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37th International Conference on Advanced Information Networking and Applications, aiNA 2023

作者： Abidemi, Sarumi Usman Mubarak, Auwalu Saleh Akanni, Olukayode Ameen, Zubaida Said Cacciagrano, Diletta Al-turjman, Fadi Artifiical Intelligence Engineering Department AI and Robotics Institue Near East University Mersin 10 Nicosia Turkey Computer Science Division School of Science and Technology Camerino University Camerino62032 Italy Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey

ISBN: (纸本)9783031286933

Many researchers have make efforts on creating several attendance systems to keep track of student attendance in school which is also part of academic curriculum that will have great impact in student academic performance. There have been different technologies such as RFID, face recognition, biometric, web-based attendance system etc. To capture attendance in a way that’s better than the traditional method of pen and paper or roll call system. Making this attendance record available on blockchain network and applying artificial intelligence algorithm for prediction or classification in correlation with the data captured from student attendance purpose. Several articles, papers and journals has been reviewed from different research plethora of research publishers’ database through internet such as Google scholar, IEEExplore, Springerslink etc. This will make it easy for comparing several attendance systems with respect to internet of things, blockchain and artificial intelligence to address the research gap in this aspect. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Job-SDF: A Multi-Granularity dataset for Job Skill Demand Forecasting and Benchmarking 38

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc. China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024 Neural information processing systems foundation. All rights reserved.

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Akane: Perplexity-Guided Time Series data Cleaning

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Proceedings of the ACM on Management of data 2024年第3期2卷 1-26页

作者： Xiaoyu Han Haoran Xiong Zhenying He Peng Wang Chen Wang X. Sean Wang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China National Engineering Research Center for Big Data Software EIRI Tsinghua University Beijing China

Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.

关键词： data cleaning perplexity recurrent patterns time series

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds 42

Layered Structure Aware Dependent Microservice Placement Tow...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zeng, Deze Geng, Hongmin Gu, Lin Li, Zhexiong China University of Geosciences School of Computer Science Wuhan China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350334142

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies. © 2023 IEEE.

关键词： Containers

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Privacy-preserving graph publishing with disentangled variational information bottleneck

Privacy-preserving graph publishing with disentangled variat...

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作者： Ma, Liya Li, Chen Sun, Siqi Guo, Shu Wang, Lihong Li, Jianxin School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China ARC LAB Tencent Shenzhen China China Academy of Industrial Internet Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Social networks collect enormous amounts of user personal and behavioral data, which could threaten users' privacy if published or shared directly. Privacy-preserving graph publishing (PPGP) can make user data available while protecting private information. For this purpose, in PPGP, anonymization methods like perturbation and generalization are commonly used. However, traditional anonymization methods are challenging in balancing high-level privacy and utility, ineffective at defending against both various link and hybrid inference attacks, as well as vulnerable to graph neural network (GNN)-based attacks. To solve those problems, we present a novel privacy-disentangled approach that disentangles private and non-private information for a better privacy-utility trade-off. Moreover, we propose a unified graph deep learning framework for PPGP, denoted privacy-disentangled variational information bottleneck (PDVIB). Using low-dimensional perturbations, the model generates an anonymized graph to defend against various inference attacks, including GNN-based attacks. Particularly, the model fits various privacy settings by employing adjustable perturbations at the node level. With three real-world datasets, PDVIB is demonstrated to generate robust anonymous graphs that defend against various privacy inference attacks while maintaining the utility of non-private information. © 2023 John Wiley & Sons Ltd.

关键词： Graph neural networks

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Reproducibility of predictive networks for mouse visual cortex 38

