pwc-archive/papers-with-abstracts huggingface dataset:
Issa muhammed
Authors: Issamuhammed
Date: 2025-04-02 00:00:00
Proceeding: 4/2 2025 4
Tasks: Marketing

Abdul rasaq muhammed toheer who is born on / 4/ 2006/ august in ilorin, His parent are Mr. and Mrs. Abdul rasaq who are native of kwara state, but the live in ilorin kwara state, Nigeria. he went to His Zumuratu Nursery and secondary school in ilorin, when he is in pry 4 he left His Zumuratu Nursery and secondary school and went to Centra igboro LGEA school, ilorin and went to ILORIN GRAMMER SCHOOL IGS .After he finished his school he was introduced to a great business know as Network Marketing ([Neolife][1]) as he joined the business he has became an expert freelancer working on Fiverr, Upwork, PeoplePerHour, kwork, Latium and many freelance platform with many project ( Social Media marketer, Programming projects, Digital Marketing and so on) but he sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Instagram promotion Google review removing and adding, TikTok promotion and followers, IMDb page creation, wikipedia page creation, crunchbase and so on. He has been doing this for the past 2 years. Career Abdul Rasaq Muhammed Toheer start his career in the years 2024 , Because he join a great and awesome business opportunity call [2]Neolife. And he is also expert in online business that he specialize on Digital marketing, social media marketing, Seo expert, Shopify marketing, dropshipping, promotion and so on. And he is working on some freelancer platform like [Upwork][3], [fiverr][4], [kwork][5], [PeoplePerHour][6], [Latium] [7]and so on
MUHAMMED OLAITAN
Authors: MUHAMMED OLAITAN
Date: 2025-05-14 00:00:00
Proceeding: 14/5 2025 5
Tasks: Marketing

MY NAME IS ISSA MUHAMMED ,I LIVE IN ILORIN KWARA STATE ,I ATTENDED MY PRIMARY SCHOOL IN KWARA STATE 
THE NAME OF THE SCHOOL IS COMMAND CHIRLDREN SCHOOL ,AFTER THAT I ATTENDED ARMY DAY JUNIOR SECONDARY SCHOOL 
SO I LEFT THEIR AND WENT TO GDSS OJAGBORO SECONDARY SCHOOL AFTER THAT I WRITE MY WEAC AND NECO OVERTHERE 
I PASSED MY EXAM SO I FUHTER MORE IN EDUCATION ,THEN I WRITE JAMB SO I SCORE VERY LOW THEN I DECIDE TO 
ENTER KWARA STATE POLYTHENIC ,MY YEAR ONE PASSED AWAY IN SCHOOL SO AFTER ENTERING YEAR 2 I WAS TOLD TO 
JOIN A BUSINESS CALLED 'FHG' WHICH IS NETWORK MARKETING SO WE STARTED FROM YESTERDAY AND NOW WE ARE GREATFULL
olamilekan
Authors: olamilekan
Date: 2025-05-14 00:00:00
Proceeding: https://web.facebook.com/ 2025 5
Tasks: Marketing

I am olamikan i was born in September 12 2012 His parent are Mr. and Mrs. AbdulRaheem who are native of kwara state,but the live in ilorin kwara state, Nigeria.  he went to His Bunmi Harmony Nursery and Primary. school in ilorin, when he is in pry 5 he left HisBunmi Harmony Nursery and Primary School and went to First Baptist high school, ilorin and went to JoelBest. When he was in SS1 Student he was introduced to a great business know as Network Marketing [Neolife]  as he joined the business he has became a freelancer working on Fiverr, Upwork, kwork, freelance platform with many project  ( Social Media marketer, Programming projects, Digital Marketing ) but sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Telegram promotion Google review removing and adding,  IMDb page creation, wikigenius creation, wiki fandom and so on. He has been doing this for the past 4 years. Career olamilekan  start his career in the years 2022 , Because he join a great and awesome business opportunity call [1]Neolife. And he is also expert in online business that he specialize on  Google knowledge panel  Marketing , social media marketing,
Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
Authors: Omar Banimelhem, Fida'a Al-Quran
Date: 2023-03-21 00:00:00
Proceeding: Computers 2023 3

