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IEEE International Conference on Group IV Photonics

作者： Wenbo Ren Haipeng Liu Wei Jiang Xincheng Xia Zejun Yan Jijun Feng Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System (Ministry of Education) School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350394047

ISBN: (纸本)9798350394054

In this study, we present a method for free-space beam shaping and steering based on a silicon optical phased array, which addresses the theoretical limitation of traditional bulk optics. We theoretically analyze the beam propagation properties with changes in the applied phase. Different beam profiles can be shaped by varying the phase combination, while a high-order quasi-Bessel beam can be generated with a cubic change to the phase modulation. The simulated results are validated further experimentally, and they match one another well. The presented method is expected to have broad application prospects for optical communications, free-space optical interconnects, and light detection and ranging.

关键词： Phased arrays Optical interconnections Free-space optical communication Phase modulation Optical propagation Silicon photonics Optics

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Machine Learning Techniques Using the Rapid Miner Tool for Solar Production Forecasting 4

Machine Learning Techniques Using the Rapid Miner Tool for S...

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4th International Conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Ledmaoui, Younes El Fahli, Asmaa Elmaghraoui, Adila El Aroussi, Mohamed Saadane, Rachid Chehri, Abdellah Hassania School of Public Works Casablanca Laboratory Engineering System Morocco Mohammed Vi Polytechnic University Benguerir Morocco Mathematics and Computer Science KingstonONK7K 7B4 Canada

ISBN: (纸本)9798350311112

The Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBOOST), Support Vector Machine (SVM), and Random Forest (RF) machine learning (ML) algorithms are used in this research to give a comparative study of solar energy production forecasting in Morocco. The models were developed, trained, and then assessed. In this paper, two metrics were employed to evaluate the models' performance: Mean absolute error (MAE), and root mean squared error (RMSE). The models' performances show that ANN is the most reliable predictive model for energy forecasting in similar scenarios. © 2023 IEEE.

关键词： Solar energy

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On Privacy-Preserving Task Assignment for Heterogeneous Users in Mobile Crowdsensing 26

On Privacy-Preserving Task Assignment for Heterogeneous User...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Ji Li, Peng Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350331684

Task assignment is a key challenge in mobile crowd-sensing because of the varying capabilities of crowd users. Location-based task assignment schemes require users to upload their location to an untrusted platform, which raises many privacy concerns. However, stronger privacy preservation may lead to lower system utility. It is challenging to maximize system utility under privacy preservation for users. In this paper, we propose a privacy-preserving task assignment for heterogeneous users (PTAH) problem in mobile crowdsensing. Specifically, we divide users into two groups: private users with location privacy requirements and public users without location privacy requirements. We first design a privacy-preserving mechanism to obfuscate the actual location of private users. Then we construct a relationship graph based on the locations between users and tasks. We prove that the PTAH problem is an NP-hard problem, so to maximize the system utility, we propose an approximation algorithm based on the greedy algorithm. Then we propose a multi-thread cooperative simulated annealing algorithm to search for a better approximate solution. Finally, we conducted simulations based on the widely-used real-world Roma dataset. The results show that our proposed algorithm consistently outperforms other baseline algorithms. © 2023 IEEE.

关键词： Location

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Diversified Sequential Recommendation via Evolutionary Multi-Objective Transfer Optimization

Diversified Sequential Recommendation via Evolutionary Multi...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Wei Zhou Xiaolong Luo Hongyue Bao Zexuan Zhu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Sequential Recommendation (SR) intends to model user interests based on historical behavior sequences and suggest the next item. Most existing sequential models primarily focus on learning users’ preferences on the relevance objective of the target item. However, the investigation of personalized preferences on the diversity objective of SR results is usually ignored. Evolutionary algorithms have amply demonstrated effectiveness in solving multi-objective problems but encounter efficiency bottlenecks when applied to multi-objective recommendation tasks due to the limited scalability at the user level. To address the problem, this study proposes to facilitate diversified SR via a multi-objective transfer optimization algorithm, in which each optimization task corresponds to the recommendation for a target user. With the optimization knowledge of user preference transferred within and across tasks, the diversified SR of a set of users can synchronously proceed. The novelty of our proposed algorithm is fully utilizing the outstanding global search capability of evolutionary multi-objective optimization without hindering the efficiency of sequential models.

