation. SCAs pose a significant threat to cryptographic integrity, compromising device keys. While literature on SCAs focuses on real-world devices, the rise of sophisticated devices necessitates fresh approaches. Electromagnetic side-channel analysis (EM-SCA) gathers information by monitoring EM radiation, capable…

Eccentricity matters: Impact of eccentricity on inferred binary black hole populations
Muhammad Zeeshan, Richard O'Shaughnessy
https://arxiv.org/abs/2404.08185

Eccentricity matters: Impact of eccentricity on inferred binary black hole populations
Gravitational waves (GW), emanating from binary black holes (BBH), encode vital information about their source. GW signals enable us to deduce key properties of the BBH population across the universe, including mass, spin, and eccentricity distribution. While the masses and spins of binary components are already recognized for their insights into formation, eccentricity stands out as a distinct and quantifiable indicator of formation and evolution. Yet, despite its significance, eccentricity is…

AI-driven, Model-Free Current Control: A Deep Symbolic Approach for Optimal Induction Machine Performance
Muhammad Usama, Yunkyung Hwang, Jaehong Kim
https://arxiv.org/abs/2405.08277

Quenched Mixing Rates for Doubly Intermittent Maps
Mubarak Muhammad, Marks Ruziboev
https://arxiv.org/abs/2404.09751 https://arxiv.or…

Quenched Mixing Rates for Doubly Intermittent Maps
We study quenched mixing rates for two classes of random interval maps characterized by the presence of two indifferent fixed points and singular points. Using a random tower construction we prove the existence of an equivariant absolutely continuous probability measures with polynomial decay of correlations.

Jasper: Scalable and Fair Multicast for Financial Exchanges in the Cloud
Muhammad Haseeb, Jinkun Geng, Ulysses Butler, Xiyu Hao, Daniel Duclos-Cavalcanti, Anirudh Sivaraman
https://arxiv.org/abs/2402.09527

Jasper: Scalable and Fair Multicast for Financial Exchanges in the Cloud
Financial exchanges have recently shown an interest in migrating to the public cloud for scalability, elasticity, and cost savings. However, financial exchanges often have strict network requirements that can be difficult to meet on the cloud. Notably, market participants (MPs) trade based on market data about different activities in the market. Exchanges often use switch multicast to disseminate market data to MPs. However, if one MP receives market data earlier than another, that MP would hav…

'I’d be a king in Somalia with this money:’ Foreign POWs on being lured to fight for Russia in Ukraine: https://benborges.xyz/2024/04/11/id-be-a.html

Error-Free Near-Optimal Validated Agreement
Pierre Civit, Muhammad Ayaz Dzulfikar, Seth Gilbert, Rachid Guerraoui, Jovan Komatovic, Manuel Vidigueira, Igor Zablotchi
https://arxiv.org/abs/2403.08374

Error-Free Near-Optimal Validated Agreement
Byzantine agreement enables n processes to agree on a common L-bit value, despite t > 0 arbitrary failures. A long line of work has been dedicated to improving the worst-case bit complexity of Byzantine agreement in synchrony. This has culminated in COOL, an error-free (deterministically secure against a computationally unbounded adversary) algorithm that achieves a near-optimal bit complexity of O(nL + n^2 log n). COOL satisfies strong validity: if all correct processes propose the same value,…

A quantum oscillator interacting with a classical oscillator
Muhammad Sajjad, Andrea Russo, Maite Arcos, Andrzej Grudka, Jonathan Oppenheim
https://arxiv.org/abs/2403.07479

A quantum oscillator interacting with a classical oscillator
We study a quantum oscillator interacting and back-reacting on a classical oscillator. This can be done consistently provided the quantum system decoheres, while the backreaction has a stochastic component which causes the classical system to undergo diffusion. Nonetheless the state of the quantum oscillator can remain pure conditioned on the trajectory of the classical oscillator. We solve the system using the classical-quantum path integral formulation, and investigate slow moving regimes of …

Dynamic FMR and magneto-optical response of hydrogenated FCC phase Fe25Pd75 thin films and micro patterned devices
Shahbaz Khan, Satyajit Sarkar, Nicolas B. Lawler, Ali Akbar, Muhammad Sabieh Anwar, Mariusz Martyniuk, K. Swaminathan Iyer, Mikhail Kostylev
https://arxiv.org/abs/2405.07753…

A Graph-Theoretical Approach to Ring Analysis: An Exploration of Dominant Metric Dimension in Compressed Zero Divisor Graphs and Its Interplay with Ring Structures
Nasir Ali, Hafiz Muhammad Afzal Siddiqui, Muhammad Imran Qureshi
https://arxiv.org/abs/2405.04934

A Graph-Theoretical Approach to Ring Analysis: An Exploration of Dominant Metric Dimension in Compressed Zero Divisor Graphs and Its Interplay with Ring Structures
The paper systematically classifies rings based on the dominant metric dimensions (Ddim) of their associated CZDG, establishing consequential bounds for the Ddim of these compressed zero-divisor graphs. The authors investigate the interplay between the ring-theoretic properties of a ring ( R ) and associated CZDG. An undirected graph consisting of vertex set ( Z(R_E)\{[0]}\ =\ R_E\{[0],[1]}), where ( R_E=\{[x]:\ x\in R\} ) and ([x]=\{y\in R:\ \text{ann}(x)=\text{ann}(y)\} ) is called a compress…

Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
Samuel Cahyawijaya, Holy Lovenia, Fajri Koto, Rifki Afina Putri, Emmanuel Dave, Jhonson Lee, Nuur Shadieq, Wawan Cenggoro, Salsabil Maulana Akbar, Muhammad Ihza Mahendra, Dea Annisayanti Putri, Bryan Wilie, Genta Indra Winata, Alham Fikri Aji, Ayu Purwarianti, Pascale Fung

Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesian indigenous languages, rendering them ineffective and inefficient in such linguistic contexts. To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes. We highlight Cendol's effectiveness across a …

ISAC-Assisted Wireless Rechargeable Sensor Networks with Multiple Mobile Charging Vehicles
Muhammad Umar Farooq Qaisar, Weijie Yuan, Paolo Bellavista, Guangjie Han, Adeel Ahmed
https://arxiv.org/abs/2405.06983

Speech Robust Bench: A Robustness Benchmark For Speech Recognition
Muhammad A. Shah, David Solans Noguero, Mikko A. Heikkila, Nicolas Kourtellis
https://arxiv.org/abs/2403.07937 <…

Speech Robust Bench: A Robustness Benchmark For Speech Recognition
As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR models to diverse corruptions. SRB is composed of 69 input perturbations which are intended to simulate various corruptions that ASR models may encounter in the physical and digital world. We use SRB to…

Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs
Erik Ostrowski, Muhammad Shafique
https://arxiv.org/abs/2403.05340 h…

Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs
When deploying neural networks in real-life situations, the size and computational effort are often the limiting factors. This is especially true in environments where big, expensive hardware is not affordable, like in embedded medical devices, where budgets are often tight. State-of-the-art proposed multiple different lightweight solutions for such use cases, mostly by changing the base model architecture, not taking the input and output resolution into consideration. In this paper, we propose…

Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems
Nenad Petrovic, Fengjunjie Pan, Krzysztof Lebioda, Vahid Zolfaghari, Sven Kirchner, Nils Purschke, Muhammad Aqib Khan, Viktor Vorobev, Alois Knoll
https://arxiv.org/abs/2404.05508…

Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems
We present a prototype of a tool leveraging the synergy of model driven engineering (MDE) and Large Language Models (LLM) for the purpose of software development process automation in the automotive industry. In this approach, the user-provided input is free form textual requirements, which are first translated to Ecore model instance representation using an LLM, which is afterwards checked for consistency using Object Constraint Language (OCL) rules. After successful consistency check, the mod…

Enhancing Holonic Architecture with Natural Language Processing for System of Systems
Muhammad Ashfaq, Ahmed R. Sadik, Tommi Mikkonen, Muhammad Waseem, Niko M akitalo
https://arxiv.org/abs/2405.05365

Enhancing Holonic Architecture with Natural Language Processing for System of Systems
The complexity and dynamic nature of System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems, termed holons. This paper proposes an innovative approach to enhance holon communication within SoS through the integration of Conversational Generative Intelligence (CGI) techniques. Our approach leverages advancements in CGI, specifically Large Language Models (LLMs), to enable holons to understand and a…

Building Trust in AI-Driven Decision Making for Cyber-Physical Systems (CPS): A Comprehensive Review
Rahul Umesh Mhapsekar, Muhammad Iftikhar Umrani, Malik Faizan, Omer Ali, Lizy Abraham
https://arxiv.org/abs/2405.06347

Effect of the Source toSubstrate Distance on Structural, Optoelectronic, and Thermoelectric Properties of Zinc Sulfide Thin Films
Asad Ur Rehman Khan, Muhammad Ramzan, Muhammad Faisal Iqbal, Muhammad Hafeez, Mohammed M. Fadhali, Hamoud H. Somaily, Muhammad Javid, Muhammad Waqas Mukhtar, Muhammad Farooq Saleem
https://arxiv.org/a…

Effect of the Source toSubstrate Distance on Structural, Optoelectronic, and Thermoelectric Properties of Zinc Sulfide Thin Films
Zinc sulfide ZnS thin films with variable structural, optical, electrical, and thermoelectric properties were obtained by changing the source to substrate SSD distance in the physical vaporthermal coating PVTC system. The films crystallized into a zinc blede cubic structure with 111 preferred orientation.

Behavior Generation with Latent Actions
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad Mahi Shafiullah, Lerrel Pinto
https://arxiv.org/abs/2403.03181

Behavior Generation with Latent Actions
Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction. A recent class of models called Behavior Transformers (BeT) addresses this by discretizing actions using k-means clustering to ca…

A Two-Level Thermal Cycling-aware Task Mapping Technique for Reliability Management in Manycore Systems
Fatemeh Hossein Khani, Omid Akbari, Muhammad Shafique
https://arxiv.org/abs/2403.06134

A Two-Level Thermal Cycling-aware Task Mapping Technique for Reliability Management in Manycore Systems
Reliability management is one of the primary concerns in manycore systems design. Different aging mechanisms such as Negative-Bias Temperature Instability (NBTI), Electromigration (EM), and thermal cycling can reduce the reliability of these systems. However, state-of-the-art works mainly focused on NBTI and EM, whereas a few works have considered the thermal cycling effect. The thermal cycling effect can significantly aggravate the systems lifetime. Moreover, the thermal effects of cores on ea…

Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings
Yassaman Ebrahimzadeh Maboud, Muhammad Adnan, Divya Mahajan, Prashant J. Nair
https://arxiv.org/abs/2404.04270

Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings
Training recommendation models pose significant challenges regarding resource utilization and performance. Prior research has proposed an approach that categorizes embeddings into popular and non-popular classes to reduce the training time for recommendation models. We observe that, even among the popular embeddings, certain embeddings undergo rapid training and exhibit minimal subsequent variation, resulting in saturation. Consequently, updates to these embeddings lack any contribution to mode…

Probing Heavy Charged Higgs Boson Using Multivariate Technique at Gamma-Gamma Collider
Ijaz Ahmed, Abdul Quddus, Jamil Muhammad, Muhammad Shoaib, Saba Shafaq
https://arxiv.org/abs/2403.20293

Probing Heavy Charged Higgs Boson Using Multivariate Technique at Gamma-Gamma Collider
The current study explores the production of charged Higgs particles through photon-photon collisions within the Two Higgs Doublet Model context, including one-loop-level scattering amplitude of Electroweak and QED radiation. The cross-section has been scanned for plane ($m_{ϕ^{0}}, \sqrt{s}$) investigating the process of $γγ\rightarrow H^{+}H^{-}$. Three particular numerical scenarios low-$m_{H}$, non-alignment, and short-cascade are employed. Hence using $h^{0}$ for low-$m_{H^{0}}$ and $H^…

A Graph-Theoretical Approach to Ring Analysis: An Exploration of Dominant Metric Dimension in Compressed Zero Divisor Graphs and Its Interplay with Ring Structures
Nasir Ali, Hafiz Muhammad Afzal Siddiqui, Muhammad Imran Qureshi
https://arxiv.org/abs/2405.04934

A Graph-Theoretical Approach to Ring Analysis: An Exploration of Dominant Metric Dimension in Compressed Zero Divisor Graphs and Its Interplay with Ring Structures
The paper systematically classifies rings based on the dominant metric dimensions (Ddim) of their associated CZDG, establishing consequential bounds for the Ddim of these compressed zero-divisor graphs. The authors investigate the interplay between the ring-theoretic properties of a ring ( R ) and associated CZDG. An undirected graph consisting of vertex set ( Z(R_E)\{[0]}\ =\ R_E\{[0],[1]}), where ( R_E=\{[x]:\ x\in R\} ) and ([x]=\{y\in R:\ \text{ann}(x)=\text{ann}(y)\} ) is called a compress…

I stayed up way too late last night watching the Ken Burms documentary about Muhammad Ali.
I’m old enough to just barely remember his middle and late career. I met him at a Macworld Expo when Apple was still doing the Apple Masters program (remember those?).
Cried tears of joy and sadness throughout.
https://

A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media
Ayaz Mehmood, Muhammad Tayyab Zamir, Muhammad Asif Ayub, Nasir Ahmad, Kashif Ahmad
https://arxiv.org/abs/2405.00903

A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media
Over the last decade, similar to other application domains, social media content has been proven very effective in disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in social media content. To fully explore the potential of social media content in disaster informatics, access to relevant content and the correct geo-location information is very critical. In this paper, we propose a three-step solution to tackling t…

Blockchain-Enhanced UAV Networks for Post-Disaster Communication: A Decentralized Flocking Approach
Sana Hafeez, Runze Cheng, Lina Mohjazi, Yao Sun, Muhammad Ali Imran
https://arxiv.org/abs/2403.04796