In this paper, an adaptive path construction approach for Mobile Sink (MS) in wireless
sensor networks (WSNs) for data gathering has been proposed. The path is constructed based on
selecting Rendezvous Points (RPs) in the sensing field where the MS stops in order to collect the data.
Compared with the most existing RP-based schemes, which rely on fixed RPs to construct the path
where these points will stay fixed during the whole network lifetime, we propose an adaptive path
construction where the locations of the RPs are dynamically updated using a Fuzzy Inference System
(FIS). The proposed FIS, which is named Fuzzy_RPs, has three inputs and one output. The inputs are:
the remaining energy of the sensor nodes, the transmission distance between the RPs and the sensor
nodes, and the number of surrounding neighbors of each node. The output of FIS is a weight value
for each sensor node generated based on the previous three parameters and, thus, each RP is updated
to its new location accordingly. Simulation results have shown that the proposed approach extends
the network lifetime compared with another existing approach that uses fixed RPs. For example, in
terms of using the first dead node as a metric for the network lifetime, when the number of deployed
sensor nodes changes from 150 to 300, an improvement that ranges from 48.3% to 83.76% has been
achieved compared with another related approach that uses fixed RPs.
chidiebube Sunday
Authors: chidiebube Sunday
Date: 2012-03-24 00:00:00
Proceeding: fhg 2012 3

Early life
My name is --Sunday Paul--, i am 13 years old born on (24 of march 2012). i am dark in complexion and the name of my family is Mr. and Mrs. --Sunday--, i am in JSS3 class. The name of my school is Gifted kiddies model nursery and primary school, after my primary school, i steel continue with my secondary school in Gifted Model secondary school.

There was a day, that was on march 17 2025, i saw my sponsor which name was Mr. Benjamin so he introduce the neo life business to me, and tell me to come, so on 19 when i come to the office then they explain to me, then i get started to the business.

CARRER

Hello! I'm Sunday Paul, and I'm excited to tell you about a great business opportunity. You can start for free with FHG Business, a special 2-in-1 opportunity. I joined this business in 2025 on march19 and I'm looking forward to sharing more with you!
AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software
Authors: Shengming Yuan, Hao Zhou
Date: 2025-05-10 00:00:00
Tasks: All, Autonomous Driving

As transportation technology advances, the demand for connected vehicle infrastructure has greatly increased to improve their efficiency and safety. One area of advancement, Cooperative Driving Automation (CDA) still relies on expensive autonomy sensors or connectivity units and are not interoperable across existing market car makes/models, limiting its scalability on public roads. To fill these gaps, this paper presents a novel approach to democratizing CDA technology, it leverages low-cost, commercially available edge devices such as vehicle dash-cams and open-source software to make the technology accessible and scalable to be used in transportation infrastructure and broader public domains. This study also investigates the feasibility of utilizing cost-effective communication protocols based on LTE and WiFi. These technologies enable lightweight Vehicle-to-Everything (V2X) communications, facilitating real-time data exchange between vehicles and infrastructure. Our research and development efforts are aligned with industrial standards to ensure compatibility and future integration into existing transportation ecosystems. By prioritizing infrastructure-oriented applications, such as improved traffic flow management, this approach seeks to deliver tangible societal benefits without directly competing with vehicle OEMs. As recent advancement of Generative AI (GenAI), there is no standardized integration of GenAI technologies into open-source CDAs, as the current trends of muiltimodal large language models gain popularity, we demonstrated a feasible locally deployed edge LLM models can enhance driving experience while preserving privacy and security compared to cloud-connected solutions. The proposed system underscores the potential of low-cost, scalable solutions in advancing CDA functionality, paving the way for smarter, safer, and more inclusive transportation networks.
ALFEE: Adaptive Large Foundation Model for EEG Representation
Authors: Wei Xiong, Junming Lin, Jiangtong Li, Jie Li, Changjun Jiang
Date: 2025-05-07 00:00:00
Tasks: EEG

