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Far-field super-resolution chemical microscopy

Light(science & Applications) 2023年第7期12卷 1246-1267页

作者： Mingwei Tang Yubing Han Danchen Jia Qing Yang Ji-Xin Cheng State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Intelligent Perception Research Institute Zhejiang LabHangzhou 311100China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02459USA

Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.

关键词： chemical details optics

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Cyberbullying Recognition on social media for an Afro-Asiatic language Content Using Attention Mechanism and Bi-RNN Classifier

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Procedia computer science 2025年 258卷 2232-2243页

作者： Rajesh Sharma R Muluken Ayele Alemu Nune Sreenivas Akey Sungheetha Sheila Mahapatra Department of Computer Science and Engineering Alliance University Bangalore-India Department of Computer Science College of Business and Economics Arsi University Asella Ethiopia Department of Computer Science and Engineering Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronic Engineering Alliance University Bangalore-India

In the present era, cyberbullying on social media has developed into a complicated issue. In Ethiopia, cyberbullying involving sexual content material has become a prevalent issue in recent times. Because the content material on social media is unstructured, cyberbullying that is just motivated by sexual texts on the platform can be a laborious and complicated process. The difficulty of finding sexual texts on public media has raised in recent years, leading some experts to focus their attention on the detection of cyberbullying. Because deep learning algorithms do greatly on tasks involving natural language processing, several academics have suggested using these models to detect cyberbullying. Additionally, the majority of research on this challenge count number has focused on socio-political context, handicap, religion, and ethnicity. As a remedy, this observer suggested a thorough investigation of cyberbullying detection for Amharic sexual writing on social media. To create models, a binary Amharic sexual dataset is paired with a sexual dataset trained from collected Amharic content from the Facebook platform. "Bullying" and "Non-bullying" are the binary classes that make up the prepared dataset. Because the W2Vec model works well for describing non-unusual place key phrases in constrained period datasets, it is mostly based on a Skip-gram model. Furthermore, GRU and LSTM networks are used for model comparison, while the Bi-RNN and Attention Mechanism models are employed for classification. Trends in the application of N-fold cross-validation are understood. N-fold cross-validation on our dataset yields very good universal overall performance, according to the results.

关键词： Cyberbullying detection Deep Learning Natural language processing Amharic language sexual content Bidirectional RNN

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Piccolo: Large-Scale Graph Processing with Fine-Grained in-Memory Scatter-Gather 31

Piccolo: Large-Scale Graph Processing with Fine-Grained in-M...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Shin, Changmin Song, Jaeyong Jang, Hongsun Kim, Dogeun Sung, Jun Kwon, Taehee Ju, Jae Hyung Liu, Frank Choi, Yeonkyu Lee, Jinho Seoul National University Department of Electrical and Computer Engineering Korea Republic of Old Dominion Univeristy School of Data Science United States Samsung Electronics Korea Republic of

ISBN: (纸本)9798331506476

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28 × and a geometric mean speedup of 1.62 ×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks. © 2025 IEEE.

关键词： Memory architecture

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Afra: An Eclipse-Based Tool with Extensible Architecture for Modeling and Model Checking of Rebeca Family Models 1

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10th IPM International Conference on Fundamentals of Software engineering, FSEN 2023

作者： Khamespanah, Ehsan Sirjani, Marjan Khosravi, Ramtin School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Computer Science Reykjavik University Reykjavik Iceland School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (数字)9783031424410

ISBN: (纸本)9783031424403

Afra is an Eclipse-based tool for the modeling and model checking of Rebeca family models. Together with the standard enriched editor, easy to trace counter-example viewer, modular temporal property definition, exporting a model and its transition system to some other formats facilities are features of Afra. Rebeca family provides actor-based modeling languages which are designed to bridge the gap between formal methods and software engineering. Faithfulness to the system being modeled, and the usability of Rebeca family languages help in ease of modeling and analysis of the model, together with the synthesis of the system based on the model. In this paper, architectural decisions and design strategies we made in the development of Afra are presented. This makes Afra an extensible and reusable application for the modeling and analysis of Rebeca family models. Here, we show how different compilers can be developed for the family of languages which are the same in general language constructs but have some minor differences. Then we show how the model checking engine for these different languages is designed. Despite the fact that Afra has a layered object-oriented design and is developed in Java technology, we use C++ codes for developing its model checking for the performance purposes. This decision made the design of the application even harder. © 2023, IFIP International Federation for Information Processing.

关键词： Model checking

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Multilevel Chaotic Maps with Differential Encoding-Based Encryption Method for DICOM Images 5th

Multilevel Chaotic Maps with Differential Encoding-Based En...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India

ISBN: (纸本)9789819773701

This paper proposes an encryption method based on multiple chaotic maps and differential encoding method to secure the medical images. The proposed method uses a permutation-based image scrambling method in the confusion phase. In the diffusion phase, Piecewise Linear Chaotic Map (PWLCM), Piecewise Chaotic Map (PWCM), and tent map with differential encoding method have been employed at different levels of the phase to secure the medical images. The model has been tested on Digital Imaging and Communications in Medicine (DICOM) images. The proposed encryption method demonstrates robust security against statistical and entropy attacks, with encrypted images exhibiting a correlation close to zero and achieving a maximum entropy value of 7.9993. The low SSIM value between the original and encrypted images confirms their dissimilarity. Higher Mean Square Error (MSE) values and lower Peak Signal-to-Noise Ratio (PSNR) values for encrypted images prove a negligible presence of original pixel values. Additionally, the encryption approach showcases resilience against differential attacks, with a Number of Pixel Change Rate (NPCR) value of 99.6% and a Unified Averaged Changed Intensity (UACI) value of 33.4%, both within acceptable standard ranges. The effectiveness of the proposed method demonstrates that these encrypted DICOM images are safe against various forms of attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Enhancing Cloud-Based Video Streaming Efficiency using Neural Networks