Blockchain-Enhanced UAV Networks for Post-Disaster Communication: A Decentralized Flocking Approach
Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios, particularly when ground infrastructure is compromised. However, efficiently coordinating and securing flocks of heterogeneous UAVs from different service providers poses significant challenges related to privacy, scalability, lightweight consensus protocols, and comprehensive cybersecurity mechanisms. This study introduces a robust blockchain-enabled framework …

Effect of the Source toSubstrate Distance on Structural, Optoelectronic, and Thermoelectric Properties of Zinc Sulfide Thin Films
Asad Ur Rehman Khan, Muhammad Ramzan, Muhammad Faisal Iqbal, Muhammad Hafeez, Mohammed M. Fadhali, Hamoud H. Somaily, Muhammad Javid, Muhammad Waqas Mukhtar, Muhammad Farooq Saleem
https://arxiv.org/a…

Effect of the Source toSubstrate Distance on Structural, Optoelectronic, and Thermoelectric Properties of Zinc Sulfide Thin Films
Zinc sulfide ZnS thin films with variable structural, optical, electrical, and thermoelectric properties were obtained by changing the source to substrate SSD distance in the physical vaporthermal coating PVTC system. The films crystallized into a zinc blede cubic structure with 111 preferred orientation.

PREDIS-MHI Thermal Data
Seun Osonuga (G2Elab-MAGE, G2ELab), Ali Chouman (G2ELab, CSTB), Muhammad-Salman Shahid (G2ELab), Frederic Wurtz (G2ELab), Benoit Delinchant (G2ELab)
https://arxiv.org/abs/2404.04307

PREDIS-MHI Thermal Data
The French Construction and Housing Code had set an obligation for tertiary buildings to reduce their final energy consumption by 40% in 2030, 50% in 2040, and 60% in 2050. According to ADEME, tertiary buildings in France account for almost a third of the energy consumed in the built environment. As such, buildings in this category could be an important lever to meet these goals necessitated by the energy transition. The availability of high-quality datasets from this sector will be a crucial e…

Factors Affecting Terahertz Emission from InGaN Quantum Wells under Ultrafast Excitation
Muhammad Farooq Saleem, Ghulam Abbas Ashraf, Muhammad Faisal Iqbal, Rashid Khan, Muhammad Javid, Tianwu Wang
https://arxiv.org/abs/2404.02398

Factors Affecting Terahertz Emission from InGaN Quantum Wells under Ultrafast Excitation
InGaN quantum wells (QWs) grown on c-plane sapphire substrate experience strain due to the lattice mismatch. The strain generates a strong piezoelectric field in QWs that contributes to THz emission under ultrafast excitation. Physical parameters such as QW width, period number, and Indium concentration can affect the strength of the piezoelectric field and result in THz emission. Experimental parameters such as pump fluence, laser energy, excitation power, pump polarization angle, and incident…

Perturbing Dynamics of Active Emulsions and Their Collectives
Muhammad Turab Ali Khan, Gaurav Gardi, Ren Hao Soon, Mingchao Zhang, Metin Sitti
https://arxiv.org/abs/2405.05889

Perturbing Dynamics of Active Emulsions and Their Collectives
Controlling fluidic flows in active droplets is crucial in developing intelligent models to understand and mimic single-celled microorganisms. Typically, these fluidic flows are affected by the interfacial dynamics of chemical agents. We found that these flows can be reconfigured by the mere presence of anisotropic solid boundary embedded within active droplets. Spontaneous fluidic flows dynamically orient an embedded magnetic cluster and the magnetic cluster, when realigned, causes these flows…

A Mean Field Game Model for Timely Computation in Edge Computing Systems
Shubham Aggarwal, Muhammad Aneeq uz Zaman, Melih Bastopcu, Sennur Ulukus, Tamer Ba\c{s}ar
https://arxiv.org/abs/2404.02898

A Mean Field Game Model for Timely Computation in Edge Computing Systems
We consider the problem of task offloading in multi-access edge computing (MEC) systems constituting $N$ devices assisted by an edge server (ES), where the devices can split task execution between a local processor and the ES. Since the local task execution and communication with the ES both consume power, each device must judiciously choose between the two. We model the problem as a large population non-cooperative game among the $N$ devices. Since computation of an equilibrium in this scenari…

A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
https://arxiv.org/abs/2404.03493

A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve …

Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case
Muhammad Asif Auyb, Muhammad Tayyab Zamir, Imran Khan, Hannia Naseem, Nasir Ahmad, Kashif Ahmad
https://arxiv.org/abs/2404.14977

Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case
This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing the water networks, such as offline and online surveys, are used. However, these surveys have several limitations, such as the limited num…

Using Stratified Sampling to Improve LIME Image Explanations
Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari, Damiano Verda
https://arxiv.org/abs/2403.17742

Using Stratified Sampling to Improve LIME Image Explanations
We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts generated by typical Monte Carlo sampling. Such artifacts are due to the undersampling of the dependent variable in the synthetic neighborhood around the image being explained, which may result in inadequate explanations due to the impossibility of fitting a linear regressor on the sampled data. We then highlight a co…

Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management
Bingzhang Wang (Joey), Zhiyu (Joey), Cai, Muhammad Monjurul Karim, Chenxi Liu, Yinhai Wang
https://arxiv.org/abs/2405.03076

Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management
The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation analytics. The complexities associated with querying large-scale multi-table databases require specialized programming expertise and labor-intensive development. Additionally, traditional analysis methods have focused mainly on numerical data, often neglecting the semantic aspects that could enhance interpretability and …

Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
Hong Jin Kang, Fabrice Harel-Canada, Muhammad Ali Gulzar, Violet Peng, Miryung Kim
https://arxiv.org/abs/2404.18881

Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
Data augmentation techniques apply transformations to existing texts to generate additional data. The transformations may produce low-quality texts, where the meaning of the text is changed and the text may even be mangled beyond human comprehension. Analyzing the synthetically generated texts and their corresponding labels is slow and demanding. To winnow out texts with incorrect labels, we develop INSPECTOR, a human-in-the-loop data inspection technique. INSPECTOR combines the strengths of pr…

🔊 #NowPlaying on fip
Adrian Younge & Ali Shaheed Muhammad:
🎵 Dans un moment d'errance (feat. Laetitia Sadier Questlove and Keyon Harrold)
#AdrianYounge #AliShaheedMuhammad
https://open.spotify.com/track/22LT1tAkMsDqa38sO7nrcK

Blockchain-Enhanced UAV Networks for Post-Disaster Communication: A Decentralized Flocking Approach
Sana Hafeez, Runze Cheng, Lina Mohjazi, Yao Sun, Muhammad Ali Imran
https://arxiv.org/abs/2403.04796