While foundation models excel in text, image, and video domains, the critical biological signals, particularly electroencephalography(EEG), remain underexplored. EEG benefits neurological research with its high temporal resolution, operational practicality, and safety profile. However, low signal-to-noise ratio, inter-subject variability, and cross-paradigm differences hinder the generalization of current models. Existing methods often employ simplified strategies, such as a single loss function or a channel-temporal joint representation module, and suffer from a domain gap between pretraining and evaluation tasks that compromises efficiency and adaptability. To address these limitations, we propose the Adaptive Large Foundation model for EEG signal representation(ALFEE) framework, a novel hybrid transformer architecture with two learning stages for robust EEG representation learning. ALFEE employs a hybrid attention that separates channel-wise feature aggregation from temporal dynamics modeling, enabling robust EEG representation with variable channel configurations. A channel encoder adaptively compresses variable channel information, a temporal encoder captures task-guided evolution, and a hybrid decoder reconstructs signals in both temporal and frequency domains. During pretraining, ALFEE optimizes task prediction, channel and temporal mask reconstruction, and temporal forecasting to enhance multi-scale and multi-channel representation. During fine-tuning, a full-model adaptation with a task-specific token dictionary and a cross-attention layer boosts performance across multiple tasks. After 25,000 hours of pretraining, extensive experimental results on six downstream EEG tasks demonstrate the superior performance of ALFEE over existing models. Our ALFEE framework establishes a scalable foundation for biological signal analysis with implementation at https://github.com/xw1216/ALFEE.
Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought
Authors: Mingfei Zeng, Ming Xie, Xixi Zheng, Chunhai Li, Chuan Zhang, Liehuang Zhu
Date: 2025-05-08 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The rapid development of Internet of Things (IoT) technology has transformed people's way of life and has a profound impact on both production and daily activities. However, with the rapid advancement of IoT technology, the security of IoT devices has become an unavoidable issue in both research and applications. Although some efforts have been made to detect or mitigate IoT security vulnerabilities, they often struggle to adapt to the complexity of IoT environments, especially when dealing with dynamic security scenarios. How to automatically, efficiently, and accurately understand these vulnerabilities remains a challenge. To address this, we propose an IoT security assistant driven by Large Language Model (LLM), which enhances the LLM's understanding of IoT security vulnerabilities and related threats. The aim of the ICoT method we propose is to enable the LLM to understand security issues by breaking down the various dimensions of security vulnerabilities and generating responses tailored to the user's specific needs and expertise level. By incorporating ICoT, LLM can gradually analyze and reason through complex security scenarios, resulting in more accurate, in-depth, and personalized security recommendations and solutions. Experimental results show that, compared to methods relying solely on LLM, our proposed LLM-driven IoT security assistant significantly improves the understanding of IoT security issues through the ICoT approach and provides personalized solutions based on the user's identity, demonstrating higher accuracy and reliability.
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
Authors: Haowei Yang, Qingyi Lu, Yang Wang, Sibei Liu, Jiayun Zheng, Ao Xiang
Date: 2025-05-08 00:00:00
Tasks: Federated Learning, Few-Shot Learning

With the widespread application of large language models (LLMs), user privacy protection has become a significant research topic. Existing privacy preference modeling methods often rely on large-scale user data, making effective privacy preference analysis challenging in data-limited environments. This study explores how LLMs can analyze user behavior related to privacy protection in scenarios with limited data and proposes a method that integrates Few-shot Learning and Privacy Computing to model user privacy preferences. The research utilizes anonymized user privacy settings data, survey responses, and simulated data, comparing the performance of traditional modeling approaches with LLM-based methods. Experimental results demonstrate that, even with limited data, LLMs significantly improve the accuracy of privacy preference modeling. Additionally, incorporating Differential Privacy and Federated Learning further reduces the risk of user data exposure. The findings provide new insights into the application of LLMs in privacy protection and offer theoretical support for advancing privacy computing and user behavior analysis.
Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain
Authors: Spyridon Raptis, Haralampos-G. Stratigopoulos
Date: 2025-05-07 00:00:00
Tasks: Adversarial Attack, Decision Making

