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Exploring Transparency and AI Assessment in LLM-Assisted Research Applications

Exploring Transparency and AI Assessment in LLM-Assisted Res...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Bacon, Greg Menon, Vineetha The University of Alabama in Huntsville Dept. of Computer Science HuntsvilleAL United States

ISBN: (纸本)9798331504847

In this work, we further investigate the utility of using a large language model as a research assistant to identify research grant funding opportunities that are best suited for a user-defined natural language set of capabilities. The use case is a United States Department of Defense Small Business Innovation Research Broad Agency Announcement. To explore principles of responsible/ethical artificial intelligence and accountability in the context of large language model-driven applications, we perform clustering on embeddings and apply a suite of metrics to compare cluster quality against Latent Semantic Indexing. Further, we use visualization techniques to depict the contents of funding opportunities that lie at the intersection of multiple capabilities. Finally, we show the importance of maintaining the human in the loop for vetted data quality. © 2025 IEEE.

关键词： Cluster analysis

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COMPACT: Content-aware Multipath Live Video Streaming for Online Classes using Video Tiles 25

COMPACT: Content-aware Multipath Live Video Streaming for On...

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16th ACM Multimedia Systems Conference, MMSys 2025

作者： Chaudhary, Shubham Mishra, Navneet Gambhir, Keshav Rajore, Tanmay Bhattacharya, Arani Maity, Mukulika Dept. of Computer Science & Engineering IIIT Delhi Delhi New Delhi India

ISBN: (纸本)9798400714672

The growing popularity of live online classes, even in remote areas, stresses the need for a good and seamless quality of experience to enhance learning. However, these bandwidth-hungry applications challenge the current cellular networks to maintain consistent bandwidth and latency. In this work, we, therefore, propose using the collaboration of multiple devices with their individual cellular networks to support such live video streaming. We design a content-aware system Compact that splits video into foreground and background using video tiles (independently encoded spatial blocks) and streams them over different paths. Compact depends on its scheduler, which exhaustively searches for the best quality based on the network estimates. We extensively evaluate our system using network traces while walking and traveling on the bus or car. Compared to the single path, Compact manages to reduce the median stall and E2E lag by 70.6% and 28.57%, and the tail stall and lag by 83.9% and ≈ 80% on a bus trace. Furthermore, we performed a live experiment to test Compact on the actual cellular network. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mobile telecommunication systems

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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 IEEE.

关键词： Convolutional neural networks

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A Medium Voltage Grid-connected PV Inverter with a New Modular High Voltage Gain Converter Featuring Internal Modified Voltage Doubling Balancers 14

A Medium Voltage Grid-connected PV Inverter with a New Modul...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Kanathipan, Kajanan Cheema, Muhammad Ali Masood Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada Northern Transformer Toronto Canada

ISBN: (纸本)9798331516116

This work proposes a medium voltage grid-connected inverter with modular high voltage gain converters for PV energy applications. The proposed topology utilizes (1) PV arrays interfaced with modular DC-DC converters in an input independent output series (IIOS) configuration, (2) internal modified voltage doubling balancers to achieve balanced output voltage under power mismatch scenarios, and (3) a high voltage neutral point clamped (NPC) 5-level grid-connected inverter system. The presented DC-DC converters are able to perform maximum power point tracking operation through phase-shift control on their full-bridge switches and ensure balanced output voltage under all scenarios with duty ratio control on the internal voltage doublers. In addition, a high step-up gain and soft-switching operation on all module switches are achieved through the use of an LLC resonant stage. A five-level three-phase inverter interfaces the system with the MVAC grid. The steady-state and dynamic performance of the proposed modular system are validated through simulation results on a 27kVrms (L-L) 60Hz output, four-module 60kW system and preliminary experimental results on a two-module proof-of-concept 1kV DC-link laboratory prototype. © 2025 IEEE.

关键词： Electric power transmission networks

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Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection System 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 IEEE.