关键词： Scalability Evolutionary computation Task analysis Artificial intelligence Optimization

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ENHANCING TWITTER BOT DETECTION VIA MULTIMODAL INVARIANT REPRESENTATIONS

arXiv

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arXiv 2024年

作者： Gong, Jibing Peng, Jiquan Qu, Jin Du, ShuYing Wang, Kaiyu School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China

Detecting Twitter Bots is crucial for maintaining the integrity of online discourse, safeguarding democratic processes, and preventing the spread of malicious propaganda. However, advanced Twitter Bots today often employ sophisticated feature manipulation and account farming techniques to blend seamlessly with genuine user interactions, posing significant challenges to existing detection models. In response to these challenges, this paper proposes a novel Twitter Bot Detection framework called BotSAI. This framework enhances the consistency of multimodal user features, accurately characterizing various modalities to distinguish between real users and bots. Specifically, the architecture integrates information from users, textual content, and heterogeneous network topologies, leveraging customized encoders to obtain comprehensive user feature representations. The heterogeneous network encoder efficiently aggregates information from neighboring nodes through oversampling techniques and local relationship transformers. Subsequently, a multi-channel representation mechanism maps user representations into invariant and specific subspaces, enhancing the feature vectors. Finally, a self-attention mechanism is introduced to integrate and refine the enhanced user representations, enabling efficient information interaction. Extensive experiments demonstrate that BotSAI outperforms existing state-of-the-art methods on two major Twitter Bot Detection benchmarks, exhibiting superior performance. Additionally, systematic experiments reveal the impact of different social relationships on detection accuracy, providing novel insights for the identification of social bots. © 2024, CC BY.

关键词： Tweets

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Using Lock-Free Design for Throughput-Optimized Cache Eviction

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Proceedings of the ACM on Measurement and Analysis of Computing systems 2025年第2期9卷 1-28页

作者： Chao Dong Fang Wang Hong Jiang Dan Feng Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics) Engineering Research Center of data storage systems and Technology School of Computer Science and Technology) Ministry of Education of China Huazhong University of Science and Technology Wuhan Hubei China Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas USA

In large-scale information systems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in terms of concurrent throughput. However, existing cache eviction policies often struggle to provide adequate concurrent throughput due to their reliance on coarse-grained locking mechanisms and complex data structures. This paper presents a practical approach to cache eviction algorithm design, called Mobius, that optimizes the concurrent throughput of caches and reduces cache operation latency by utilizing lock-free data structures, while maintaining comparable hit ratios. Mobius includes two key designs. First, Mobius employs two lock-free FIFO queues to manage cache items, ensuring that all cache operations are executed efficiently in parallel. Second, Mobius integrates a consecutive detection mechanism that merges multiple modifications during eviction into a single operation, thereby reducing data races. Extensive evaluations using both synthetic and real-world workloads from high-concurrency clusters demonstrate that Mobius achieves a concurrent-throughput improvement ranging from 1.2× to 8.5× over state-of-the-art methods, while also maintaining lower latency and comparable cache hit ratios. The implementation of Mobius in CacheLib and RocksDB highlights its effectiveness in enhancing cache performance in practical scenarios.

关键词： cache eviction policy concurrency fifo queue lock-free throughput

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Tree Regularization for Visual Explanations of Cancer Cell Classification

Tree Regularization for Visual Explanations of Cancer Cell C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Li Wang Adil Nawaz Victor C.M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network’s decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications.

关键词： Visualization Accuracy Decision making Artificial neural networks Predictive models Skin Decision trees Lesions Biological cells Cancer

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Quality-Oriented Task Assignment for Heterogeneous Users in Mobile Crowdsensing 26

Quality-Oriented Task Assignment for Heterogeneous Users in ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Kang Li, Peng Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350331684

Mobile crowdsensing (MCS) is a potential technology for large-scale data collection. This technology requires the platform to recruit users to complete tasks in specific areas. A vital issue in MCS is task assignment, and most existing task assignment efforts consider only a single user type, which is not reasonable in real scenarios. Task assignment becomes more complicated when the platform tries to assign tasks to heterogeneous users with a limited budget in the platform. In this paper, we consider a quality-oriented task assignment for heterogeneous users problem. Professional users have a high sensing quality with a high cost, and normal users have a low sensing quality with a low cost First, we model the sensing capabilities and the costs of different users and formulate the quality-oriented task assignment for heterogeneous users problem. Then, we design the integer linear programming form and prove that the problem is NP-hard. By verifying the submodularity of the objective function, we present a greedy algorithm. Considering the inefficiency of the algorithm, we design two genetic algorithms to improve the total sensing quality. Finally, we evaluate the proposed algorithms under different cost cases based on a real dataset The results show that our proposed algorithm performs well under different cost distribution scenarios. © 2023 IEEE.

关键词： Genetic algorithms

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Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing 26

Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wei, Xiaobo Li, Peng Huang, Weiyi Liu, Zhiyuan Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350331684

Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework. © 2023 IEEE.

关键词： Vehicles

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using 1 times1 convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance;ii) Compared to building multi-view representations directly from scratch, extracting them automatically using 1 times1 convolutions is more economical, and may be more effective since 1 times1 convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability. © 2022 IEEE.

关键词： Convolution

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