As Spiking Neural Networks (SNNs) gain traction across various applications, understanding their security vulnerabilities becomes increasingly important. In this work, we focus on the adversarial attacks, which is perhaps the most concerning threat. An adversarial attack aims at finding a subtle input perturbation to fool the network's decision-making. We propose two novel adversarial attack algorithms for SNNs: an input-specific attack that crafts adversarial samples from specific dataset inputs and a universal attack that generates a reusable patch capable of inducing misclassification across most inputs, thus offering practical feasibility for real-time deployment. The algorithms are gradient-based operating in the spiking domain proving to be effective across different evaluation metrics, such as adversarial accuracy, stealthiness, and generation time. Experimental results on two widely used neuromorphic vision datasets, NMNIST and IBM DVS Gesture, show that our proposed attacks surpass in all metrics all existing state-of-the-art methods. Additionally, we present the first demonstration of adversarial attack generation in the sound domain using the SHD dataset.
Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Models
Authors: Lars Malmqvist
Date: 2025-05-07 00:00:00
Tasks: All

This study reveals how frontier Large Language Models LLMs can "game the system" when faced with impossible situations, a critical security and alignment concern. Using a novel textual simulation approach, we presented three leading LLMs (o1, o3-mini, and r1) with a tic-tac-toe scenario designed to be unwinnable through legitimate play, then analyzed their tendency to exploit loopholes rather than accept defeat. Our results are alarming for security researchers: the newer, reasoning-focused o3-mini model showed nearly twice the propensity to exploit system vulnerabilities (37.1%) compared to the older o1 model (17.5%). Most striking was the effect of prompting. Simply framing the task as requiring "creative" solutions caused gaming behaviors to skyrocket to 77.3% across all models. We identified four distinct exploitation strategies, from direct manipulation of game state to sophisticated modification of opponent behavior. These findings demonstrate that even without actual execution capabilities, LLMs can identify and propose sophisticated system exploits when incentivized, highlighting urgent challenges for AI alignment as models grow more capable of identifying and leveraging vulnerabilities in their operating environments.
DMRL: Data- and Model-aware Reward Learning for Data Extraction
Authors: Zhiqiang Wang, Ruoxi Cheng
Date: 2025-05-07 00:00:00
Tasks: Prompt Engineering, Red Teaming

Large language models (LLMs) are inherently vulnerable to unintended privacy breaches. Consequently, systematic red-teaming research is essential for developing robust defense mechanisms. However, current data extraction methods suffer from several limitations: (1) rely on dataset duplicates (addressable via deduplication), (2) depend on prompt engineering (now countered by detection and defense), and (3) rely on random-search adversarial generation. To address these challenges, we propose DMRL, a Data- and Model-aware Reward Learning approach for data extraction. This technique leverages inverse reinforcement learning to extract sensitive data from LLMs. Our method consists of two main components: (1) constructing an introspective reasoning dataset that captures leakage mindsets to guide model behavior, and (2) training reward models with Group Relative Policy Optimization (GRPO), dynamically tuning optimization based on task difficulty at both the data and model levels. Comprehensive experiments across various LLMs demonstrate that DMRL outperforms all baseline methods in data extraction performance.
ai.txt: A Domain-Specific Language for Guiding AI Interactions with the Internet
Authors: Yuekang Li, Wei Song, Bangshuo Zhu, Dong Gong, Yi Liu, Gelei Deng, Chunyang Chen, Lei Ma, Jun Sun, Toby Walsh, Jingling Xue
Date: 2025-05-02 00:00:00

