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ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synt...

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

作者： Pan, Hongyang Zhu, Keren Yang, Fan Chu, Zhufei Zeng, Xuan School of Microelectronics Fudan University State Key Lab of Integrated Circuits and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9783982674100

Mapping to k-input lookup tables (k-LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact k-LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%. © 2025 EDAA.

关键词： Table lookup

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Research on air quality prediction based on correlation analysis and XGBoost

Research on air quality prediction based on correlation anal...

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2023 International Conference on Electronics and Devices, Computational science, ICEDCS 2023

作者： Liu, Zijie Chen, Kaijie Ning, Ziyu Wang, Lele Zheng, Zhaoting Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Wuhan University of Science and Technology College of Mechanical and Automation Wuhan China Guangzhou City University of Technology School of Computer Engineering School of Data Science Guangzhou China

ISBN: (纸本)9798350343038

As one of the primary concerns of people, air quality, its pollution and management is an important topic. Air pollution can cause harm to human health, ecological environment, and social economy, and its pollution level is affected by many factors. Exploring the factors of air pollutant concentration and predicting PM2.5 concentration and AQI index more accurately is a common concern for the scientific community and policymakers. In order to make a more accurate prediction of air quality, this paper addresses the problem of air quality prediction. The quantitative features are extracted based on the Pearson correlation model and stepwise regression model, and then the qualitative features are extracted based on the chi-square test. After the feature extraction, an XGBoost model is constructed and the extracted features are used as input to predict the future air quality. The obtained prediction accuracy is much higher than that of the support vector machine and BP neural network. The predicted R2 is 0.905. © 2023 IEEE.

关键词： Air quality

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Enhancing Robustness in Multi-Agent Actor-Critic with Double Actors 38

Enhancing Robustness in Multi-Agent Actor-Critic with Double...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Han, Xue Cui, Peng Zhang, Ya Southeast University School of Automation Nanjing210096 China Science and Technology on Information Systems Engineering Laboratory Nanjing210007 China

ISBN: (纸本)9798350303636

In recent years, multi-agent reinforcement learning (MARL) has shown a strong ability to solve complex system tasks. How to solve the overfitting problem of MARL algorithm and how to improve the robustness of MARL algorithm are important issues that need to be considered when the algorithm is turned to practical application. In this paper, we discuss the application potential of two-actor networks in MARL algorithms. First, we design double actors based on MADDPG algorithm, to improve the robustness of learning strategies, which is called DA-MADDPG algorithm. In the DA-MADDPG, the central-decentralized mismatch caused by two actor networks updating with the same Q value is further discussed. A gradient updating optimization method for DA-MADDPG by KL divergence and clipped function is proposed respectively. Experimental results in MPE environment show that DA-MADDPG and DA-MADDPG bycclipped function can achieve better results than MADDPG algorithm. © 2023 IEEE.

关键词： clipped function double actors KL divergence Multi-Agent Reinforcement Learning

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A Systematic Approach to Predict Anemia from Eye Conjunctiva Images 26

A Systematic Approach to Predict Anemia from Eye Conjunctiva...

引用

26th International Conference on computer and information Technology, ICCIT 2023

作者： Noor, Nahiyan B. Oyshi, Uttamasha A. Das, Avishek Iqbal, Kamran University of Arkansas Information Science Little Rock United States University of Arkansas Computer Science Little Rock United States Chittagong University of Engineering and Technology Systems Engineering Chattogram4349 Bangladesh University of Arkansas Computer Science & Engineering Little Rock United States

ISBN: (纸本)9798350359015

Anemia is a health condition that occurs when there is insufficient hemoglobin (Hb) in the bloodstream. Although not usually lethal in mild cases, in severe cases, it can be life-threatening, especially for women, as it can lead to premature birth during pregnancy, heart problems, and multi-organ failure resulting in death. Anemia is a disease that can be easily cured if caught early, however, patients often ignore symptoms like fatigue, headache, or shortness of breath. Diagnosis of anemia requires a CBC blood test or a physical examination done by a healthcare professional, which can be both costly and time-consuming. The main aim of the current study is to propose a non-invasive and efficient method to diagnose anemia, which can be done by taking images of the eye conjunctiva. The raw image data corresponding to anemic conditions and blood hemoglobin levels (BHL) were collected from three distinct clinical sources. After preprocessing and cleaning the images, 18 features in 3 different color spaces were extracted. Using those features, anemia was predicted by various machine learning algorithms. The highest detection accuracies of 92% were observed with an ensemble of Support Vector Classifier, Random Forest, Logistic Regression, and *** Relevance-Anemia affects approximately 25% of the world's population. This study suggests a non-invasive method for predicting anemia using images of the eye conjunctiva, which eliminates the need for any blood tests. © 2023 IEEE.

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Cooperative Self-Scheduling Routing Approach Based on Energy Efficient Optimal Link Stability Routing Allocation for Improving QoS-WSN

Cooperative Self-Scheduling Routing Approach Based on Energy...