关键词： Network intrusion

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An Intelligent Self-Aware Financial Inclusion System for Digital Society 3

An Intelligent Self-Aware Financial Inclusion System for Dig...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Rawat, Ruchira Goyal, Himanshu Rai Sharma, Sachin Kotiyal, Bina Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542375

The continuous advancement in technology has restructured industries, due to innovations in Artificial Intelligence (AI) the financial sector endorsing astonishing transformations. This research recognizes the potential of AI in fintech for digital society while focusing on the transition toward Industry 5.0, which is based on human-AI collaboration. AI technologies, such as Artificial Generative Intelligence (AGI) and Artificial Narrow Intelligence (ANI), are limited in handling intricate, vigorous financial environments. ANI has improved efficiency via credit risk assessment, customer service automation, and fraud detection, whereas AGI aims for more comprehensive decision-making capabilities. ANI and AGI are creating a gap in accomplishing a truly responsive, customer-centric system as it cannot fully adapt to subtle decision-making or erratic market shifts. This paper suggests a Super Artificial Intelligence (SAl) as a promising solution to overcome limitations, standing SAI-based design for a unified nifty digital financial system. Its forward-thinking decision-making proficiencies, self-learning, and flexibility are expected to transmute financial aligned with Industry 5.0's human-centered principles by providing adaptive services, enhancing financial inclusion and personalized. The study identifies key challenges in current AI applications, through a comprehensive review of literature and case studies, such as algorithmic errors and customer facility, and highlights how SAI could offer a more autonomous and ethical approach to financial decision-making. The paper concludes by underscoring the importance of continuous AI validation, ethical considerations, and human oversight to ensure sustainable, secure, and customer-focused financial systems. © 2025 IEEE.

关键词： Fintech

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Improved Bounds For Online Convex Optimization

Improved Bounds For Online Convex Optimization

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Nayak, Tanvi S. Bharath, B.N. Dept. of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India Dept. of EECE Indian Institute of Technology Dharwad Dharwad India

ISBN: (纸本)9798350368741

In this paper, we consider the problem of online learning with convex objectives. Most of the existing work shows that static and dynamic regrets scale logarithmically or sub-linearly with the time horizon T. On the contrary, for the strongly convex setting, it is shown that the static and dynamic regrets scale with the variation term that measures the rate at which the function changes over time. In the convex setting, we show that a simple Online Gradient Descent (OGD) algorithm with single round at each time achieves a regret that is comparable with the strongly convex setting with an additional assumption of Quadratic Growth (QG) condition. More specifically, we show that the static and dynamic regrets scale as O(Var3(T)) and O(Var1(T)) + O(Var2(T)), respectively. Here, Vari, i = 1, 2, 3 represent variation terms that capture different aspects of changes in the optimal solution over time. We use experiments to corroborate some of our theoretical findings. © 2025 IEEE.

关键词： convex optimization Online learning QG condition regret bounds sparse signal processing

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A computer science Perspective on Digital Sustainability: A Thematic Literature Review 22

A Computer Science Perspective on Digital Sustainability: A ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Olaiya, Kelvin Salomoni, Paola University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331508050

This paper explores the evolving concept of digital sustainability, emphasizing its growing importance as digital technologies become essential to addressing global sustainability challenges. Through a thematic literature review focused on computer science, we analyze papers from Scopus-indexed journals to identify key themes in the discourse on digital sustainability. Additionally, we map the identified topics to the traditional three-pillar model of sustainable development, noting areas of alignment and divergence. The study offers insights into how digital sustainability is framed within the computer science literature and its implications for future research. © 2025 IEEE.

关键词： digital sustainability inductive thematic literature review thematic literature review

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Theta-λ Optimization for Network Performance in Rician Fading and Multi-User Interference Scenarios 4

Theta-λ Optimization for Network Performance in Rician Fadi...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Sharma, Kunj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331530402

In this paper, an investigation into the application of Theta-λ optimization strategies to improve network performance is made in a challenging wireless communication systems environment subject both Rician fading and multi-user interference (MUI). This paper discusses the dynamic adjustments of Theta (angle-of-arrival) and λ parameters necessary to balance system resiliency against high throughput. We did some simulations to test the performance in terms of latency, throughput as well as accuracy using values such frequency stability and jitter. The results show strong enhancements after the optimization - latency: from a mean of 20 ms to stabilizing at 15ms, throughput: dramatically enhanced from existing baseline of around ~50 to 100 Mbps with enhancement between ~70-120Mbps while maintaining range and jitter reduction halved as average jitter is now less than two milliseconds compared previous Jitter at an optimal value. This had the added benefit of improvements in frequency stability: now, instead of hovering around 2.4 GHz like when we first tested it out-of-the-box, average was up to 2.5 GHz after optimization as well! The results of this study illustrate the potential for enhanced network reliability and availability through fading and interferer mitigation using Theta-λ optimization. This case study is representative of the relevance for real-time adaptive optimization techniques in current-day telecommunications given surging demands on resilient wireless connectivity. Further research directions include enhancement of the optimization algorithms depending on specific network conditions and integration with adaptive strategies for real-time delivery using QoE-oriented mechanisms. © 2025 IEEE.

关键词： Fading MUI Rician Theta-λ Wireless

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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