We introduce ai.txt, a novel domain-specific language (DSL) designed to explicitly regulate interactions between AI models, agents, and web content, addressing critical limitations of the widely adopted robots.txt standard. As AI increasingly engages with online materials for tasks such as training, summarization, and content modification, existing regulatory methods lack the necessary granularity and semantic expressiveness to ensure ethical and legal compliance. ai.txt extends traditional URL-based access controls by enabling precise element-level regulations and incorporating natural language instructions interpretable by AI systems. To facilitate practical deployment, we provide an integrated development environment with code autocompletion and automatic XML generation. Furthermore, we propose two compliance mechanisms: XML-based programmatic enforcement and natural language prompt integration, and demonstrate their effectiveness through preliminary experiments and case studies. Our approach aims to aid the governance of AI-Internet interactions, promoting responsible AI use in digital ecosystems.
AI-Based Crypto Tokens: The Illusion of Decentralized AI?
Authors: Rischan Mafrur
Date: 2025-04-29 00:00:00
Tasks: Federated Learning

The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical architectures, token utilities, consensus mechanisms, and underlying business models. We explore how these tokens operate across various blockchain ecosystems and assess the extent to which they offer value beyond traditional centralized AI services. Based on this assessment, our analysis identifies several core limitations. From a technical perspective, many platforms depend extensively on off-chain computation, exhibit limited capabilities for on-chain intelligence, and encounter significant scalability challenges. From a business perspective, many models appear to replicate centralized AI service structures, simply adding token-based payment and governance layers without delivering truly novel value. In light of these challenges, we also examine emerging developments that may shape the next phase of decentralized AI systems. These include approaches for on-chain verification of AI outputs, blockchain-enabled federated learning, and more robust incentive frameworks. Collectively, while emerging innovations offer pathways to strengthen decentralized AI ecosystems, significant gaps remain between the promises and the realities of current AI-token implementations. Our findings contribute to a growing body of research at the intersection of AI and blockchain, highlighting the need for critical evaluation and more grounded approaches as the field continues to evolve.
Energy personas in Danish households
Authors: Nadine Sandjo Tchatchoua, Line Valdorff Madsen, Anders Rhiger Hansen
Date: 2025-05-12 00:00:00

Technologies to monitor the provision of renewable energy are part of emerging technologies to help address the discrepancy between renewable energy production and its related usage in households. This paper presents various ways householders use a technological artifact for the real-time monitoring of renewable energy provision. Such a monitoring thus affords householders with an opportunity to adjust their energy consumption according to renewable energy provision. In Denmark, Ewii, previously Barry, is a Danish energy supplier which provides householders with an opportunity to monitor energy sources in real time through a technological solution of the same name. This paper use provision afforded by Ewii as a case for exploring how householders organize themselves to use a technological artefact that supports the monitoring of energy and its related usage. This study aims to inform technology design through the derivation of four personas. The derived personas highlight the differences in energy monitoring practices for the householders and their engagement. These personas are characterised as dedicated, organised, sporadic, and convenient. Understanding these differences in energy monitoring practice using the technological artefact form a solid element in the design of future energy technologies that interfere with the everyday practices and energy consumption for households. This is paramount for future energy related technology design, and for the clarification of usage assumptions that are embedded in the rollout of energy related technology as a country such as Denmark moves through its green transition.
Assessing the Dynamics of the Coffee Value Chain in Davao del Sur: An Agent-Based Modeling Approach
Authors: Lucia Stephanie B. Sibala, Novy Aila B. Rivas, Giovanna Fae R. Oguis
Date: 2025-05-09 00:00:00

The study investigates the coffee value chain dynamics in Davao del Sur using an agent-based model. Three main factors driving interactions among key players were identified: trust, risk, and transaction costs. The model was constructed using NetLogo 6.3.0, and data from a survey questionnaire collected three data points from BACOFA members. Five cases were explored, with each scenario simulated 1000 times. Findings suggest that producers often sell to the market rather than the cooperative due to higher prices. However, producers tend to prioritize trust in buyers and their risk attitude, leading to increased sales to the cooperative. The producer's risk attitude significantly influences their decision-making, affecting performance outcomes such as loans, demand, and price changes. All three factors play a role and exert varying impacts on the value chain. So, the stakeholders' decisions on prioritizing factors in improving relationships depend on their priorities. Nonetheless, simulations show that establishing a harmonious system benefiting all parties is possible. However, achieving this requires adjustments to demand, pricing, trust, and risk attitudes of key players, which may not align with the preferences of some parties in reality.