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Imp: Highly Capable Large Multimodal Models for Mobile Devices

IEEE Transactions on Multimedia 2025年 27卷 2961-2974页

作者： Shao, Zhenwei Yu, Zhou Yu, Jun Ouyang, Xuecheng Zheng, Lihao Gai, Zhenbiao Wang, Mingyang Kuang, Zhenzhong Ding, Jiajun Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science Hangzhou310005 China Harbin Institute of Technology Shenzhen Department of Computer Science and Technology Shenzhen China Hangzhou Dianzi University HDU-ITMO Joint Institute Hangzhou310005 China

By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive, thus hindering their applicability in resource-constrained scenarios. To this end, several lightweight LMMs have been proposed successively to maximize the capabilities under constrained scale (e.g., 3B). Despite the encouraging results achieved by these methods, most of them only focus on one or two aspects of the design space, and the key design choices that influence model capability have not yet been thoroughly investigated. In this paper, we conduct a systematic study for lightweight LMMs from the aspects of model architecture, training strategy, and training data. Based on our findings, we obtain Imp - a family of highly capable LMMs at the 2B$\sim$4B scales. Notably, our Imp-3B model steadily outperforms all the existing lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs at the 13B scale. With low-bit quantization and resolution reduction techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile chip with a high inference speed of about 13 tokens/s. © 2025 IEEE.

关键词： Visual languages

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Improved Mayfly Algorithm Based on Co-Evolution for Feature Selection

Improved Mayfly Algorithm Based on Co-Evolution for Feature ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Tianyu, Guo Hongbin, Dong Jing, Zhou School of Computer Science and Technology Harbin Engineering University National Engineering Laboratory for E-government Modeling and Simulation Harbin China

ISBN: (纸本)9798350337020

Mayfly algorithm (MA) is a new type of intelligent optimization algorithm with excellent search ability and a broad application prospect in feature selection. However, as the dimension of the data increases, MA also has the problem of weak search ability and falling into the local optimal solution. To solve these problems, this paper combines the idea of collaborative evolution and proposes an improved Mayfly algorithm (CEIMA) suitable for feature selection problems. The CEIMA algorithm first divides the population into two sub-populations and initializes them in different ways to increase the diversity of the population. During evolution, information sharing is achieved through an information transmission mechanism between sub-populations, achieving collaborative evolution between sub-populations to enhance the global search ability of CEIMA. Finally, the mRMR-m strategy is proposed to mutate the global optimal individual based on feature importance, improving the ability of CEIMA to escape from local optimal solutions. Through a comparison with 7 feature selection algorithms on 12 UCI datasets, the effectiveness of CEIMA is proven. © 2023 IEEE.

关键词： co-evolution feature selection halton sequence max-relevance and min-redundancy mayfly algorithm

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Multi-Cell Discrete-Time SIRI Epidemic Model with Travel Restrictions and Vaccination Controls

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International Journal of Applied and Computational Mathematics 2024年第6期10卷 1-20页

作者： Zouhri, Samira El Baroudi, Mohcine Laarabi, Hassan Laboratory of Analysis Modeling and Simulation (LAMS) Department of Mathematics and Computer Science Faculty of Sciences Ben M’sik Hassan II University of Casablanca BP 7955 Sidi Othman Casablanca Morocco

In this paper, we propose a multi-cell discrete-time SIRI epidemic model that describes the spatial-temporal spread of an infectious disease in a geographical domain represented as a grid of cells (regions) divided into p² cells. We assume that people can move between these cells due to their connectivity. To investigate the efficacy of travel restrictions and vaccination in preventing epidemic spread, we include two control variables in our model. We aim to reduce the number of infected individuals in each cell designated as infectious by the health authority, while also minimizing the expenses associated with implementing the travel ban and vaccination efforts. We develop an optimal control approach using the definition of a supplementary function that identifies the automatic activation of travel restrictions and vaccination in every cell according to health authority decisions. We present numerical results testing the spread of epidemics without any intervention, applying travel restrictions only, implementing vaccination only, and combining the two strategies. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Discrete SIRI model Multi-cell Optimal control Travel restrictions Vaccination

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Optimal Control in a Mathematical Model for the Occurrence of Electronic Cheating in Certification Exams;Baccalaureate Exams in Morocco as an Example

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Operations Research Forum 2024年第4期5卷 104页

作者： El Mansouri, Abdelbar Khajji, Bouchaib Labzai, Abderrahim Belam, Mohamed Laboratory LMACS Department of Mathematics and Computer Science Khouribga Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Analysis Modeling and Simulation Department of Mathematics and Computer Science Faculty of Sciences Ben M’Sik Hassan II University of Casablanca Casablanca Morocco

Researchers across a range of fields have raised concerns about the serious rise in the phenomenon of school cheating among pupils and students, which has a negative impact on the quality of education and training. This is because cheating has gone from being an unacceptable act to being a” legitimate right” for a large group of students. Here, we offer a model using mathematics to attempt to understand how this scandalous occurrence spreads among Moroccan students, particularly those in their second year of baccalaureate, and we also examine the effectiveness of some of the suggested solutions. Our method leverages Pontryagin’s maximum principle to identify the best control strategies, and we then apply iterative methods to solve the system that results. Lastly, we validate our theoretical analysis with numerical simulations conducted using MATLAB. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： 34H05 34K28 34K35 49J25 93C15 Academic performance Cheating practices Objective functional Pontryagin’s maximum principle School cheating

