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2022 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2022

作者： Zaman, Meem Tasfia Fu, Xingang Challoo, Rajab North South Univeristy Dept. of Electrical and Computer Engineering Dhaka Bangladesh Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science Kingsville United States

ISBN: (纸本)9798350311563

The objectives of this paper are to obtain state-space average models of the three new topologies of the DC-DC boost converter and apply Proportional Integral Derivative (PID) controller on them to analyze their performance. One of the most important applications of DC-DC boost converter in modern world is in Photovoltaic (PV) systems. It boosts up the low DC voltage coming from the PV panels to a high DC bus voltage and it drives the DC-AC inverter. Thus, the efficiency and the control of it play very important role in this entire process. Recently, some new topologies have been proposed to improve the efficiency of this converter. In order to inspect these topologies, proper mathematical modeling is required, and the state-space average model is one of the efficient techniques to do that. Among various control techniques, PID controller is widely used to get the best-desired output voltage of this converter. To get the expected result, MATLAB Simulink models and MATLAB m-file code will be used in this paper. © 2022 IEEE.

关键词： Three term control systems

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A Construction of Three Party Post Quantum Secure Authenticated Key Exchange for Mobile Users 14

A Construction of Three Party Post Quantum Secure Authentica...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Uddeshaya Pal, Yogesh Nikhil, Perla Sai Garg, Manish Kumar, M. Ranjith Chaudhary, Dharminder The Lnm Institute of Information Technology Dept. of Mathematics Jaipur India Noida India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Chennai India

ISBN: (纸本)9798350335095

A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server through an insecure channel. Lattice based cryptography plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. However, it is not easy to design quantum resistant password based three-party protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a new post quantum three party key exchange based on a variant of lattice assumption, the ring learning errors. The protocol ensures security against impersonation attack, stolen smartcard attack, password guessing attack, and other existing attacks. In authentication phase, the protocol have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications. © 2023 IEEE.

关键词： Authentication

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Empowering Regional Language: NLP-driven Conversion from Rangpur Dialect to Standard Bengali Language

Empowering Regional Language: NLP-driven Conversion from Ran...

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International Conference on computer and Information Technology (ICCIT)

作者： Supta Dip Tabib E Alahi Md Tajwar Mohammad Asif Dewan Mayen Uddin Mojumdar Narayan Ranjan Chakraborty Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The Rangpur dialect, a geographical variant of the native spoken in the Rangpur division, including districts such as Dinajpur, Kurigram, Lalmonirhat, Nilphamari, etc. The Rangpur area has its local oral languages (like Rajhongshi) that general speakers in that region could find challenging to understand. As these languages are becoming extinct, a natural language processing approach would be a great attempt to preserve and protect these heritages. As an NLP approach, here applied well-known models such as LSTM, and Seq2Seq models. The dataset has been built using many sources to translate from standard to rangpur local tongue for this model implementation. Almost one thousand plus sentences have been gathered directly from local people. The trained dataset gave the output with accuracies of 94.69%, and 96.03% respectively. Make a plan to gather more knowledge about specific domains, such as better preprocessing techniques and necessary expertise.

关键词： Translation Tongue Accuracy Natural language processing Information technology Standards Long short term memory

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AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius

arXiv

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arXiv 2024年

作者： Wang, Xinzhe Yi, Ran Ma, Lizhuang Dept. of Computer Science and Engineering Shanghai Jiao Tong University China

3D Gaussian Splatting (3DGS) is a recent explicit 3D representation that has achieved high-quality reconstruction and real-time rendering of complex scenes. However, the rasterization pipeline still suffers from unnecessary overhead resulting from avoidable serial Gaussian culling, and uneven load due to the distinct number of Gaussian to be rendered across pixels, which hinders wider promotion and application of 3DGS. In order to accelerate Gaussian splatting, we propose AdR-Gaussian, which moves part of serial culling in Render stage into the earlier Preprocess stage to enable parallel culling, employing adaptive radius to narrow the rendering pixel range for each Gaussian, and introduces a load balancing method to minimize thread waiting time during the pixel-parallel rendering. Our contributions are threefold, achieving a rendering speed of 310% while maintaining equivalent or even better quality than the state-of-the-art. Firstly, we propose to early cull Gaussian-Tile pairs of low splatting opacity based on an adaptive radius in the Gaussian-parallel Preprocess stage, which reduces the number of affected tile through the Gaussian bounding circle, thus reducing unnecessary overhead and achieving faster rendering speed. Secondly, we further propose early culling based on axis-aligned bounding box for Gaussian splatting, which achieves a more significant reduction in ineffective expenses by accurately calculating the Gaussian size in the 2D directions. Thirdly, we propose a balancing algorithm for pixel thread load, which compresses the information of heavy-load pixels to reduce thread waiting time, and enhance information of light-load pixels to hedge against rendering quality loss. Experiments on three datasets demonstrate that our algorithm can significantly improve the Gaussian Splatting rendering speed. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Seokhan Lee Bonhwa Ku Hanseok Ko Dept. Computer Science and Engineering Korea University Seoul Republic of Korea Machine Learning Big Data Institute Korea University Seoul Republic of Korea Dept. Electrical and Computer Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines.