Reproducibility of predictive networks for mouse visual cort...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Turishcheva, Polina Burg, Max F. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science University Göttingen Campus Institute Data Science Germany International Max Planck Research School for Intelligent Systems Tübingen Germany Tübingen AI Center University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Deep predictive models of neuronal activity have recently enabled several new discoveries about the selectivity and invariance of neurons in the visual cortex. These models learn a shared set of nonlinear basis functions, which are linearly combined via a learned weight vector to represent a neuron's function. Such weight vectors, which can be thought as embeddings of neuronal function, have been proposed to define functional cell types via unsupervised clustering. However, as deep models are usually highly overparameterized, the learning problem is unlikely to have a unique solution, which raises the question if such embeddings can be used in a meaningful way for downstream analysis. In this paper, we investigate how stable neuronal embeddings are with respect to changes in model architecture and initialization. We find that L1 regularization to be an important ingredient for structured embeddings and develop an adaptive regularization that adjusts the strength of regularization per neuron. This regularization improves both predictive performance and how consistently neuronal embeddings cluster across model fits compared to uniform regularization. To overcome overparametrization, we propose an iterative feature pruning strategy which reduces the dimensionality of performance-optimized models by half without loss of performance and improves the consistency of neuronal embeddings with respect to clustering neurons. Our results suggest that to achieve an objective taxonomy of cell types or a compact representation of the functional landscape, we need novel architectures or learning techniques that improve identifiability. The code is available https://***/pollytur/readout_reproducibility. © 2024 Neural information processing systems foundation. All rights reserved.

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Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media Texts 28th

Internet Public Safety Event Grading and Hybrid Storage Bas...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Hu, Die Xie, Yulai Feng, Dan Zhao, Shixun Fu, Pengyu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some events that cause negative public opinion seriously threaten public security, which are called Internet Public Safety events (IPSe). Existing solutions use a single metric to realize event detection, which has a high false detection rate for Internet Public Safety events. In addition, they lack countermeasures after the outbreak of public opinion, resulting in inefficient information management. This paper proposes a novel Internet Public Safety event grading method based on multi-feature, which measures events from the three perspectives of heat, emotion, and sensitivity. In order to improve the retrieval efficiency of events information, we implement a smart dynamic hot/cold data migration mechanism using a hybrid storage system containing solid-state drives and hard-disk drives, which realizes real-time data adjustment in the storage layer and ensures efficient events management. In the experiments, we verify our method through several real internet events, and the results show that our method achieves the state-of-the-art accuracy in the detection of Internet Public Safety events and realizes efficient retrieval with a low query overhead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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EXPLOITING A TRANSFORMER ARCHITECTURE FOR SIMULTANEOUS DEVELOPMENT OF TRANSITION AND TURBULENCE MODELS FOR TURBINE FLOW PREDICTIONS 69

EXPLOITING A TRANSFORMER ARCHITECTURE FOR SIMULTANEOUS DEVEL...

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69th ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition, GT 2024

作者： Fang, Yuan Reissmann, Maximilian Pacciani, Roberto Zhao, Yaomin Ooi, Andrew S.H. Marconcini, Michele Akolekar, Harshal D. Sandberg, Richard D. Department of Mechanical Engineering University of Melbourne VIC3010 Australia Department of Industrial Engineering University of Florence Florence50139 Italy Center for Applied Physics and Technology HEDPS College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering School of AI & Data Science Indian Institute of Technology Jodhpur324030 India

ISBN: (纸本)9780791888070

Previous studies have shown the potential of using a multi-objective CFD (computational fluid dynamics) - driven machine-learning approach to train both transition and turbulence models in RANS (Reynolds averaged Navier-Stokes) calculations for improved turbine flow predictions (Akolekar et al., GT2022-81091;Fang et al., GT2023-102902). However, conducting CFD-driven training incurs a high computational cost as thousands of RANS calculations are required if the starting guesses are taken from an initial population of randomly generated models. This paper, for the first time, adopts a transformer technique, belonging to the class of natural language processing models, in gene expression programming (GEP), to expedite the training process for transition and turbulence models. The efficacy of utilizing the transformer is investigated for two scenarios. In one, we introduce previously trained models to randomly generated ones in the initial population of candidate models, facilitating the generation of models with a higher likelihood of achieving lower cost function values from the outset. In the other scenario, assuming that no suitable information is available from pre-training, a dynamic approach is employed at certain training iterations, where models exhibiting significant errors are excluded and replaced by those trained on the fly by the transformer and demonstrating smaller errors. Additionally, we incorporate mathematical operators such as minimum, maximum, and exponential functions, along with a technique called a rolling window, to avoid nested functions in the trained models. This enhances the flexibility in constructing trained models while still allowing us to delve into the underlying physics and provide recommendations for developing physical models. Finally, we also introduce two additional physical features that serve as training inputs for the turbulence model that contribute to smaller errors. With these enhancements to the previous GEP framework, mo

关键词： Exponential functions

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An efficient memory data organization strategy for application-characteristic graph processing

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Frontiers of computer science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

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