Blockchain-Enhanced UAV Networks for Post-Disaster Communication: A Decentralized Flocking Approach
Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios, particularly when ground infrastructure is compromised. However, efficiently coordinating and securing flocks of heterogeneous UAVs from different service providers poses significant challenges related to privacy, scalability, lightweight consensus protocols, and comprehensive cybersecurity mechanisms. This study introduces a robust blockchain-enabled framework …

Privacy-Aware Semantic Cache for Large Language Models
Waris GillVirginia Tech, USA, Mohamed ElidrisiCisco, USA, Pallavi KalapatapuCisco, USA, Ali AnwarUniversity of Minnesota, Minneapolis, USA, Muhammad Ali GulzarVirginia Tech, USA
https://arxiv.org/abs/2403.02694

Privacy-Aware Semantic Cache for Large Language Models
Large Language Models (LLMs) like ChatGPT, Google Bard, Claude, and Llama 2 have revolutionized natural language processing and search engine dynamics. However, these models incur exceptionally high computational costs. For instance, GPT-3 consists of 175 billion parameters and inference on these models also demands billions of floating-point operations. Caching is a natural solution to reduce LLM inference costs on repeated queries. However, existing caching methods are incapable of finding se…

Quasispins of vacancy defects and their interactions in disordered antiferromagnets
Muhammad Sedik, Shijun Sun, Arthur P. Ramirez, Sergey Syzranov
https://arxiv.org/abs/2404.05845

Quasispins of vacancy defects and their interactions in disordered antiferromagnets
Vacancy defects in disordered magnetic materials are known to act as effective spins, ``quasispins'', in response to an external magnetic field. In the dilute limit, the contributions of such ``quasispins'' to the magnetic susceptibility $χ_\text{vac}(T)\propto N_\text{vac}/T$ are singular in the limit of low temperatures $T$ and match those of free spins. With increasing the density of vacancies, their interactions may become essential. Motivated by frustrated and quasi-one-dimensional magnet…

Prioritizing Software Requirements Using Large Language Models
Malik Abdul Sami, Zeeshan Rasheed, Muhammad Waseem, Zheying Zhang, Tomas Herda, Pekka Abrahamsson
https://arxiv.org/abs/2405.01564

Prioritizing Software Requirements Using Large Language Models
Large Language Models (LLMs) are revolutionizing Software Engineering (SE) by introducing innovative methods for tasks such as collecting requirements, designing software, generating code, and creating test cases, among others. This article focuses on requirements engineering, typically seen as the initial phase of software development that involves multiple system stakeholders. Despite its key role, the challenge of identifying requirements and satisfying all stakeholders within time and budge…

Renormalisation of postquantum-classical gravity
Andrzej Grudka, Jonathan Oppenheim, Andrea Russo, Muhammad Sajjad
https://arxiv.org/abs/2402.17844 https:/…

Renormalisation of postquantum-classical gravity
One of the obstacles to reconciling quantum theory with general relativity, is constructing a theory which is both consistent with observation, and and gives finite answers at high energy, so that the theory holds at arbitrarily short distances. Quantum field theory achieves this through the process of renormalisation, but famously, perturbative quantum gravity fails to be renormalisable. Recently, an alternative to quantum gravity has been proposed, in which the geometry of spacetime is taken …

Dataset Fairness: Achievable Fairness on Your Data With Utility Guarantees
Muhammad Faaiz Taufiq, Jean-Francois Ton, Yang Liu
https://arxiv.org/abs/2402.17106

Dataset Fairness: Achievable Fairness on Your Data With Utility Guarantees
In machine learning fairness, training models which minimize disparity across different sensitive groups often leads to diminished accuracy, a phenomenon known as the fairness-accuracy trade-off. The severity of this trade-off fundamentally depends on dataset characteristics such as dataset imbalances or biases. Therefore using a uniform fairness requirement across datasets remains questionable and can often lead to models with substantially low utility. To address this, we present a computatio…

Egalitarian Price of Fairness for Indivisible Goods
Karen Frilya CelineNational University of Singapore, Muhammad Ayaz DzulfikarNational University of Singapore, Ivan Adrian KoswaraNational University of Singapore
https://arxiv.org/abs/2402.16145

Egalitarian Price of Fairness for Indivisible Goods
In the context of fair division, the concept of price of fairness has been introduced to quantify the loss of welfare when we have to satisfy some fairness condition. In other words, it is the price we have to pay to guarantee fairness. Various settings of fair division have been considered previously; we extend to the setting of indivisible goods by using egalitarian welfare as the welfare measure, instead of the commonly used utilitarian welfare. We provide lower and upper bounds for various …

Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack
Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Fakhreddine Zayer, Jorge Dias, Muhammad Shafique
https://arxiv.org/abs/2404.03325

Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack
Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environment…

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology
Erin E. Gill, Baofeng Jia, Carmen Lia Murall, Rapha\"el Poujol, Muhammad Zohaib Anwar, Nithu Sara John, Justin Richardsson, Ashley Hobb, Abayomi S. Olabode, Alexandru Lepsa, Ana T. Duggan, Andrea D. Tyler, Arnaud N'Guessan, Atul Kachru, Brandon Chan, Catherine Yoshida, Christina K. Yung, David Bujold, Dusan Andric, Edmund Su, Emma J. Griffiths, Gary Van Domse…

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pi…

Perturbed $f(R)$ gravity coupled with neutrinos: exploring cosmological implications
Muhammad Yarahmadi, Amin Salehi, Kazuharu Bamba
https://arxiv.org/abs/2402.18841

Perturbed $f(R)$ gravity coupled with neutrinos: exploring cosmological implications
We conduct a thorough examination of cosmological parameters within the context of $f(R)$ gravity coupled with neutrinos, leveraging a diverse array of observational datasets, including Cosmic Microwave Background (CMB), Cosmic Chronometers (CC), Baryon Acoustic Oscillations (BAO), and Pantheon supernova data. Our analysis unveils compelling constraints on pivotal parameters such as the sum of neutrino masses ($\sum m_ν$), the interaction strength parameter ($Γ$), sound speed ($c_s$), Jean's …

Towards Measuring and Modeling "Culture" in LLMs: A Survey
Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Ashutosh Dwivedi, Alham Fikri Aji, Jacki O'Neill, Ashutosh Modi, Monojit Choudhury
https://arxiv.org/abs/2403.15412

Towards Measuring and Modeling "Culture" in LLMs: A Survey
We present a survey of 39 recent papers that aim to study cultural representation and inclusion in large language models. We observe that none of the studies define "culture," which is a complex, multifaceted concept; instead, they probe the models on some specially designed datasets which represent certain aspects of "culture." We call these aspects the proxies of cultures, and organize them across three dimensions of demographic, semantic and linguistic-cultural interaction proxies. We also c…

Fusing Depthwise and Pointwise Convolutions for Efficient Inference on GPUs
Fareed Qararyah, Muhammad Waqar Azhar, Mohammad Ali Maleki, Pedro Trancoso
https://arxiv.org/abs/2404.19331