引用

2024 Advances in science and engineering Technology International Conferences, ASET 2024

作者： Radhika, G. Radhika, N. Paul, Sujni Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Computer Information Science Higher Colleges of Technology Dubai Men's Campus Academic City Dubai United Arab Emirates

ISBN: (纸本)9798350344134

The development of Wireless Sensor Networks (WSN) is broad research in recent communication approaches. Due to the increasing demand for service, the quality is insufficient to meet the requirements to take energy efficient network. This leads to high energy losses, traffic, improper scheduling, and poor network performance. Selecting a higher traffic route introduces higher packet drop and reduces the throughput performance. Analyzing the traffic in the existing process is complex and doesn't provide Quality of Service (QoS). So data analysis is essential based on reinforcement learning with an intelligent machine learning approach. To resolve this problem, we propose a Cooperative self-scheduling routing approach (CS2RA) based on energy Efficient Optimal Link stability routing allocation (EOLSRA) designed to improve the quality of service in Wireless sensor networks. The dynamic network topology medium is constructed with Neighbor Stable Link Connectivity Based Routing. With the Probability support, the traffic density approximation rate (PTDAR) is estimated by improving energy stabilization. By intending an optimistic Q-learning network model based on the energy-consuming parameters using an adaptive Petal spider ant colony cluster (PSACC) is constructed for link stability for route optimization. Then Cooperative stabilized proactive Energy-aware cluster routing protocol (CSPECR) was intended to make self-balanced congestion routing with the support of the Lookup energy constraint duty cycle (LECDC) to improve the lifetime of the network. © 2024 IEEE.

关键词： Cooperative communication

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Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation 27th

Task Consistent Prototype Learning for Incremental Few-Shot...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Deny Whatever You Want: Dual-Deniable Public-Key Encryption 28th

Deny Whatever You Want: Dual-Deniable Public-Key Encryption

引用

28th IACR International Conference on Practice and Theory of Public Key Cryptography, PKC 2025

作者： An, Zhiyuan Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9783031918285

We introduce an enhanced requirement of deniable public key encryption that we call dual-deniability. It asks that a sender who is coerced should be able to produce fake randomness, which can explain the target ciphertext as the encryption of any alternative message under any valid key she/he desires to deny. Compared with the original notion of deniability (Canetti et al. in CRYPTO ’97, hereafter named message-deniability), this term further provides a shield for the anonymity of the receiver against coercion attacks. We first give a formal definition of dual-deniability, along with its weak-mode variant. For conceptual understanding, we then show dual-deniability implies semantic security and anonymity against CPA, separates full robustness, and even contradicts key-less or mixed robustness, while is (constructively) implied by key-deniability and full robustness with a minor assumption for bits encryption. As for the availability of dual-deniability, our main scheme is a generic approach from ciphertext-simulatable PKE, where we devise a subtle multi-encryption schema to hide the true message within random masking ciphertexts under plan-ahead setting. Further, we leverage the weak model to present a more efficient scheme having negligible detection probability and constant ciphertext size. Besides, we revisit the notable scheme (Sahai and Waters in STOC ’14) and show it is inherently dual-deniable. Finally, we extend the Boneh-Katz transform to capture CCA security, deriving dual-deniable and CCA-secure PKE from any selectively dual-deniable IBE under multi-TA setting. Overall our work mounts the feasibility of anonymous messaging against coercion attacks. © International Association for Cryptologic Research 2025.

关键词： Anonymity

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Taxonomy of Proactive Detection Methods of Drunk Driving for Enhancing Traffic Safety 17

Taxonomy of Proactive Detection Methods of Drunk Driving for...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Swiley, Richard Preston Alsulieman, Razan Sherif, Ahmed Elsersy, Mohamed Khalil, Kasem University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States Higher Colleges of Technology Computer and Information Systems Dept. AlAin Abu Dhabi United Arab Emirates University of Mississippi Electrical and Computer Engineering Department MS United States

ISBN: (纸本)9798331530471

Drunk driving continues to be a substantial public health issue, leading to a multitude of accidents and deaths on a global scale. Conventional techniques for identifying drunk driving, such as breathalyzers and field sobriety tests, tend to be reactive rather than proactive. This paper surveys the existing methods for detecting drunk driving to enhance traffic safety. The technologies studied can proactively detect impaired driving behaviors by combining machine learning (ML) approaches with real-time data processing. The study outlines various classifications based on measured metrics and driving behavior. © 2024 IEEE.

关键词： Artificial Intelligence Drunk Driving Detection Traffic Safety

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Digital Twin (DT) Smart City for Air Quality Management 8

Digital Twin (DT) Smart City for Air Quality Management

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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WHAT USERS WANT: A NATURAL LANGUAGE PROCESSING APPROACH TO DISCOVER USERS' NEEDS FROM ONLINE REVIEWS 24

WHAT USERS WANT: A NATURAL LANGUAGE PROCESSING APPROACH TO D...

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24th International Conference on engineering Design, ICED 2023

作者： Spada, Irene Barandoni, Simone Giordano, Vito Chiarello, Filippo Fantoni, Gualtiero Martini, Antonella School of Engineering Department of Information Engineering University of Pisa Italy Department of Computer Science University of Pisa Italy School of Engineering Department of Energy Systems Land and Construction Engineering University of Pisa Italy School of Engineering Department of Civil and Industrial Engineering University of Pisa Italy B4DS - Business Engineering for Data Science Lab University of Pisa Italy

Digital media are a means to deliver products and services, but also a channel to interact with consumers and a source of information on users' preferences. Data shared by customers on the web, the User-Generated Content (UGC), can give entrepreneurs a detailed perspective of the market. This work examines an application of Natural Language Processing techniques on UGC to discover insights on users' opinions. We collected more than 13.000 reviews of software from digital stores and review website to gather information on the customers' perspective and their response to a given marketing strategy in two case studies on digital product's launch. The objective is to give support to two Italian companies in the process of business model development through data-driven evidence. We aim to discover who are the users and which are their needs using a lexicon-based approach to mine unstructured text. The results provide qualitative and quantitative descriptions of the market segments. We propose a method to examine UGC and to explore customers' behavior on social media. The findings helped managers for the development of their business model, enhancing an informed decision-making process. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Digital storage

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