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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sowmiya, R. Revathi, P. Ragunath, D. Sindhuja, S. Gokila, P. Kalaivani, T. Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Tamilnadu Coimbatore India Info Institute of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This study presents a framework for multimodal models to control and interact with computers: The Multimodal-driven Computer Interface. A decision-making module is used by the framework to determine what actions are acceptable after it processes user input through a variety of modalities, such as speech, text, and gestures, and runs it through a multimodal fusion algorithm. Platform-specific APIs are then used to carry out these operations on the computer. It runs on Linux, Mac, and Windows platforms and is currently integrated with the multimodal LLM. Although the framework shows promising results, there are still issues with adjusting to different user demands and preferences and increasing the accuracy of mouse click position prediction. Potentially revolutionizing human-computer interaction, the Multimodal-driven Computer Interface offers new avenues for accessibility, productivity, and pleasure. © 2024 IEEE.

关键词： Windows operating system

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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU 4

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mouthami, K. Yuvaraj, N. Thilaheswaran, K.K. Lokeshvar, K.J. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kpr Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300093

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques;with Keras API and the Internet Movie database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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Development of the high-strength ductile ferritic alloys via regulating the intragranular and grain boundary precipitation of G-phase

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Journal of Materials science & Technology 2023年第5期136卷 180-199页

作者： Mujin Yang Chao Huang Jiajia Han Haichen Wu Yilu Zhao Tao Yang Shenbao Jin Chenglei Wang Zhou Li Ruiying Shu Cuiping Wang Huanming Lu Gang Sha Xingjun Liu School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China College of Materials and Fujian Provincial Key Laboratory of Materials Genome Xiamen UniversityXiamen 361005China Test Center Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Department of Materials Science and Engineering City University of Hong KongHong KongChina Department of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 150001China Institute of Materials Genome and Big Data Harbin Institute of TechnologyShenzhen 518055China

A typical G-phase strengthened ferritic model alloy(1Ti:Fe-20Cr-3Ni-1Ti-3Si,wt.%)has been carefully studied using both advanced experimental(EBSD,TEM and APT)and theoretical(DFT)*** the classic“solid solution and aging”process,the superfine(Fe,Ni)_(2)TiSi-L2_(1)particles densely precipitate within the ferritic grain and subsequently transform into the(Ni,Fe)_(16)Ti_(6)Si_(7)-G *** the meanwhile,the elemental segregation at grain boundaries and the resulting precipitation of a large amount of the(Ni,Fe)_(16)Ti_(6)Si_(7)-G phase are also *** nanoscale microstructural evolutions result in a remarkable increase in hardness(100-300 HV)and severe *** the“cold rolling and aging”process is used,the brittle fracture is effectively suppressed without loss of nano-precipitation strengthening *** yield strength of 1700 MPa with 4%elongation at break is *** key improvement in mechanical properties is mainly attributed to the pre-cold rolling process which effectively avoids the dense precipitation of the G-phase at the grain *** findings could shed light on the further exploration of the precipitation site via optimal processing strategies.

关键词： G-phase Precipitation strengthening Grain boundary segregation Nano-precipitates

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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

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Knowledge Leaks in data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

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Schmalenbach Journal of Business Research 2024年第3期76卷 357-396页

作者： Fruhwirth, Michael Pammer-Schindler, Viktoria Thalmann, Stefan Know-Center GmbH Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Graz University of Technology Silicon Austria Labs GmbH Business Analytics and Data Science-Center (BANDAS-Center) School of Business Economics and Social Sciences University of Graz

data-driven business models imply the inter-organisational exchange of data or similar value objects. data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. © The Author(s) 2024.

关键词： Business model innovation data analytics data-driven business models Knowledge risks Risk management Value objects

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SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization 38

SymILO: A Symmetry-Aware Learning Framework for Integer Line...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qian Zhang, Tianjian Yang, Linxin Han, Qingyu Wang, Akang Sun, Ruoyu Luo, Xiaodong Chang, Tsung-Hui School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized to solve ILPs. A straightforward idea is to train a model via supervised learning, with an ILP as the input and an optimal solution as the label. An ILP is symmetric if its variables can be permuted without changing the problem structure, resulting in numerous equivalent and optimal solutions. Randomly selecting an optimal solution as the label can introduce variability in the training data, which may hinder the model from learning stable patterns. In this work, we incorporate the intrinsic symmetry of ILPs and propose a novel training framework called SymILO. Specifically, we modify the learning task by introducing solution permutation along with neural network weights as learnable parameters and then design an alternating algorithm to jointly optimize the loss function. We conduct extensive experiments on ILPs involving different symmetries and the computational results demonstrate that our symmetry-aware approach significantly outperforms three existing methods-achieving 50.3%, 66.5%, and 45.4% average improvements, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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Magnetic Levitation Control Using Deep Reinforcement Learning and Prioritized Experience Replay with Action Noise Variations 4

Magnetic Levitation Control Using Deep Reinforcement Learnin...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Pare, Gopala Shil, Manas Pillai, G.N. Dey, Arnab Department of Electrical Engineering Indian Institute of Technology Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee India

ISBN: (纸本)9798331530402

In recent years, Reinforcement Learning (RL) has emerged as a promising approach for addressing control problems in complex, nonlinear environments. The ability of RL algorithms to autonomously learn optimal control policies without requiring an explicit system model makes them particularly suited for real-time applications in fields like robotics and industrial automation. This paper investigates the application of RL algorithms for controlling a magnetic levitation (maglev) system, which presents significant challenges due to its nonlinear dynamics. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and the integration of Prioritized Experience Replay (PER) are employed to enhance learning efficiency. The impact of varying action noise (random perturbations added to actions) for exploration and system performance is explored;results demonstrate that TD3 with PER achieves superior stability, faster convergence, and reduced rise and settling times without overshoot. These findings emphasize the importance of tailored exploration strategies in optimizing control policies for complex environments, highlighting the potential of advanced RL techniques for real-time control applications. © 2025 IEEE.

关键词： Reinforcement learning

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