Fusing Depthwise and Pointwise Convolutions for Efficient Inference on GPUs
Depthwise and pointwise convolutions have fewer parameters and perform fewer operations than standard convolutions. As a result, they have become increasingly used in various compact DNNs, including convolutional neural networks (CNNs) and vision transformers (ViTs). However, they have a lower compute-to-memory-access ratio than standard convolutions, making their memory accesses often the performance bottleneck. This paper explores fusing depthwise and pointwise convolutions to overcome the me…

DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
Abhieet Parida, Daniel Capellan-Martin, Sara Atito, Muhammad Awais, Maria J. Ledesma-Carbayo, Marius G. Linguraru, Syed Muhammad Anwar
https://arxiv.org/abs/2402.15534

DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on radiology reads and supervised learning, entail the cumbersome requirement of high quality annotated training data. To address this challenge, self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks, rep…

Vaporization dynamics of a super-heated water-in-oil droplet: modeling and numerical solution
Muhammad Saeed Saleem, Michel Versluis, Guillaume Lajoinie
https://arxiv.org/abs/2404.17457

Vaporization dynamics of a super-heated water-in-oil droplet: modeling and numerical solution
The study of vapor bubble growth following droplet vaporization in a superheated liquid involves research areas such as hydrodynamics, heat transfer, mass transfer, and thermodynamics. The interplay between these multiscale aspects is strongly dependent on the geometry, the thermodynamic response, and the local physical properties of the system. To understand the role of each aspect of this complex mechanism we model super-heated droplet vaporization by coupling the equation of motion for bubbl…

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan
https://arxiv.org/abs/2404.14632

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addresses both single-device and distributed pipeline and tensor model parallel scenarios, latter being addressed for the first time. The search optimized accelerators for training relevant metrics such as throughput/TDP under a fixed area and power constraints. However, with the proliferation of specialized architectures…

QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management
Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya
https://arxiv.org/abs/2405.01021

QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management
Quantum cloud computing is an emerging computing paradigm that allows seamless access to quantum hardware as cloud-based services. However, effective use of quantum resources is challenging and necessitates robust simulation frameworks for effective resource management design and evaluation. To address this need, we proposed QSimPy, a novel discrete-event simulation framework designed with the main focus of facilitating learning-centric approaches for quantum resource management problems in clo…

FedQNN: Federated Learning using Quantum Neural Networks
Nouhaila Innan, Muhammad Al-Zafar Khan, Alberto Marchisio, Muhammad Shafique, Mohamed Bennai
https://arxiv.org/abs/2403.10861

FedQNN: Federated Learning using Quantum Neural Networks
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge solution, integrating the singular characteristics of QML with the principles of classical federated l…

Cybersecurity for Modern Smart Grid against Emerging Threats
Daisuke Mashima, Yao Chen, Muhammad M. Roomi, Subhash Lakshminarayana, Deming Chen
https://arxiv.org/abs/2404.04466

Cybersecurity for Modern Smart Grid against Emerging Threats
Smart Grid is a power grid system that uses digital communication technologies. By deploying intelligent devices throughout the power grid infrastructure,from power generation to consumption, and enabling communication among them, it revolutionizes the modern power grid industry with increased efficiency, reliability, and availability. However, reliance on information and communication technologies has also made the smart grids exposed to new vulnerabilities and complications that may negativel…

Zak-OTFS for Integration of Sensing and Communication
Muhammad Ubadah, Saif Khan Mohammed, Ronny Hadani, Shachar Kons, Ananthanarayanan Chockalingam, Robert Calderbank
https://arxiv.org/abs/2404.04182

Zak-OTFS for Integration of Sensing and Communication
The Zak-OTFS input/output (I/O) relation is predictable and non-fading when the delay and Doppler periods are greater than the effective channel delay and Doppler spreads, a condition which we refer to as the crystallization condition. The filter taps can simply be read off from the response to a single Zak-OTFS point (impulse) pulsone waveform, and the I/O relation can be reconstructed for a sampled system that operates under finite duration and bandwidth constraints. Predictability opens up t…

Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack
Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Fakhreddine Zayer, Jorge Dias, Muhammad Shafique
https://arxiv.org/abs/2404.03325

Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack
Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environment…

Pilot Contamination in Massive MIMO Systems: Challenges and Future Prospects
Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed
https://arxiv.org/abs/2404.19238

Pilot Contamination in Massive MIMO Systems: Challenges and Future Prospects
Massive multiple input multiple output (M-MIMO) technology plays a pivotal role in fifth-generation (5G) and beyond communication systems, offering a wide range of benefits, from increased spectral efficiency (SE) to enhanced energy efficiency and higher reliability. However, these advantages are contingent upon precise channel state information (CSI) availability at the base station (BS). Ensuring precise CSI is challenging due to the constrained size of the coherence interval and the resultin…

A Methodology for Improving Accuracy of Embedded Spiking Neural Networks through Kernel Size Scaling
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
https://arxiv.org/abs/2404.01685

A Methodology for Improving Accuracy of Embedded Spiking Neural Networks through Kernel Size Scaling
Spiking Neural Networks (SNNs) can offer ultra low power/ energy consumption for machine learning-based applications due to their sparse spike-based operations. Currently, most of the SNN architectures need a significantly larger model size to achieve higher accuracy, which is not suitable for resource-constrained embedded applications. Therefore, developing SNNs that can achieve high accuracy with acceptable memory footprint is highly needed. Toward this, we propose a novel methodology that im…

On the solitary wave configurations of nonlinear Schr\"odinger equation under the effect of L\'evy noise
Hina Zulfiqar, Shenglan Yuan, Muhammad Shoaib Saleem
https://arxiv.org/abs/2405.00939

On the solitary wave configurations of nonlinear Schrödinger equation under the effect of Lévy noise
This study aims to examine the effect of Lévy noise on the solutions of the nonlinear Schrödinger equation. An improved diversity of stochastic solutions is instinctively located discretely on certain conditions by applying the generalized Kudryashov method. Moreover, the dynamical behaviors of these exact results of the nonlinear Schrödinger equation are interpreted in the context of the effect of Lévy noise. Even mathematical evaluations have been conducted and presented.