关键词： Learning systems Large language models Diversity reception Signal processing algorithms Reinforcement learning Signal processing Acoustics Safety Speech processing

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Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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IEEE International Conference on Big data

作者： Yuuri Tanaka Yasuhiro Hashimoto Dept. Computer Science & Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Human beings have long been regarded as "homo narrans," employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling" ability in humans, who adjust readability based on the original story’s readability. These insights advance our understanding of the "AI narrans" concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities.

关键词： Measurement Vocabulary Correlation Large language models Big data data models Complexity theory Global communication Cultural differences Context modeling

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CAS-PUF: Current-Mode Array-Type Strong PUF for Secure Computing in Area Constrained SoCs

CAS-PUF: Current-Mode Array-Type Strong PUF for Secure Compu...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Georgoulas, Dimosthenis Tsiatouhas, Yiorgos Tenentes, Vasileios University of Ioannina Vcas Lab Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9783982674100

Secure computing necessitates the integration in Systems-on-Chips (SoCs) of strong Physical Unclonable Functions (PUFs) that can generate a vast amount of Challenge Response Pairs (CRPs) for cryptographic keys generation, identification and authentication. However, the excessive area cost of strong PUF designs imposes integration difficulties to SoCs of area constrained applications, such as the IoT and mobile computing. In this paper, we present a novel strong PUF design, with silicon area requirements significantly lower than those of previous strong PUFs. The proposed Current-mode Array-type Strong PUF (CAS-PUF) is based on a current source topology of only six minimum size transistors, which is tolerant to power supply variation for enhanced reliability. Compared to previous strong PUFs, the CAS-PUF achieves the same number of CRPs with 20% to 72% less area size;while for the same area size, it provides 19 to 53 orders of magnitude higher number of CRPs. Furthermore, extensive Monte Carlo simulations on CAS-PDF show a reliability of 96.45% under ±10% power supply fluctuation;and 97.69% under temperature variation (0°C to 80°C), with an average uniqueness and uniformity of 50.01% and 49.54%, respectively. Therefore, CAS-PUF can be used as a hardware root of trust mechanism to secure computing in area constrained SoCs. © 2025 EDAA.

关键词： Integrated circuit design

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Leveraging Artificial Intelligence for Colon Cancer: Trends, Challenges and Innovations 3

Leveraging Artificial Intelligence for Colon Cancer: Trends,...

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

作者： Takkar, Sakshi Rakhra, Manik Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798331542375

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research. © 2025 IEEE.

关键词： Deep learning

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Multilingual Approaches to Named Entity Recognition

Multilingual Approaches to Named Entity Recognition

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Delhi Section Conference (DELCON), IEEE

作者： Padmaja Savaram Suhana Tabassum Sasidhar Bandu Lakshitha B Dept. of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering University College of Engineering Osmania University Hyderabad India Dept. of English Prince Sattam Bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering VNRVJIET JNTU Hyderabad India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Named Entity Recognition (NER) represents a fundamental operation within Natural Language Processing (NLP), focused on the extraction and classification of specific entities embedded in textual data. Given the rising linguistic diversity present in digital communications, there is an escalating need for NER systems to be proficient in identifying and categorizing entities across a spectrum of languages. However, developing NER models for resource-poor languages presents significant challenges due to limited labeled data and linguistic resources. This paper examines methodologies for enhancing the ability of NLP models to perform NER across diverse languages by transferring knowledge from high-resource languages to low-resource languages. We delve into advanced approaches such as cross-lingual transfer learning, multilingual embeddings, and cross-lingual model adaptation. Cross-lingual transfer learning utilizes pre-trained models from high-resource languages to initialize NER systems for low-resource languages, thereby facilitating the effective transfer of linguistic knowledge across language boundaries. Multilingual embeddings provide a shared representation space for words across languages, facilitating the transfer of linguistic knowledge. Additionally, cross-lingual model adaptation techniques aim to adapt existing NER models to new languages through fine-tuning or domain adaptation. By enhancing the generalizability of NER models through cross-lingual knowledge transfer, we enable these models to perform effectively across diverse linguistic contexts, including both resource-rich and resource-poor languages. These advancements contribute to broader accessibility and applicability of NER technology across languages and cultures, facilitating more inclusive and comprehensive language processing applications.

关键词： Adaptation models Error analysis Transfer learning Collaboration Training data Named entity recognition data models Multilingual Trajectory Context modeling

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