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DiffMat:Latent diffusion models for image-guided material generation

Visual Informatics 2024年第1期8卷 6-14页

作者： Liang Yuan Dingkun Yan Suguru Saito Issei Fujishiro Graduate School of Science and Technology Keio UniversityKanagawaJapan School of Computing Tokyo Institute of TechnologyTokyoJapan Department of Information and Computer Science Keio UniversityKanagawaJapan

Creating realistic materials is essential in the construction of immersive virtual *** existing techniques for material capture and conditional generation rely on flash-lit photos,they often produce artifacts when the illumination mismatches the training *** this study,we introduce DiffMat,a novel diffusion model that integrates the CLIP image encoder and a multi-layer,crossattention denoising backbone to generate latent materials from images under various *** a pre-trained StyleGAN-based material generator,our method converts these latent materials into high-resolution SVBRDF textures,a process that enables a seamless fit into the standard physically based rendering pipeline,reducing the requirements for vast computational resources and expansive *** surpasses existing generative methods in terms of material quality and variety,and shows adaptability to a broader spectrum of lighting conditions in reference images.

关键词： SVBRDF Diffusion model Generative model Appearance modeling

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Leveraging Explainable AI for Improved Understanding and Prediction of Drug Responses in IGF1R Signaling Pathways

Leveraging Explainable AI for Improved Understanding and Pre...

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Janiel, J. Rajendran, P. Selvi School Of Computing Hindustan Institute Of Technology And Science Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Pharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their response to medications. A persons genetic composition can impact the likelihood of experiencing reactions or determining the effectiveness of a medication. By providing insights into the safety and effectiveness of drug therapies pharmacogenomics holds potential for significantly enhancing health outcomes. Through advancements in targeted therapies we can precisely target abnormalities that trigger tumor growth in patients. For instance IGF1R (Insulin like Growth Factor 1 Receptor) which belongs to the tyrosine kinase receptor family plays a crucial role in promoting cell growth, survival and proliferation across different types of cancers. The overexpression of IGF1R has been observed in cancer types indicating its involvement in fueling continuous growth and survival of cancer cells. Targeting IGF1R helps address the dysregulation of this receptor within cancer cells. Artificial Intelligence (AI) comes into play by enabling prediction of suitable drugs based on a patients genomic profile thereby reducing adverse effects and improving treatment effectiveness. Parallel, here has been growing concern regarding model explanation due, to the opaque nature of model predictions. This is particularly important when it comes to modeling drug responses. In our research paper we have employed AI to gain a clear understanding of the prediction model and the factors that affect its results. The findings show that lower valued counts of YAP-pS127-Caution protein tend to negatively impact the output. Similarly lower values of YAP-pS127-Caution protein and higher valued counts of YAP-pS127 -Caution protein, Xanthine, Tyrosine tends to positively impact the output. This helps as an aiding reference in knowing which feature of an unknown cell line should be focused to know

关键词： Proteins

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Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

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Empowering data structure visualization through modification in Python 15

Empowering data structure visualization through modification...

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

作者： Puvvadi, Harsha Vardhan Angala, Hari Prasad Swaroop, Bandi Vishnu Reddy, Busa Thanuj Sathwik Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This article introduces a novel approach to data structure visualization through the development of a new programming language, utilizing Python's Lex-YACC library for lexical analysis and parsing, and the Turtle module for graphics in the backend. The proposed language enables users to visualize various data structures such as arrays, stacks, and queues, along with their operations, facilitating a deeper understanding of these fundamental concepts in computer science. By combining the power of a custom scripting language with Python's versatile libraries, this research aims to empower users to interactively explore and comprehend complex data structures through intuitive visualizations. © 2024 IEEE.

关键词： Python

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Exploring Deep Learning architectures for Crop and Weed Identification 3

Exploring Deep Learning architectures for Crop and Weed Iden...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Akaash Dravid, J. Karthik, R.K. Sreekumar, K. School of Computing Amrita Vishwavidyapeetham Department of Computer Science and IT Kochi India

ISBN: (纸本)9798350372120

In agriculture, weeds always prove to be a major threat. It is tedious to do weeding at a later stage when the crops and the weeds are significantly grown. Incorporating technology in agriculture has revolutionized both industries in many ways. In the AI approach towards agriculture, neural networks are employed for identifying and classifying crops and weeds. However, what features they learn, how they learn the difference between crops and weeds is what we need to investigate. Many vegetable crop seedlings and weeds look similar, so a significantly large amount of data is necessary for the model to classify them. In this paper we are using CNN (Convolutional Neural Networks) and Efficientnet architectures to classify the crops and weeds. We further analyze the computational efficiency and model complexity of the architectures, providing insights into their practical applicability in real-world agricultural settings. This helps in better understanding of deep learning techniques and selecting features to perform this task. We have obtained an accuracy of 91% for CNN architecture and 99% for both B3, B4 of EfficientNet Architecture. © 2024 IEEE.

关键词： Crops

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Blockchain-based Decentralized Digital Forensics Case Management System using IPFS 3

Blockchain-based Decentralized Digital Forensics Case Manage...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Viswanathan, Jayaraj Kumar, S. Udhaya Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354348

Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance. © 2024 IEEE.

关键词： Data integrity

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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

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Tsinghua science and Technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning Liming Chen Sahraoui Dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua science and Technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Energy-Aware Task Offloading for Rotatable STAR-RIS-Enhanced Mobile Edge computing Systems

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IEEE Internet of Things Journal 2025年第12期12卷 20239-20250页

作者： Yang, Dongdong Li, Bin Niyato, Dusit Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou313000 China Nanyang Technological University College of Computing and Data Science Jurong West Singapore

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rotatable STAR-RIS offers potential enhancement for MEC systems. This article investigates a rotatable STAR-RIS-assisted MEC system, operated under three protocols, namely energy splitting (ES), mode switching, and time switching. The goal is to minimize energy consumption for multiple moving user devices (UDs) through the joint optimization of STAR-RIS configurations, orientation, computation resource allocation, transmission power, and task offloading strategies. Considering the mobility of UDs, we model the original optimization problem as a sequential decision-making process across multiple time slots. The high-dimensional, highly coupled, and nonlinear nature makes it a challenging nonconvex decision-making problem for traditional optimization algorithms. Therefore, a deep reinforcement learning (DRL) approach is employed, specifically utilizing soft actor-critic algorithm to train the DRL model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in both convergence speed and energy efficiency, while reducing energy consumption by up to 52.7% compared to the fixed STAR-RIS scheme. Among three operating protocols, the ES yields the best performance. © 2014 IEEE.

关键词： Mobile edge computing

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