Carbon Filter: Real-time Alert Triage Using Large Scale Clustering and Fast Search
Jonathan Oliver, Raghav Batta, Adam Bates, Muhammad Adil Inam, Shelly Mehta, Shugao Xia
https://arxiv.org/abs/2405.04691

Carbon Filter: Real-time Alert Triage Using Large Scale Clustering and Fast Search
"Alert fatigue" is one of the biggest challenges faced by the Security Operations Center (SOC) today, with analysts spending more than half of their time reviewing false alerts. Endpoint detection products raise alerts by pattern matching on event telemetry against behavioral rules that describe potentially malicious behavior, but can suffer from high false positives that distract from actual attacks. While alert triage techniques based on data provenance may show promise, these techniques can …

Decoding Driver Takeover Behaviour in Conditional Automation with Immersive Virtual Reality
Muhammad Sajjad Ansar, Bilal Farooq
https://arxiv.org/abs/2402.16046

Decoding Driver Takeover Behaviour in Conditional Automation with Immersive Virtual Reality
The safe transition from conditional automation to manual driving control is significantly intertwined with the vehicle's lateral and longitudinal dynamics. The transition may occur as a result of a system-initiated mandatory takeover (MTOR) or as a driver-initiated discretionary takeover (DTOR). In either condition, the takeover process entails differing cognitive demands and may affect the driving behaviour differently. This study analyzes driving stability and perceived mental workload in 30…

An Overview of Intelligent Meta-surfaces for 6G and Beyond: Opportunities, Trends, and Challenges
Mayur Katwe, Aryan Kaushik, Lina Mohjazi, Mohammad Abualhayja'a, Davide Dardari, Keshav Singh, Muhammad Ali Imran, M. Majid Butt, Octavia A. Dobre
https://arxiv.org/abs/2405.03356

An Overview of Intelligent Meta-surfaces for 6G and Beyond: Opportunities, Trends, and Challenges
With the impending arrival of the sixth generation (6G) of wireless communication technology, the telecommunications landscape is poised for another revolutionary transformation. At the forefront of this evolution are intelligent meta-surfaces (IS), emerging as a disruptive physical layer technology with the potential to redefine the capabilities and performance metrics of future wireless networks. As 6G evolves from concept to reality, industry stakeholders, standards organizations, and regula…

Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation
Muhammad Waseem, Aakash Ahmad, Peng Liang, Muhammad Azeem Akbar, Arif Ali Khan, Iftikhar Ahmad, Manu Set\"al\"a, Tommi Mikkonen
https://arxiv.org/abs/2403.12980

Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation
Containerization in a multi-cloud environment facilitates workload portability and optimized resource utilization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of container utilization in m…

Small Language Models Need Strong Verifiers to Self-Correct Reasoning
Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang
https://arxiv.org/abs/2404.17140

Small Language Models Need Strong Verifiers to Self-Correct Reasoning
Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors. This work explores whether smaller-size (<= 13B) language models (LMs) have the ability of self-correction on reasoning tasks with minimal inputs from stronger LMs. We propose a novel pipeline that prompts smaller LMs to collect self-correction data that supports the training of self-ref…

AI-powered Code Review with LLMs: Early Results
Zeeshan Rasheed, Malik Abdul Sami, Muhammad Waseem, Kai-Kristian Kemell, Xiaofeng Wang, Anh Nguyen, Kari Syst\"a, Pekka Abrahamsson
https://arxiv.org/abs/2404.18496

AI-powered Code Review with LLMs: Early Results
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static c…

Enhancing Data Integrity and Traceability in Industry Cyber Physical Systems (ICPS) through Blockchain Technology: A Comprehensive Approach
Mohammad Ikbal Hossain, Dr. Tanja Steigner, Muhammad Imam Hussain, Afroja Akther
https://arxiv.org/abs/2405.04837

Enhancing Data Integrity and Traceability in Industry Cyber Physical Systems (ICPS) through Blockchain Technology: A Comprehensive Approach
Blockchain technology, heralded as a transformative innovation, has far-reaching implications beyond its initial application in cryptocurrencies. This study explores the potential of blockchain in enhancing data integrity and traceability within Industry Cyber-Physical Systems (ICPS), a crucial aspect in the era of Industry 4.0. ICPS, integrating computational and physical components, is pivotal in managing critical infrastructure like manufacturing, power grids, and transportation networks. Ho…

Impact of JLab data on the determination of GPDs at zero skewness and new insights from transition form factors $ N\rightarrow \Delta $
The MMGPDs Collaboration, Muhammad Goharipour, Hadi Hashamipour, Fatemeh Irani, K. Azizi
https://arxiv.org/abs/2403.19384

Impact of JLab data on the determination of GPDs at zero skewness and new insights from transition form factors $ N\rightarrow Δ$
It is well established now that the generalized parton distributions (GPDs) at zero skewness are playing important roles in some physical process such as elastic electron-nucleon scattering, elastic (anti)neutrino-nucleon scattering, and wide-angle Compton scattering (WACS) via various types of form factors (FFs). In this study, we are going to utilize the recent JLab measurements of the elastic electron-nucleon scattering reduced cross-section, namely GMp12, as a touchstone to unravel the tens…

A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
Amarjeet Kumar, Hongxu Jiang, Muhammad Imran, Cyndi Valdes, Gabriela Leon, Dahyun Kang, Parvathi Nataraj, Yuyin Zhou, Michael D. Weiss, Wei Shao
https://arxiv.org/abs/2405.00130

A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, which have high in-plane but low through-plane resolution, is a relatively unexplored challenge. While applying 2D models to individual slices of a 2.5D image is feasible, it fails to capture the spatial relationships between slices. On the other hand, 3D models…

On the solitary wave configurations of nonlinear Schr\"odinger equation under the effect of L\'evy noise
Hina Zulfiqar, Shenglan Yuan, Muhammad Shoaib Saleem
https://arxiv.org/abs/2405.00939

On the solitary wave configurations of nonlinear Schrödinger equation under the effect of Lévy noise
This study aims to examine the effect of Lévy noise on the solutions of the nonlinear Schrödinger equation. An improved diversity of stochastic solutions is instinctively located discretely on certain conditions by applying the generalized Kudryashov method. Moreover, the dynamical behaviors of these exact results of the nonlinear Schrödinger equation are interpreted in the context of the effect of Lévy noise. Even mathematical evaluations have been conducted and presented.

Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation
Muhammad Waseem, Aakash Ahmad, Peng Liang, Muhammad Azeem Akbar, Arif Ali Khan, Iftikhar Ahmad, Manu Set\"al\"a, Tommi Mikkonen
https://arxiv.org/abs/2403.12980

Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation
Containerization in a multi-cloud environment facilitates workload portability and optimized resource utilization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of container utilization in m…

Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study
Muhammad Rizwan, Jure Dem\v{s}ar
https://arxiv.org/abs/2404.17183

Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study
Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prevalence and frequency of social anxiety symptoms taken from Mayo Clinic…

An Investigation into Misuse of Java Security APIs by Large Language Models
Zahra Mousavi, Chadni Islam, Kristen Moore, Alsharif Abuadbba, Muhammad Ali Babar
https://arxiv.org/abs/2404.03823

An Investigation into Misuse of Java Security APIs by Large Language Models
The increasing trend of using Large Language Models (LLMs) for code generation raises the question of their capability to generate trustworthy code. While many researchers are exploring the utility of code generation for uncovering software vulnerabilities, one crucial but often overlooked aspect is the security Application Programming Interfaces (APIs). APIs play an integral role in upholding software security, yet effectively integrating security APIs presents substantial challenges. This lea…

Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models
Abdul Wahab Ziaullah, Ferda Ofli, Muhammad Imran
https://arxiv.org/abs/2404.14432

Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models
Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies. In this paper, we explore a potential application of Large Language Models (LLMs) to monitor the status of CIFs affected by natural disasters through information disseminated in social media networks. To this end, we analyze social media data from two disaster events in two different countries to identify reported i…

Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings
Junbiao Pang, Muhammad Ayub Sabir, Zhuyun Wang, Anjing Hu, Xue Yang, Haitao Yu, Qingming Huang
https://arxiv.org/abs/2404.11844

Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings
In our urban life, Illegal Driver Substitution (IDS) activity for a taxi is a grave unlawful activity in the taxi industry, possibly causing severe traffic accidents and painful social repercussions. Currently, the IDS activity is manually supervised by law enforcers, i.e., law enforcers empirically choose a taxi and inspect it. The pressing problem of this scheme is the dilemma between the limited number of law-enforcers and the large volume of taxis. In this paper, motivated by this problem, …

Revisiting Crossflow-Based Stabilization in Channel Flows
Muhammad Abdullah, George Ilhwan Park
https://arxiv.org/abs/2404.14725 https://

Revisiting Crossflow-Based Stabilization in Channel Flows
Stabilization schemes in wall-bounded flows often invoke fluid transpiration through porous boundaries. While these have been extensively validated for external flows, their efficacy in channels, particularly from the standpoint of non-modal perturbations, is yet to be demonstrated. Here, we show that crossflow strengths previously considered ``ideal'' for optimizing stability in channels in fact admit strong non-modal energy amplification. We begin by supplementing existing modal calculations …

Mobile Edge Computing
Sohaib Ahmed, Hassan Khalid, Muhammad Hamza, Danyal Farhat
https://arxiv.org/abs/2404.17944 https://arxiv.org/p…

Mobile Edge Computing
Mobile Edge Computing (MEC) has emerged as a solution to the high latency and suboptimal Quality of Experience (QoE) associated with Mobile Cloud Computing (MCC). By processing data near the source, MEC reduces the need to send information to distant data centers, resulting in faster response times and lower latency. This paper explores the differences between MEC and traditional cloud computing, emphasizing architecture, data flow, and resource allocation. Key technologies like Network Functio…

Calculation of Femur Caput Collum Diaphyseal angle for X-Rays images using Semantic Segmentation
Deepak Bhatia, Muhammad Abdullah, Anne Querfurth, Mahdi Mantash
https://arxiv.org/abs/2404.17083

Calculation of Femur Caput Collum Diaphyseal angle for X-Rays images using Semantic Segmentation
This paper investigates the use of deep learning approaches to estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images. The CCD angle is an important measurement in the diagnosis of hip problems, and correct prediction can help in the planning of surgical procedures. Manual measurement of this angle, on the other hand, can be time-intensive and vulnerable to inter-observer variability. In this paper, we present a deep-learning algorithm that can reliably estimate the femur CCD …

Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections
Muhammad Faris, Mario Zanon, Paolo Falcone
https://arxiv.org/abs/2404.14048

Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections
In this paper, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs). The main objective is to have a safe and optimal crossing order for vehicles approaching unsignalized intersections. This problem results in a mixed-integer quadratic programming (MIQP) formulation which is unsuitable for real-time applications. Therefore, we propose a computationally tractable optimization-based heuristic that monitors plato…

Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings
Junbiao Pang, Muhammad Ayub Sabir, Zhuyun Wang, Anjing Hu, Xue Yang, Haitao Yu, Qingming Huang
https://arxiv.org/abs/2404.11844

Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings
In our urban life, Illegal Driver Substitution (IDS) activity for a taxi is a grave unlawful activity in the taxi industry, possibly causing severe traffic accidents and painful social repercussions. Currently, the IDS activity is manually supervised by law enforcers, i.e., law enforcers empirically choose a taxi and inspect it. The pressing problem of this scheme is the dilemma between the limited number of law-enforcers and the large volume of taxis. In this paper, motivated by this problem, …

Massive MIMO CSI Feedback using Channel Prediction: How to Avoid Machine Learning at UE?
Muhammad Karam Shehzad, Luca Rose, Mohamad Assaad
https://arxiv.org/abs/2403.13363

Massive MIMO CSI Feedback using Channel Prediction: How to Avoid Machine Learning at UE?
In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered…

Impact of Monoatomic Vacancies in 2D Materials on the Performance of Magnetic Tunnel Junction Devices: Insights from Configurations and Interface Interactions
Halimah Harfah, Yusuf Wicaksono, Gagus K. Sunnardianto, Muhammad Aziz Majidi, Koichi Kusakabe
https://arxiv.org/abs/2404.02586

Impact of Monoatomic Vacancies in 2D Materials on the Performance of Magnetic Tunnel Junction Devices: Insights from Configurations and Interface Interactions
We investigate the impact of monoatomic vacancies in 2D materials on the performance of magnetic tunnel junction (MTJ) devices using first-principles calculations within Density Functional Theory (DFT). Specifically, we analyze the influence on hexagonal boron nitride (hBN) with various layer configurations, uncovering distinct transmission probability patterns. Transmission calculations were conducted using the Landauer-Büttiker formula employing the Non-Equilibrium Green's Function (NEGF) me…

PALO: A Polyglot Large Multimodal Model for 5B People
Muhammad Maaz, Hanoona Rasheed, Abdelrahman Shaker, Salman Khan, Hisham Cholakal, Rao M. Anwer, Tim Baldwin, Michael Felsberg, Fahad S. Khan
https://arxiv.org/abs/2402.14818

PALO: A Polyglot Large Multimodal Model for 5B People
In pursuit of more inclusive Vision-Language Models (VLMs), this study introduces a Large Multilingual Multimodal Model called \textsc{Palo}. \textsc{Palo} offers visual reasoning capabilities in 10 major languages, including English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese, that span a total of $\sim$5B people (65\% of the world population). Our approach involves a semi-automated translation approach to adapt the multimodal instruction dataset from English…

LogSD: Detecting Anomalies from System Logs through Self-supervised Learning and Frequency-based Masking
Yongzheng Xie, Hongyu Zhang, Muhammad Ali Babar
https://arxiv.org/abs/2404.11294

LogSD: Detecting Anomalies from System Logs through Self-supervised Learning and Frequency-based Masking
Log analysis is one of the main techniques that engineers use for troubleshooting large-scale software systems. Over the years, many supervised, semi-supervised, and unsupervised log analysis methods have been proposed to detect system anomalies by analyzing system logs. Among these, semi-supervised methods have garnered increasing attention as they strike a balance between relaxed labeled data requirements and optimal detection performance, contrasting with their supervised and unsupervised co…

Impact of Monoatomic Vacancies in 2D Materials on the Performance of Magnetic Tunnel Junction Devices: Insights from Configurations and Interface Interactions
Halimah Harfah, Yusuf Wicaksono, Gagus K. Sunnardianto, Muhammad Aziz Majidi, Koichi Kusakabe
https://arxiv.org/abs/2404.02586