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Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 221-236页

作者： Abdellaoui, Benyoussef Remaida, Ahmed Sabri, Zineb Abdellaoui, Mohammed El Hafidy, Abderrahim El Bouzekri El Idrissi, Younes Moumen, Aniss National School of Applied Sciences Engineering Sciences Laboratory Ibn Tofaïl University Kenitra Morocco Faculty of Sciences Laboratory of Computer Science Ibn Tofaïl University Kenitra Morocco LPHE-modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat Morocco

High dropout rates globally perpetuate educational disparities with various underlying causes. Despite numerous strategies to address this issue, more attention should be given to understanding and addressing student emotions during classes. This lack of focus adversely affects learner engagement and retention rates. While previous studies on online learning have primarily emphasized the effectiveness of technology, infrastructure, cognition, motivation, and economic benefits, there is still a gap in understanding the emotional aspects of distance learning. First, this study addresses this gap by employing thematic modeling and utilizing non-negative matrix factorization (NMF) for emotion recognition through students’ deep learning techniques and facial emotion recognition (FER). Second, statistical analysis of these findings further augments the depth of the study. Finally, the research proposes a mathematical model based on the random walk of emotional state transitions. The findings of this study underscore the importance of considering emotions in distance learning environments and their significant impact on student's academic performance and satisfaction. By acknowledging and addressing these emotional factors, educators can enhance learner engagement, promote positive emotions, mitigate negative emotions during online learning, and ultimately improve the effectiveness of online courses. © 2024 The Authors

关键词： Emotion Recognition

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Industrial Automation PLC Implementation of MPPT Using P&O Algorithm for PV System Applications 1

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Yatimi, Hanane Ouberri, Youness Marah, Rim Aroudam, Elhassan Modeling and Simulation of Mechanical System Team Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco Computer Science and Engineering System Laboratory Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031352454

ISBN: (纸本)9783031352447

One of the most important concerns about the control of the photovoltaic system is the maximum power point tracking (MPPT) algorithms and their implementation in various electronic devices. This paper presents a programmable logic controller (PLC) software design for a standalone photovoltaic system based on the Perturb and Observe (P&O) MPPT algorithm. The entire photovoltaic system is simulated in Siemens total integration automation interface (TIA portal) and the results are plotted using Siemens Human Machine Interface WinCC flexible. The main contributions of the present paper are: 1) development of a simulated PV system block using automation PLC software for simulation test before hardware implementation, 2) development of a new automated design based on P&O-MPPT algorithm for Photovoltaic system control, 3) Validate the applicability and the effectiveness of using PLCs as maximum power point controller based on MPPT algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network

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Journal of Systems Science & Complexity 2021年第1期34卷 68-82页

作者： WANG Guangyu XU Gang WU Qing WU Xundong School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Key Laboratory of Complex Systems Modeling and Simulation Ministry of EducationHangzhou 310018China

This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://***/qwerty1319/PC-SR).

关键词： Neural network outer-product network point cloud super resolution

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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An Anomaly Detection Method Based on Adaptive Log and Dual Feature Fusion Analysis for Distributed Systems 11

An Anomaly Detection Method Based on Adaptive Log and Dual F...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Jia, Yijiang Li, Kefei Lu, Ping Xie, Baodi Wang, Huayang Chen, Jincai Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

ISBN: (纸本)9798350342406

In this study, we present a middleware-based approach for detecting anomalies in distributed systems. Our method facilitates the dynamic collection of logs at various levels of detail and incorporates an a priori dictionary-based compression strategy to process and transmit logs, minimizing the performance impact on distributed systems. Additionally, we utilize a dual feature fusion technique to analyze the logs. We evaluate the effectiveness of our approach by performing anomaly detection in a publish/subscribe distributed system and comparing it with existing methods. The results illustrate that our method outperforms other approaches, demonstrating superior performance. © 2023 IEEE.

关键词： Anomaly detection

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BCTM: A Topic modeling Method Based on External Information 13th

BCTM: A Topic Modeling Method Based on External Information

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Proceedings of the 13th EAI International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2022

作者： Liu, Gang Wan, Taiying Yu, Jinfeng Zhan, Kai Wang, Wei College of Computer Science and Technology Harbin Engineering University Harbin150001 China PwC Enterprise Digital PricewaterhouseCoopers SydneyNSW2070 Australia National Engineering Laboratory of E-Government Modeling Simulation Harbin Engineering University Harbin150001 China

ISBN: (纸本)9783031404665

Topic models are often used as intermediate algorithms for text mining and semantic analysis in natural language processing, and have a wide range of functions. However, most of the existing improvements to the topic model use word embedding to improve the accuracy of text modeling, but ignore the external information in the text. This paper proposes a topic model BCTM (Bi-Concept Topic Model) using the word feature information and concept information. Based on the BTM topic model, BCTM introduces word feature information through word vector technology and concept information based on ConceptNet to optimize topic modeling. The construction method of Bi-Concept pair is proposed. Based on ConceptNet semantic network, and the content of text is enriched with concept information. A more accurate topic distribution is obtained through the improved topic model, at the same time, due to the rich feature information, the model is also superior to the baseline model in short text modeling. The experiments prove that the bilingual topic model proposed in this paper has a good performance in modeling accuracy. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Embeddings

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