Enhancing Cloud-Based Video Streaming Efficiency using Neura...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering UniversityMS38677 United States Southern University and AandM College Electrical Engineering Department Baton RougeLA70807 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798350346473

The growing demand for high-quality video streaming services has increased the need for efficient utilization of cloud resources. This research proposes a novel solution to this challenge by utilizing neural networks to enhance video streaming performance in the cloud. Specifically, the research introduces a neural network architecture capable of dynamically adjusting video quality based on network conditions and user preferences. By combining convolutional and recurrent neural networks, the proposed model learns patterns in video streams and predicts the optimal bit rate for each frame. The architecture also considers network conditions such as bandwidth, latency, and packet loss to adapt video quality in real-Time. The proposed approach aims to improve quality of service while reducing cloud resource utilization, leading to cost-effective and scalable video streaming services. Extensive simulations and experiments were conducted to evaluate the feasibility and effectiveness of the approach. The proposed neural network architecture achieved up to 30% and 40% higher PSNR and SSIM scores, respectively, than the baseline approach. These results demonstrate the effectiveness of the proposed approach in enhancing video streaming efficiency in the cloud. Overall, this research provides valuable insights into designing efficient video streaming systems and highlights the potential benefits of neural networks in cloud-based video streaming services. © 2023 IEEE.

关键词： Quality of service

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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Enhancing Physical-Layer Security in Full-Duplex Cooperative Wireless Networks over α-μ Fading Channels 7

Enhancing Physical-Layer Security in Full-Duplex Cooperative...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Saddouq, Ayam Al-Mistarihi, Mamoun F. Khoderi, Mahmoud A. Darabkh, Khalid A. Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department P.O. Box 3030 Irbid Jordan The University of Jordan School of Engineering Computer Engineering Department Amman11942 Jordan

ISBN: (纸本)9798350367027

Cooperative communication is an emerging method that allows devices with a single antenna to share their antennas and assist other nodes in transmitting signals. This leads to enhanced spatial diversity, lower power consumption, and increased reliability of signal reception. This study uses full-duplex mode and α-μ fading channels and derives the secrecy outage probability for a single relay to assess the system's secrecy performance and examine how utilizing jamming signals affects system security. Theoretical results for the derived expression are given. © 2024 IEEE.

关键词： Cooperative communication

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Leveraging ChatGPT for enhanced stock selection and portfolio optimization

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Neural Computing and Applications 2025年第8期37卷 6163-6179页

作者： Huang, Zhendai Liao, Bolin Hua, Cheng Cao, Xinwei Li, Shuai College of Computer Science and Engineering Jishou University Jishou416000 China School of Business Jiangnan University Wuxi214122 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland VTT-Technology Research Center of Finland Kaitovayla 1 Oulu90570 Finland

The public release of ChatGPT represents a significant milestone in generative AI technology, enabling the autonomous generation of content based on pre-training. This breakthrough presents new opportunities for advancements in the field of portfolio selection. This paper aims to introduce a comprehensive portfolio selection method by applying ChatGPT for stock selection and combining it with optimization algorithms to jointly optimize portfolio selection. Compared to randomly selected stocks, the portfolios optimized using ChatGPT-selected stocks and solved with the egret swarm optimization algorithm (ESOA) demonstrate higher diversification and lower volatility, leading to superior portfolio optimization results. Additionally, to validate ESOA’s superiority, its performance is compared against genetic algorithm (GA) and particle swarm optimization (PSO) on five metrics: risk, expected return, Sharpe ratio, objective value, and penalty term. Under equivalent experimental setting, ESOA exhibits a better ability to balance the relationship between risk and return. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Optimization algorithms

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In-Memory Template Matching with Approximated PCC Computation Leveraging Memristive System

In-Memory Template Matching with Approximated PCC Computatio...

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IEEE International Symposium on Quality Electronic Design

作者： Sree Nirmillo Biswash Tushar Sk Hasibul Alam Graham Buchanan Rocco Febbo Hritom Das Garrett S. Rose Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Department of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Template matching is a well-known computer vision algorithm that involves scanning a template across various parts of an image. The template is correlated within this algorithm using a similarity or matching score, such as the Pearson correlation coefficient (PCC). A chieving a m ore a ccurate match necessitates searching many regions using the PCC metric, which is hindered by the Von Neumann Bottleneck, resulting in increased energy consumption and delays. Therefore, this paper proposes an energy-efficient, c omprehensive memristive in-memory computing architecture for template matching with its physical design, where the PCC computation unit sensor readout unit, DAC, demultiplexers, in-memory memristive computing array, ADC, running sum module, fixed point operation unit and comparator. The PCC equation is approximated, considering the limitations of the hardware characteristics and application requirements. The proposed approximated memristive in-memory based template-matching scheme demonstrates competitive performance compared to the Von Neumann system and achieves around 678× improvement in the power-delay product. Lastly, a threshold-based optimization strategy is suggested to reduce energy consumption in the application.

关键词： Correlation coefficient Energy consumption Voltage In-memory computing Hardware Energy efficiency Delays Computational efficiency Physical design Sensor arrays

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