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检索条件"机构=Dept. Informatics Engineering of Computer Science"

共 36957 条记录，以下是411-420 订阅

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

引用

32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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Enhancing Agricultural Quality Control: Freshness Detection in Root Vegetables Through Deep Neural Networks 15

Enhancing Agricultural Quality Control: Freshness Detection ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shorna, Aklema Akther Ave, Abida Sultana Mahi, Hasibul Hasan Chowdhury Shital, Sabrina Bari Sowrna, Nourin Jahan Nahid, Aynul Hasan Daffodil International University Dept. computer science and engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

In monarchy agricultural technology, this research pioneers the classification of freshness in root vegetables, a field that has been abruptly neglected in earlier research on a variety of vegetables. This study addresses this research gap and employs a transfer learning-based system to swiftly determine the freshness of root vegetables through images. To conduct this research, we create our own dataset which contains six different vegetables, 'Potato', 'Radish', 'Carrot', 'Onion', 'Taro', and 'Turnip'. Vegetables were collected from the local market and clicked manually by personal mobile phone, giving the dataset authenticity and real-world application. In total we collect 6384 images, divided into 3 classes: 'Fresh', 'Aged' and 'Rotten' based on their physical attributes and texture. Different types of transfer learning model VGG19, ResNet50, InceptionResNetV2 and DenseNet201 used to classify freshness of root vegetables. Although every model achieved decent accuracy, DenseNet201 achieved a remarkable result. DenseNet201achived highest accuracy 98.75% where data loss was 0.02%. Conversely, though ResNet50 achieved lowest accuracy 60.97% among all models. The variety of algorithms used seeks to determine the best course of action for this particular agricultural setting in addition to precisely classifying freshness. This research not only defines a novel benchmark but also sets the stage for future explorations in agricultural technology, ushering in a new era of precision farming for root vegetables © 2024 IEEE.

关键词： classification deep neural network freshness Root vegetable transfer learning

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Voice Mate: Voice Controlled Chessboard for Visually Impaired

Voice Mate: Voice Controlled Chessboard for Visually Impaire...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Abraham, Anishamol Mammen, Agnel Soyi, Ajay Jhonson, Ben Biju, Joel Amal Jyothi College of Engineering Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350382693

Traditional chess can affect people with disabilities who have physical difficulty controlling the pieces, although the concept and gameplay are closely related. This paper explores the concept of voice-controlled chessboards to address this gap and encourage participation in chess. This paper presents a method for creating voice-controlled chessboards. This method introduces the structure of the circuit board, focusing on communication skills. It explains how to use stepper motors to move the chess according to commands. The paper recognises the importance of careful observation and control to ensure correct pronunciation and good chess performance. This paper discusses possible implications and areas that require further consideration. These areas include ensuring speech accuracy, maintaining reliable status monitoring of the card, and ensuring motor control. Solving these issues through development and testing is crucial to the success of voice-controlled chessboards. The paper concludes by highlighting the importance of user testing with target users (people with disabilities) to improve usability. This project has the potential to not only make chess more accessible but also stimulate the development of new technologies for a variety of recreational games. © 2024 IEEE.

关键词： Stepping motors

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Performance Analysis of Load Balancing Mechanism in Cloud Computing 1

Performance Analysis of Load Balancing Mechanism in Cloud Co...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Halappa, Annaiah Rajesh, A. Government Engineering College Dept. of Computer Science & Engineering Hassan India Jain Technological University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350323795

The advancement of Information Technology has made cloud computing technology an innovative model for offering its consumers services on a rental basis at any time or location. Numerous firms converted to cloud technology by establishing new data centers because of the flexibility of cloud services. However, it has become necessary to ensure successful job execution and effective resource usage. Load balancing (LB) in cloud computing remains a complex challenge, specifically in the Infrastructure as a Service (IaaS) cloud architecture. A server being overloaded or underloaded is a problem that mustn't happen in the process of cloud access because it would slow down processing or causes a system crash. Hence, to ignore these problems, a suitable resource schedule must be taken, so that system can load balance tasks over all accessible assists. This research suggests effective load-balancing approaches by analyzing the advantages, applications, and disadvantages of conventional LB techniques. The conclusions show that this research provides an exceptional path for researchers to overcome major drawbacks of existing LB techniques and achieves greater efficiency based on makespan, execution and response time, resource usage, efficiency, load balancing and throughput. © 2023 IEEE.

关键词： Infrastructure as a service (IaaS)

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Multilingual Approaches to Named Entity Recognition 3

Multilingual Approaches to Named Entity Recognition

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

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

ISBN: (纸本)9798331518592

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. © 2024 IEEE.

关键词： Embeddings

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Blockchain Based Credit System to Increase the Creditability through Carbon Offset Level 5

Blockchain Based Credit System to Increase the Creditability...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Padmavathi, A. Reddy, G. Charan Santhosh, Gauri Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Our country has relied significantly on agriculture as its main source of income for several decades. The demand for food and crops is growing due to the growing population, which offers the agriculture sector tremendous prospects for growth. However, despite the soaring demand and bright future, India's agriculture industry has failed to demonstrate appreciable profitability. In the paper that follows, we provide a novel strategy meant to revolutionize agricultural marketing networks. Blockchain technology is included in our approach to enable the virtualization of products and carbon offset certificates. This system encourages agricultural producers to reduce carbon emissions and address environmental concerns. By utilizing blockchain in a particular way, carbon offset credits can be used to securely track, trade, and for other fostering sustainability practices. This integration of technologies assures to change how agricultural products are marketed currently. It also helps in promoting sustainability and provides consumers with all the valuable information about the environmental impact of their purchases. © 2024 IEEE.

关键词： Blockchain carbon offset credits Product visualization virtualization

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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2024 IEEE International Conference on Big Data, BigData 2024

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

ISBN: (纸本)9798350362480

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. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Eccher, Davide Casari, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331540081

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker. © 2024 IEEE.

关键词： Underwater acoustics

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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