Impact of Monoatomic Vacancies in 2D Materials on the Performance of Magnetic Tunnel Junction Devices: Insights from Configurations and Interface Interactions
We investigate the impact of monoatomic vacancies in 2D materials on the performance of magnetic tunnel junction (MTJ) devices using first-principles calculations within Density Functional Theory (DFT). Specifically, we analyze the influence on hexagonal boron nitride (hBN) with various layer configurations, uncovering distinct transmission probability patterns. Transmission calculations were conducted using the Landauer-Büttiker formula employing the Non-Equilibrium Green's Function (NEGF) me…

Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals
Israel Jesus Santos Filho, Muhammad Mahboob Ur Rahman, Taous-Meriem Laleg-Kirati, Tareq Al-Naffouri
https://arxiv.org/abs/2402.14565

Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals
We present for the first time a novel method that utilizes the chest movement-modulated radio signals for non-contact acquisition of the photoplethysmography (PPG) signal. Under the proposed method, a software-defined radio (SDR) exposes the chest of a subject sitting nearby to an orthogonal frequency division multiplexing signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected off the chest. This way, we …

Seer: Predictive Runtime Kernel Selection for Irregular Problems
Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud
https://arxiv.org/abs/2403.17017

Seer: Predictive Runtime Kernel Selection for Irregular Problems
Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the best kernel to map fine-grained irregular parallelism to a GPU. We instead propose Seer, an abstraction for producing a simple, reproduceable, and understandable decision tree selector model which performs runtime kernel selection for irregular workloads. To showcase our framework, we conduct a case study in Sparse Matrix Vector Multiplicati…

Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset
Mohamed Elmanna, Ahmed Elsafty, Yomna Ahmed, Muhammad Rushdi, Ahmed Morsy
https://arxiv.org/abs/2403.18468

Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset
Digital pathology has recently been revolutionized by advancements in artificial intelligence, deep learning, and high-performance computing. With its advanced tools, digital pathology can help improve and speed up the diagnostic process, reduce human errors, and streamline the reporting step. In this paper, we report a new large red blood cell (RBC) image dataset and propose a two-stage deep learning framework for RBC image segmentation and classification. The dataset is a highly diverse datas…

Massive MIMO CSI Feedback using Channel Prediction: How to Avoid Machine Learning at UE?
Muhammad Karam Shehzad, Luca Rose, Mohamad Assaad
https://arxiv.org/abs/2403.13363

Massive MIMO CSI Feedback using Channel Prediction: How to Avoid Machine Learning at UE?
In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered…

Merchants of Vulnerabilities: How Bug Bounty Programs Benefit Software Vendors
Esther Gal-Or, Muhammad Zia Hydari, Rahul Telang
https://arxiv.org/abs/2404.17497

Merchants of Vulnerabilities: How Bug Bounty Programs Benefit Software Vendors
Software vulnerabilities enable exploitation by malicious hackers, compromising systems and data security. This paper examines bug bounty programs (BBPs) that incentivize ethical hackers to discover and responsibly disclose vulnerabilities to software vendors. Using game-theoretic models, we capture the strategic interactions between software vendors, ethical hackers, and malicious hackers. First, our analysis shows that software vendors can increase expected profits by participating in BBPs, e…

Textual analysis of End User License Agreement for red-flagging potentially malicious software
Behraj Khan, Tahir Syed, Zeshan Khan, Muhammad Rafi
https://arxiv.org/abs/2403.09715

Textual analysis of End User License Agreement for red-flagging potentially malicious software
New software and updates are downloaded by end users every day. Each dowloaded software has associated with it an End Users License Agreements (EULA), but this is rarely read. An EULA includes information to avoid legal repercussions. However,this proposes a host of potential problems such as spyware or producing an unwanted affect in the target system. End users do not read these EULA's because of length of the document and users find it extremely difficult to understand. Text summarization is…

Smishing Dataset I: Phishing SMS Dataset from Smishtank.com
Daniel Timko, Muhammad Lutfor Rahman
https://arxiv.org/abs/2402.18430 https://

Smishing Dataset I: Phishing SMS Dataset from Smishtank.com
While smishing (SMS Phishing) attacks have risen to become one of the most common types of social engineering attacks, there is a lack of relevant smishing datasets. One of the biggest challenges in the domain of smishing prevention is the availability of fresh smishing datasets. Additionally, as time persists, smishing campaigns are shut down and the crucial information related to the attack are lost. With the changing nature of smishing attacks, a consistent flow of new smishing examples is n…

An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
Sifat Muhammad Abdullah, Aravind Cheruvu, Shravya Kanchi, Taejoong Chung, Peng Gao, Murtuza Jadliwala, Bimal Viswanath
https://arxiv.org/abs/2404.16212

An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
Deepfake or synthetic images produced using deep generative models pose serious risks to online platforms. This has triggered several research efforts to accurately detect deepfake images, achieving excellent performance on publicly available deepfake datasets. In this work, we study 8 state-of-the-art detectors and argue that they are far from being ready for deployment due to two recent developments. First, the emergence of lightweight methods to customize large generative models, can enable …

BETA-UAV: Blockchain-based Efficient Authentication for Secure UAV Communication
Sana Hafeez, Mahmoud A. Shawky, Mohammad Al-Quraan, Lina Mohjazi, Muhammad Ali Imran, Yao Sun
https://arxiv.org/abs/2402.15817

BETA-UAV: Blockchain-based Efficient Authentication for Secure UAV Communication
Unmanned aerial vehicles (UAV), an emerging architecture that embodies flying ad-hoc networks, face critical privacy and security challenges, mainly when engaged in data-sensitive missions. Therefore, message authentication is a crucial security feature in drone communications. This paper presents a Blockchain-based Efficient, and Trusted Authentication scheme for UAV communication, BETA-UAV, which exploits the inherent properties of blockchain technology concerning memorability and is immutabl…

A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster Networks
Sana Hafeez, Runze Cheng, Lina Mohjazi, Muhammad Ali Imran, Yao Sun
https://arxiv.org/abs/2402.15331 …

A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster Networks
Emergency communication is critical but challenging after natural disasters when ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) offer enormous potential for agile relief coordination in these scenarios. However, effectively leveraging UAV fleets poses additional challenges around security, privacy, and efficient collaboration across response agencies. This paper presents a robust blockchain-enabled framework to address these challenges by integrating a consortium blockchai…

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Hamza Saleem, Amir Ziashahabi, Muhammad Naveed, Salman Avestimehr
https://arxiv.org/abs/2403.17296

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data owners secret-share their data between two servers that train and evaluate the model on the joint data. A significant source of inefficiency a…

Phishing Website Detection Using a Combined Model of ANN and LSTM
Muhammad Shoaib Farooq, Hina jabbar
https://arxiv.org/abs/2404.10780 https://

Phishing Website Detection Using a Combined Model of ANN and LSTM
In this digital era, our lives highly depend on the internet and worldwide technology. Wide usage of technology and platforms of communication makes our lives better and easier. But on the other side it carries out some security issues and cruel activities, phishing is one activity of these cruel activities. It is a type of cybercrime, which has the purpose of stealing the personal information of the computer user, and enterprises, which carry out fake websites that are the copy of the original…